Referencias

Aas, K., Jullum, M., & Løland, A. (2021). Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artificial Intelligence, 298, 103–502.
Abedjan, Z., Golab, L., & Naumann, F. (2015). Profiling relational data: A survey. The VLDB Journal, 24(4), 557–581.
Abraham, R., Schneider, J., & Brocke, J. vom. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424–438. https://doi.org/10.1016/j.ijinfomgt.2019.07.008
acens.com. (2014). White paper: BBDD NoSQL. https://www.acens.com/wp-content/images/2014/02/bbdd-nosql-wp-acens.pdf
Alarcon Vargas, A. J. (2022). Detección de la señalización de tránsito vertical con redes neuronales convolucionales basadas en bloques residuales. Fides Et Ratio-Revista de Difusión Cultural y Cientı́fica de La Universidad La Salle En Bolivia, 24(24), 165–194.
Allaire, J. (2022). Quarto: R interface to Quarto Markdown publishing system. https://CRAN.R-project.org/package=quarto
Almudevar, A. (2021). Theory of statistical inference. Chapman & Hall/CRC.
Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2019). A systematic literature review of data governance and cloud data governance. Personal and Ubiquitous Computing, 23(5-6), 839–859. https://doi.org/10.1007/s00779-017-1104-3
Amat Rodrigo, J. (2017). Clustering y heatmaps: Aprendizaje no supervisado. Universidad de Grananda. https://www.cienciadedatos.net/documentos/37_clustering_y_heatmaps#Model_based_clustering
Amazon Web Services. (2018). ¿Qué es NoSQL? https://aws.amazon.com/es/nosql/
Amparo, A., & Guijarro, B. (2004). Ingeniería del conocimiento: Aspectos metodológicos. Pearson Educación.
Anderberg, M. R. (1973). Cluster analysis for applications: Probability and mathematical statistics. Academic Press.
Ang, Q. W., Baddeley, A., & Nair, G. (2012). Geometrically corrected second order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics, 39(4), 591–617.
Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21. https://doi.org/10.1080/00031305.1973.10478966
Anselin, L. (1988). Spatial econometrics: Methods and models. Springer.
Anselin, L. (1996). The moran scatterplot as an ESDA tool to assess local instability in spatial association. In Spatial analytical perspectives on GIS. Routledge.
Anselin, L. (2013). Spatial econometrics: Methods and models (Vol. 4). Springer Science & Business Media.
Arnab, R. (2017). Survey sampling theory and applications. Academic Press.
Astigarraga, J., & Cruz-Alonso, V. (2022). ¡Se puede entender cómo funcionan Git y GitHub! Ecosistemas, 31(1), 2332. https://doi.org/10.7818/ecos.2332
Azevedo, A., & Santos, M. F. (2008). KDD, SEMMA and CRISP-DM: A parallel overview. IADIS European Conference Data Mining.
Baddeley, A., Davies, T. M., Rakshit, S., Nair, G., & McSwiggan, G. (2022). Diffusion smoothing for spatial point patterns. Statistical Science, 37(1), 123–142.
Baddeley, A., Nair, G., Rakshit, S., McSwiggan, G., & Davies, T. M. (2021). Analysing point patterns on networks-a review. Spatial Statistics, 42, 100435.
Baddeley, A., Rubak, E., & Turner, R. (2015). Spatial point patterns: Methodology and applications with r. CRC Press.
Baddeley, A., & Turner, R. (2005). spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software, 12, 1–42.
Balakrishnan, N., Koutras, M. V., & Politis, K. G. (2019). Introduction to probability: Models and applications. John Wiley & Sons.
Barr, C. D., & Schoenberg, F. P. (2010). On the Voronoi estimator for the intensity of an inhomogeneous planar Poisson process. Biometrika, 97(4), 977–984.
Batini, C., Scannapieco, M., et al. (2016). Data and information quality. Springer.
Baumer, B., Kaplan, D., & Horton, N. (2021). Modern data science with r. Chapman & Hall/CRC.
Beh, E. J., & Lombardo, R. (2014). Correspondence analysis: Theory, practice and new strategies. Wiley.
Berlusconi, G., Calderoni, F., Parolini, N., Verani, M., & Piccardi, C. (2016). Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis. PLOS ONE, 11(4), e0154244. https://doi.org/10.1371/journal.pone.0154244
Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65(5), 676–696.
Biecek, P. (2018). DALEX: Explainers for complex predictive models in R. The Journal of Machine Learning Research, 19(1), 3245–3249.
Bivand, R. (2020). classInt: Choose univariate class intervals. https://CRAN.R-project.org/package=classInt
Bivand, R. (2022). Spdep: Spatial dependence: Weighting schemes, statistics. https://CRAN.R-project.org/package=spdep
Blais, B. (2020). Statistical inference for everyone. Open Text Library.
Blaug, M. (1985). La metodología de la economía o cómo explican los economistas. Alianza Editorial.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Bock, H. H. (1974). Automatische klassifikation. Vandenhoeck; Rupretch. https://doi.org/3-525-40130-2
Boehmke, B., & Greenwell, B. M. (2020). Hands-on machine learning with R. CRC Press.
Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing social networks using r. SAGE. https://books.google.es/books?id=Mgq6zgEACAAJ
Borji, A. (2022). Generated faces in the wild: Quantitative comparison of stable diffusion, midjourney and DALL-E 2. arXiv Preprint arXiv:2210.00586.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–152.
Boskovitz, A., Goré, R., & Hegland, M. (2003). A logical formalisation of the Fellegi-Holt method of data cleaning. Advances in Intelligent Data Analysis v: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings 5, 554–565.
Brachman, R. J., & Anand, T. (1994). The Process of Knowledge Discovery in Databases: A First Sketch. KDD Workshop, 3, 1–12.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Breiman, L., Friedman, J., R, O., & Stone, C. (1984). Classification and regression trees. 1st edition. Taylor & Francis.
Brian, S. (1993). Cluster analysis, 3rd ed (Vol. 169). Edward Arnold.
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. Springer International Publishing. https://doi.org/10.1007/978-3-319-29854-2
Brous, P., Herder, P., & Janssen, M. (2016). Governing Asset Management Data Infrastructures. Procedia Computer Science, 95, 303–310. https://doi.org/10.1016/j.procs.2016.09.339
Brownlee, J. (2020). Data preparation for machine learning: Data cleaning, feature selection, and data transforms in python. Machine Learning Mastery.
Bryan, J. (2021). Happy Git and GitHub for the useR. GitHub. https://happygitwithr.com/
Bueno, G., Deniz, O., Espinosa, J. L., Salido, J., Serrano, I., & Vállez, N. (2015). Learning image processing with OpenCV. Packt Publishing.
Bunge, M. (2004). La investigación científica. Su estrategia y su filosofía. SIGLO XXI Editores.
Bunge, M. (2018). La ciencia: Su método y su filosofía (Vol. 1). Laetoli.
Caballero, I., Gualo, F., Rodríguez, M., & Piattini, M. (2022). BR4DQ: A methodology for grouping business rules for data quality evaluation. Information Systems, 109, 102–158.
Caballero, I., Piattini, M., & Gualo, F. (2022). Marco metodológico para la creación, implantación y mantenimiento de Sistemas de Gobierno de Datos. JISBD2022. http://hdl.handle.net/11705/JISBD/2022/6486
Caballero, I., Piattini, M., & Rodríguez, M. (2023). Modelo Alarcos de madurez de datos v4.0: Un modelo de referencia de procesos basado en estándares internacionales abiertos para la gestión de los datos, gestión de la calidad de los datos y el gobierno de los datos. DQTeam, UCLM, AQCLab. https://mamd.dqteam.es
Cancelo, J. R. (1997). Proyecto docente e investigador. Concurso de acceso al cuerpo docente de catedráticos de universidad. Universidad de Castilla-La Mancha.
Carrasco-Oberto, G. I. (2020). Cluster no Jerárquicos versus CART y BIPLOT. Universidad de Salamanca.
Carruthers, C., & Jackson, P. (2020). The chief data officer’s playbook. Facet Publishing.
Casella, G., & Berger, R. L. (2007). Statistical inference, 2nd ed. Cengage Learning.
Chacon, S., & Straub, B. (2014). Pro Git. Apress.
Chalmers, A. F., Villate, J. A. P., Máñez, P. L., & Sedeño, E. P. (2000). ¿Qué es esa cosa llamada ciencia? SIGLO XXI Editores.
Chang, W., Cheng, J., Allaire, J., Xie, Y., McPherson, J., et al. (2017). Shiny: web application framework for R. R Package Version, 1(5), 2017.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., et al. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc, 9(13), 1–73.
Chatfield, C., & Collins, A. J. (1980). Introduction to Multivariate Analysis. Chapman & Hall/CRC.
Chaudhuri, A., & Stenger, H. (2005). Survey sampling. Theory and methods, 2nd ed. Chapman & Hall/CRC.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., et al. (2015). Xgboost: EXtreme Gradient Boosting. R Package Version 0.4-2, 1(4), 1–4.
Chilès, J. P., & Delfiner, P. (1999). Geostatistics: Modeling spatial uncertainty. John Wiley; Sons, Ltd.
Codd, E. F. (1970). A Relational Model of Data for Large Shared Data Banks. Communications of the ACM, 13(6), 377–387.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Cramer, W., Guiot, J., Marini, K., Secretariat, M., & Bleu, P. (2020). Climate and environmental change in the Mediterranean Basin—current situation and risks for the future. First Mediterranean Assessment Report. Union for the Mediterranean, Plan Bleu, UNEP/MAP.
Cressie, N. A. C. (1993). Statistics for Spatial Data. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119115151
Cressie, N., & Wikle, C. K. (2015). Statistics for spatio-temporal data. John Wiley & Sons.
Cronie, O., & Van Lieshout, M. N. M. (2018). A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions. Biometrika, 105(2), 455–462.
Cryer, J. D., & Chan, K.-S. (2010). Time series analysis with applications in R. Springer.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org
Cuadras, C. M. (2007). Nuevos métodos de análisis multivariante. CMC Editions.
Cutler, A., & Zhao, G. (1999). Fast classification using perfect random trees. Utah State University.
DAMA. (2017). DAMA-DMBOK: Data management body of knowledge. Technics Publications, LLC.
DANE. (2019). Proyecciones de población departamentales y municipales por área 2005-2020. www.dane.gov.co.
Davenport, T. H., & Patil, D. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(5), 70–76.
Davenport, T., & Harris, J. (2017). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
Davies, T. M., & Baddeley, A. (2018). Fast computation of spatially adaptive kernel estimates. Statistics and Computing, 28(4), 937–956.
De Boor, C. (2001). A Practical Guide to Splines (pp. 263–290). Springer. https://doi.org/10.1007/978-1-4612-6333-3_16
De la Fuente, S. (2011). Análisis factorial. Universidad Autónoma de Madrid. https://www.fuenterrebollo.com/Economicas/ECONOMETRIA/MULTIVARIANTE/FACTORIAL/analisis-factorial.pdf
De Leeuw, J., & Mair, P. (2009). Multidimensional Scaling Using Majorization: SMACOF in R. Journal of Statistical Software, 31(3), 1–30. https://doi.org/10.18637/jss.v031.i03
Dembla, G. (2020). Intuition behind log-loss score. https://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a.
Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., & Fei-Fei, L. (2012). ImageNet large scale visual recognition competition 2012 (ILSVRC2012). See Net. Org/Challenges/LSVRC, 41.
Diday, E. (1971). Une nouvelle méthode en classification automatique et reconnaissance des formes la méthode des nuées dynamiques. Revue de Statistique Appliquée, 19(2), 19–33.
Diday, E. (1973). The dynamic clusters method in nonhierarchical clustering. International Journal of Computer & Information Sciences, 2(1), 61–88.
Díez, J. A., & Moulines, C. U. (2008). Fundamentos de filosofía de la ciencia. Editorial Ariel.
Diggle, P. (1985). A kernel method for smoothing point process data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 34(2), 138–147.
Diggle, P. (2013). Statistical analysis of spatial and spatio-temporal point patterns. CRC Press.
Diggle, P., & Giorgi, E. (2019). Model-based geostatistics for global public health: Methods and applications. Chapman & Hall/CRC.
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
Edelbrock, C. (1979). Mixture model tests of hierarchical clustering algorithms: The problem of classifying everybody. Multivariate Behavioral Research, 14(3), 367–384.
EDMCouncil. (2020). The Data Capability Assessment Model (DCAM) Framework v2.2 Overview. https://edmcouncil.org/frameworks/dcam/; EDMCouncil.
Efron, B., & Tibshirani, R. (1986). Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science, 1(1), 54–75.
Eilers, P. H., & Marx, B. D. (2010). Splines, knots, and penalties. Wiley Interdisciplinary Reviews: Computational Statistics, 2(6), 637–653.
Eilers, P. H., Marx, B. D., & Durbán, M. (2015). Twenty years of p-splines. SORT: Statistics and Operations Research Transactions, 39(2), 0149–0186.
Elhorst, J. P. (2010). Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1), 9–28. https://doi.org/10.1080/17421770903541772
Engels, B. (2019). Data Governance as the Enabler of the Data Economy. Intereconomics, 54(4), 216–222. https://doi.org/10.1007/s10272-019-0827-y
Eryurek, E., Gilad, U., Lakshmanan, V., Kibunguchy-Grant, A., & Ashdown, J. (2021). Data Governance: The Definitive Guide. O’Reilly Media, Inc. https://www.oreilly.com/library/view/data-governance-the/9781492063483/
Euler, L. (1736). Solutio problematis ad geometriam situs pertinentis. Commentarii Academiae Scientarum Imperialis Petropolitanae.
Facebook Marketing Science. (2021). Robyn.
Facebook Research AI. (2019). Nevergrad - a gradient-free optimization platform.
Faraway, J. J. (2002). Practical Regression and ANOVA using r. (Vol. 168). University of Bath.
Fay, C., Rochette, S., Guyader, V., & Girard, C. (2021). Engineering production-grade shiny apps. Chapman & Hall/CRC.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., et al. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD, 96, 82–88.
Finetti, B. de. (2017). Theory of probability: A critical introductory treatment. Wiley.
Firth, J. (1957). A Synopsis of Linguistic Theory, 1930-1955. Studies in Linguistic Analysis, 1–31.
Fleitas, F. (2017). La Inteligencia Artificial e Ingeniería del Conocimiento: Guía de la Inteligencia Artificial e Ingeniería del Conocimiento con Ejemplos de Sistemas Expertos en el Lenguaje CLIPS. Editorial Académica Española.
Forgy, E. (1965). Cluster Analysis of Multivariate Data: Efficiency vs. Interpretability of Classification. Biometrics, 21(3), 768–769.
Fradejas Rueda, J. M. (2022). Cuentapalabras. Estilometría y análisis de datos con R para filólogos. Universidad de Valladolid.
Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164.
Fukushima, K., & Miyake, S. (1982). Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Visual Pattern Recognition. In Competition and Cooperation in Neural Nets (pp. 267–285). Springer.
Gallardo-San Salvador, J. A. (2022). Introducción al análisis cluster. Universidad de Granada. https://www.ugr.es/~gallardo/pdf/cluster-g.pdf
Gallardo-San Salvador, J. A., & Vera-Vera, J. F. (2004). Técnicas aplicadas de análisis de datos multivariantes. Universidad de Granada.
García Abad, J. (2021). Comparativa de técnicas de balanceo de datos. Aplicación a un caso real para la predicción de fuga de clientes. Universidad de Oviedo.
García-Alsina, M. (2017). Big data: Gestión y explotación de grandes volúmenes de datos [Book]. Editorial UOC.
Gentile, C., & Warmuth, M. K. (1998). Linear Hinge Loss and Average Margin. Advances in Neural Information Processing Systems, 11, 225–231.
Getis, A. (1999). Spatial statistics. Geographical Information Systems, 1, 239–251.
Gilmore, R., Hutchins, S., Pastoor, D., Attali, D., Singham, L., Raja, A. M., Trimarchi, L., Khanal, K., Columbus, A., Howard, P., & Zhang, L. (2017). Awesome R Shiny. GitHub.
Giraud, T. (2022). mapsf: Thematic cartography. https://CRAN.R-project.org/package=mapsf
Gohel, D., & Skintzos, P. (2022). Flextable: Functions for tabular reporting. https://CRAN.R-project.org/package=flextable
Gómez García, J. L., Conesa, & Caralt, J. (2015). Introducción al big data [Book]. Editorial UOC.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://books.google.co.in/books?id=Np9SDQAAQBAJ
Greenacre, M. (2008). La práctica del análisis de correspondencias. Fundación BBVA.
Gualo, F., Rodríguez, M., Verdugo, J., Caballero, I., & Piattini, M. (2021). Data quality certification using ISO/IEC 25012: Industrial experiences. The Journal of Systems & Software, 176, 110938. https://doi.org/10.1016/j.jss.2021.110938
Guerry, A.-M. (1833). Essai sur la statistique morale de la France. Crochard.
Hajek, A., & Hitchcock, C. (2016). The oxford handbook of probability and philosophy. Oxford University Press.
Hamilton, J. D. (1994). Time series analysis. Princenton University Press.
Harman, H. H. (1976). Modern Factor Analyis (Third edition revised). The University of Chicago Press.
Harrison, T., F. Luna-Reyes, L., Pardo, T., De Paula, N., Najafabadi, M., & Palmer, J. (2019). The Data Firehose and AI in Government: Why Data Management is a Key to Value and Ethics. Proceedings of the 20th Annual International Conference on Digital Government Research, 171–176. https://doi.org/10.1145/3325112.3325245
Hartigan, J. A. (1975). Clustering Algorithms. John Wiley & Sons, Inc.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.
Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: The lasso and generalizations. Chapman; Hall/CRC. https://doi.org/10.1201/b18401
Haykin, S. (1999). Neural networks: A comprehensive foundation. Prentice Hall.
Hecht, R., & Jablonski, S. (2011). NoSQL evaluation: A use case oriented survey [Conference Proceedings]. Cloud and Service Computing (CSC), 2011 International Conference on Cloud and Service Computing, 336–341.
Hempel, C. (2005). La explicación científica: Estudios sobre la filosofía de la ciencia. Paidós.
Henderson, C. R. (1953). Estimation of Variance and Covariance Components. Biometrics, 9, 226–252.
Hernández-Orallo, J., Flach, P. A., & Ferri, C. (2011). Brier Curves: A New Cost-based Visualisation of Classifier Performance. The 28th International Conference on Machine Learning. ICML 2011, 585–592.
Hernangómez, D., & Fernández-Avilés, G. (2022). Visualización y geolocalización de datos con r. Netlify, Online. https://mdsr-2122-visualizacion.netlify.app/
Hester, J., & Wickham, H. (2023). Odbc: Connect to ODBC compatible databases (using the DBI interface). https://CRAN.R-project.org/package=odbc
Hijmans, R. J. (2022). raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3), 199–236.
Ho, T. K. (1995). Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, 278–282.
Hothorn, T., & Everitt, B. (2014). A Handbook of Statistical Analyses using R. CRC Press. https://www.academia.edu/37287497/A_Handbook_of_Statistical_Analyses_Using_R
Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314.
IIC. (2016). Las 7 V del big data: Características más importantes. http://www.iic.uam.es/innovacion/big-data-caracteristicas-mas-importantes-7-v/
Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. (2008). Statistical analysis and modelling of spatial point patterns (Vol. 70). John Wiley & Sons.
Ilyas, I. F., & Chu, X. (2019). Data Cleaning. ACM Books.
ISACA. (2019). COBIT Control Objectives for Information Technologies. In ISACA. https://www.isaca.org/resources/cobit
ISO. (2016). ISO 8000-61:2016. In ISO. https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/30/63086.html
ISO. (2017). ISO/IEC 38505-1:2017 Information technology — Governance of ITGovernance of data — Part 1: Application of ISO/IEC 38500 to the governance of data. In ISO/IEC 38505-1:2017 Information technology — Governance of IT — Governance of data — Part 1: Application of ISO/IEC 38500 to the governance of data. https://www.iso.org/standard/56639.html
ISO. (2018a). ISO 8000-62:2018. In ISO. https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/53/65340.html
ISO. (2018b). ISO/IEC TR 38505-2:2018 Information technology — Governance of ITGovernance of data — Part 2: Implications of ISO/IEC 38505-1 for data management. In ISO/IEC TR 38505-2:2018 Information technology — Governance of IT — Governance of data — Part 2: Implications of ISO/IEC 38505-1 for data management. https://www.iso.org/standard/70911.html
ISO/IEC. (2008). ISO/IEC 25024: Software engineering-software product quality requierements and evaluation (SQuaRE) - data quality model.
ISO/IEC. (2011). ISO/IEC 25040 -systems and software engineering — systems and software quality requirements and evaluation (SQuaRE) — evaluation process.
ISO/IEC. (2012). ISO/IEC 25012: Software engineering-software product quality requierements and evaluation (SQuaRE) - data quality model.
ISO/IEC. (2015). ISO 8000-8: Data quality — part 8: Information and data quality: Concepts and measuring.
Jackson, P., & Carruthers, C. (2019). Data Driven Business Transformation: How to Disrupt, Innovate and Stay Ahead of the Competition. John Wiley & Sons.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer. https://doi.org/10.1007/978-1-4614-7138-7
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), 101493.
Jockers, M. (2014). Text Analysis with R for Students of Literature. Springer.
Jockers, M. (2017). Introduction to the Syuzhet Package.
John, G. H., & Langley, P. (2013). Estimating Continuous Distributions in Bayesian Classifiers. arXiv Preprint arXiv:1302.4964.
Johnson, N. L., Kemp, A. W., & Kotz, S. (2008). Univariate discrete distributions, 3rd Edition. Wiley.
Jones, M. C. (1993). Simple boundary correction for kernel density estimation. Statistics and Computing, 3(3), 135–146.
Journel, A. G., & Huijbregts, C. H. J. (1978). Mining geostatistics. Academic Press.
Kalyvas, J. R., & Overly, M. R. (2014). Big Data: A Business and Legal Guide. CRC Press.
Kassambara, A. (2017). Practical guide to cluster analysis in r: Unsupervised machine learning (multivariate analysis i). sthda.com.
Kaufman, L., & Rousseeuw, P. J. (1990). Divisive Analysis (Program DIANA). In L. Kaufman & P. J. Rousseeuw (Eds.), Finding groups in data: An introduction to cluster analysis (pp. 68–125). John Wiley; Sons, Inc.
Kelejian, H. H., & Prucha, I. R. (2010). Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances. Journal of Econometrics, 157(1), 53–67. https://doi.org/10.1016/j.jeconom.2009.10.025
Khatri, V., & Brown, C. V. (2010). Designing Data Governance. Communications of the ACM, 53(1), 148–152. https://doi.org/10.1145/1629175.1629210
Kiefer, J., & Wolfowitz, J. (1952). Stochastic Estimation of the Maximum of a Regression Function. The Annals of Mathematical Statistics, 23(3), 462–466.
Kim, J.-H. (2009). Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics & Data Analysis, 53(11), 3735–3745.
Knuth, D. E. (1984). Literate Programming. The Computer Journal, 27(2), 97–111.
Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28, 1–26.
Kuhn, M. (2019). CRAN Task View: Reproducible Research. R Task View. https://cran.r-project.org/view=ReproducibleResearch
Ladley, J. (2019). Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program. Academic Press.
Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01
LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. In M. A. Arbib (Ed.), Handbook of brain theory and neural networks (pp. 33–61). MIT Press.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
Lee, C.-P., & Lin, C.-J. (2013). A Study on L2-loss (Squared Hinge-loss) Multiclass SVM. Neural Computation, 25(5), 1302–1323.
Leisch, F. (2002). Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis. Compstat, 575–580.
LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman & Hall/CRC.
Lewis, J. A. (1999). Statistical principles for clinical trials (ICH E9): An introductory note on an international guideline. Statistics in Medicine, 18(15), 1903–1942.
Lillie, T., & Eybers, S. (2019). Identifying the Constructs and Agile Capabilities of Data Governance and Data Management: A Review of the Literature. In Communications in Computer and Information Science book series (Vol. 933). Springer. https://doi.org/10.1007/978-3-030-11235-6_20
Little, R. J., & Rubin, D. B. (2019). Statistical Analysis with Missing Data. John Wiley & Sons.
Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press.
Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020). Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: The CONSORT-AI Extension. British Medical Journal, 370. https://doi.org/10.1136/bmj.m3164
Lo, F. (2017). Big data technology. DataJobs.com. https://datajobs.com/what-is-hadoop-and-nosql
Loo, M. P. van der, & Jonge, E. de. (2019). Data Validation Infrastructure for R. arXiv Preprint arXiv:1912.09759.
López, D. (2012). Análisis de las posibilidades de uso de big data en las organizaciones. Universidad de Cantabria.
López-Gónzalez, E., & Hidalgo-Sánchez, R. (2010). Escalamiento Multidimensional No Métrico. Un ejemplo con R empleando el algoritmo SMACOF. Estudios Sobre Educación, 18(1), 9–35. https://hdl.handle.net/10171/9818
Loshin, D. (2002). Rule-based Data Quality. Proceedings of the Eleventh International Conference on Information and Knowledge Management, 614–616.
Loshin, D. (2011). The Practitioner’s Guide to Data Quality Improvement. In MK series on business intelligence (pp. 327–350). Morgan Kaufmann.
Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. CRC Press, Taylor; Francis Group.
Lozano Zahonero, M. (2020). Una nueva visión de la supuesta influencia de Madame Bovary en La Regenta a través de la estilometrı́a y el análisis de sentimientos basados en lenguaje R. Orillas: Rivista D’ispanistica, (9), 573–607.
MacNaughton-Smith, P., Williams, W. T., Dale, M. B., Mockett, L. G., & Dunn, C. (1964). Dissimilarity Analysis: A New Technique of Hierarchical Sub-division. Nature, 202, 1034–1035. https://doi.org/https://doi.org/10.1038/2021034a0
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. 5th Berkeley Symp. Math. Statist. Probability, 281–297.
Mahanti, R. (2019). Data Quality: Dimensions, Measurement, Strategy, Management, and Governance. Quality Press.
Mair, P., Groenen, P. J. F., & de Leeuw, J. (2022). More on Multidimensional Scaling and Unfolding in R: Smacof Version 2. Journal of Statistical Software, 102(10), 1–47. https://doi.org/10.18637/jss.v102.i10
Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. Academic Press.
Martínez, R., Molina, J. M., & Caro, J. (2005). Desarrollo de Sistemas Basados en el Conocimiento. CLIPS y FUZZYCLIPS. Sanz y Torres.
Martı́nez, R. G., Carrasco, R. A., Garcı́a-Madariaga, J., Gallego, C. P., & Herrera-Viedma, E. (2019). A Comparison Between Fuzzy Linguistic RFM Model and Traditional RFM Model Applied to Campaign Management. Case Study of Retail Business. Procedia Computer Science, 162, 281–289.
Martı́n-Pliego, J., & Ruiz-Maya, L. (2007). Fundamentos de inferencia estadı́stica. Alfa Centauro.
Martori, J. C., Hoberg, K., & Madariaga, R. (2008). La Incorporación del Espacio en los Métodos Estadísticos: Autocorrelación Espacial y Segregación. Actas Del X Coloquio Internacional de Geocrítica.
Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 1290–1294. https://doi.org/10.1145/3025453.3025912
Matheron, G. (1962). Traité de Géostatistique Appliquée. Vol I. Éditions Technip.
Matloff, N., & Zhang, W. (2022). A Novel Regularization Approach to Fair ML. arXiv Preprint arXiv:2208.06557.
McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. The Bulletin of Mathematical Biophysics, 5, 115–133.
McNicholas, P. D. (2016). Model-based Clustering. Journal of Classification, 33(3), 331–373.
Mecca, M., Young, R., & Halcomb, J. (2014). Data Management Maturity (DMM) Model. CMMI Institute.
Mella, J. M., & Chasco, C. (2006). Urban Growth and Territorial Dynamics: A Spatial-Econometric Analysis of Spain. In Spatial Dynamics, Networks and Modelling. Edward Elgar Publishing.
Mínguez Salido, R., & García Centeno, M. C. (2011). Modelos de series temporales aplicados a rendimientos financieros. Netbiblo.
Minsky, M., & Papert, S. (1969). Perceptrons. An Introduction to Computational Geometry. Cambridge Tiass., HIT, 479, 480.
Missaoui, R., & Sarr, I. (2015). Social Network Analysis - Community Detection and Evolution. Springer.
Molinaro, A. M., Simon, R., & Pfeiffer, R. M. (2005). Prediction Error Estimation: A Comparison of Resampling Methods. Bioinformatics, 21(15), 3301–3307.
Møller, J., & Waagepetersen, R. (2003). Statistical inference and simulation for spatial point processes. CRC Press.
Molnar, C. (2020). Interpretable Machine Learning. Lulu.com.
Monsalve, C. (2019). Medellín necesita 2.000 uniformados más para reforzar seguridad. www.bluradio.com/blu360/antioquia/medellin-necesita-2-000-uniformados-mas-para-reforzar-seguridad-policia.
Montero, J. M. (1997). Proyecto docente e investigador. Concurso de acceso al cuerpo docente de catedráticos de universidad. Universidad de Castilla-La Mancha.
Montero, J. M. (2002). Una propuesta de corrección de continuidad asimétrica para tablas de contingencia (2x2) con totales marginales fijos. Estadística Española, 44(149), 29–46.
Montero, J. M. (2007). Estadística Descriptiva. Thomson-Paraninfo.
Montero, J. M., Fernández-Avilés, G., & Mateu, J. (2015). Spatial and Spatio-temporal Geostatistical Modeling and Kriging. John Wiley & Sons.
Montero, J. M., & Larraz, B. (2008). Introducción a la geoestadística lineal. Netbiblo.
Moradi, M. (2018). Spatial and Spatio-Temporal Point Patterns on Linear Networks [{PhD Dissertation}]. University Jaume I.
Moradi, M., Cronie, O., Rubak, E., Lachieze-Rey, R., Mateu, J., & Baddeley, A. (2019). Resample-smoothing of Voronoi Intensity Estimators. Statistics and Computing, 29(5), 995–1010.
Morgan, A. (2015). Joins and other aggregation enhancements in MongoDB 3.2. http://www.clusterdb.com/mongodb/joins-and-other-aggregation-enhancements-in-mongodb-3-2.
Morin, D. J. (2016). Probability: For the enthusiastic beginner. CreateSpace Independent Publishing Platform.
Morrison, D. F. (1976). Multivariate statistical methods. McGraw-Hill.
Müller, K., Ooms, J., James, D., DebRoy, S., Wickham, H., & Horner, J. (2022). RMariaDB: Database interface and MariaDB driver. https://CRAN.R-project.org/package=RMariaDB
Müller, K., Wickham, H., James, D. A., & Falcon, S. (2022). RSQLite: SQLite interface for R. https://CRAN.R-project.org/package=RSQLite
Muñoz-Reja, I. C., Carretero, A. I. G., & Cejudo, F. G. (2018). Calidad de datos. RA-MA Editorial.
Nair, V., & Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. ICML 2010, 807–814.
Newell, A., Simon, H. A., & Seoane, J. (1974). Simulación del pensamiento humano. Teorema: Revista Internacional de Filosofía, 4(3), 335–378.
Ng, R. T., & Han, J. (2002). CLARANS: A Method for Clustering Objects for Spatial Data Mining. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1003–1016.
Novikoff, A. B. (1962). On Convergence Proofs on Perceptrons. Proceedings of the Symposium on the Mathematical Theory of Automata, 12, 615–622.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
OECD. (2019). The Path to Becoming a Data-Driven Public Sector. OECD Publishing. doi:https://doi.org/10.1787/059814a7-en
Okabe, A., & Sugihara, K. (2012). Spatial analysis along networks: Statistical and computational methods. John Wiley & Sons.
Ooms, J., James, D., DebRoy, S., Wickham, H., & Horner, J. (2022). RMySQL: Database interface and MySQL driver for R. https://CRAN.R-project.org/package=RMySQL
OpenAI. (2022). ChatGPT. https://chat.openai.com/
Osimo, D., Mureddu, F., Peristeras, V., Cioffi, A., Moise, C., & van Ooijen, C. (2020). Data Strategies, Policies and Agenda. EGOV-CeDEM-ePart-2020, 11–28.
Osorio-Sanabria, M. A., Amaya-Fernández, F., & González-Zabala, M. P. (2020). Developing a model to readiness assessment of open government data in public institutions in Colombia. Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, 334–340.
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
Pearson, K. (1904). On the theory of contingency and its relation to association and normal correlation. In Univ. of L. Department of Applied Mathematics Univ. College (Ed.), Mathematical contributions to the theory of evolution (pp. 1–34). Dulau; Co.
Pebesma, E. (2018). Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10(1), 439–446. https://doi.org/10.32614/RJ-2018-009
Pebesma, E., & Bivand, R. (2023). Spatial data science: With applications in R. Chapman & Hall/CRC.
Pemberton, J. (2011). Time Series Analysis with Applications in R. Journal of Applied Statistics, 38(6), 1311–1332. https://doi.org/10.1080/02664760903075663
Peña, D. (2002). Análisis de Datos Multivariantes. McGraw-Hill.
Pérez Infante, J. I. (2006). Las estadísticas del mercado de trabajo en españa. Ministerio de Empleo y Seguridad Social. Subdirección General de Información Administrativa y Publicaciones.
Pérez-Gil, J. A., Chacón, S., & Moreno, R. (2000). Validez de constructo: El uso de análisis factorial exploratorio-confirmatorio para obtener evidencias de validez. Psicothema, 12(Su2), 442–446.
Pérez-Solà, C., & Casas-Roma, J. (2021). Análisis de Datos de Redes Sociales. Editorial UOC.
Piattini, M., Marcos, E., Calero, C., & Vela, B. (2006). Tecnologı́a y Diseño de Bases de Datos. Editorial Ra-Ma.
Pizarro, M., Hernangómez, D., & Fernández-Avilés, G. (2021). climaemet: Climate AEMET tools. http://hdl.handle.net/10261/250390. https://doi.org/10.5281/zenodo.5205573
Plotkin, D. (2020). Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance. Academic Press.
Price, R., & Shanks, G. (2004). A Semiotic Information Quality Framework. Proceedings of the International Conference on Decision Support Systems DSS04, 658–672.
R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
R Special Interest Group on Databases (R-SIG-DB), Wickham, H., & Müller, K. (2022). DBI: R database interface. https://CRAN.R-project.org/package=DBI
RAE. (2023). Diccionario de la Lengua Española. 23.ª ed. Definición de "inteligencia artificial". https://dle.rae.es/inteligencia?m=form#2DxmhCT
Rakshit, S., Baddeley, A., & Nair, G. (2019). Efficient code for second order analysis of events on a linear network. Journal of Statistical Software, 90, 1–37.
Rakshit, S., Davies, T., Moradi, M., McSwiggan, G., Nair, G., Mateu, J., & Baddeley, A. (2019). Fast Kernel Smoothing of Point Patterns on a Large Network Using Two-dimensional Convolution. International Statistical Review, 87(3), 531–556.
Rakshit, S., Nair, G., & Baddeley, A. (2017). Second-order analysis of point patterns on a network using any distance metric. Spatial Statistics, 22, 129–154.
Redman, T. C. (2016). Getting in Front on Data: Who Does What. Technics Publications.
Restrepo, V. (2019). ¿Qué tan segura se siente la gente en Medellín? https://www.elcolombiano.com/antioquia/seguridad/percepcion-de-seguridad-en-medellin-encuesta-de-victimizacion-PC10033581; El Colombiano.
Revelle, W. (2022). Psych: Procedures for psychological, psychometric, and personality research. Northwestern University. https://CRAN.R-project.org/package=psych
Reynolds, H. T. (1984). Analysis of nominal data (2nd edition). Sage Publication.
Reynolds, R. W., Banzon, V. F., & Program, N. C. (2008). NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, Version 2. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5SQ8XB5
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Model-agnostic Interpretability of Machine Learning. arXiv Preprint arXiv:1606.05386.
Rivera, S. C., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., et al. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. The Lancet Digital Health, 2(10), e549–e560.
Romanski, P., Kotthoff, L., & Kotthoff, M. L. (2013). Package FSelector. URL http://cran/r-project. org/web/packages/FSelector/index. html; Citeseer.
Rosenbaum, P. R. (2005). Observational Studies. In Encyclopedia of Statistics in Behavioral Science. John Wiley & Sons, Ltd.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–404.
Ross, S. (2012). A first course in probability 9th ed. Pearson.
Rubenfa. (2014). MongoDB: Empezando por el principio. Insertando datos. Genbeta Dev. https://www.genbetadev.com/bases-de-datos/mongodb-empezando-por-el-principio-insertando-datos
Ruiz-Maya, L., Martin-Pliego, J., Montero, J. M., & Uríz, P. (1995). Análisis estadístico de encuestas: Datos cualitativos. Alfa Centauro.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
Ryu, C. (2022). dlookr: Tools for data diagnosis, exploration, transformation. https://CRAN.R-project.org/package=dlookr
Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517.
Sakia, R. M. (1992). The Box-Cox Transformation Technique: A Review. Journal of the Royal Statistical Society: Series D (The Statistician), 41(2), 169–178.
Sanabria, A. M. F., Castañeda, M. P. B., Ramos, R. R. R., & Mateu, J. (2022). Identification of patterns for space-time event networks. Applied Network Science, 7(1), 1–24.
Sánchez, J. (2017). Manual de Administración de Bases de Datos. Fundamentos de los Sistemas Gestores de Bases de Datos. https://jorgesanchez.net/manuales/abd/bases-sgbd.html.
Sarkar, D. (2008). Lattice: Multivariate data visualization with r. Springer. http://lmdvr.r-forge.r-project.org
SAS Institute. (2017). Big data. What it is and why it matters. https://www.sas.com/en_us/insights/big-data/what-is-big-data.html
Schabenberger, O., & Gotway, C. A. (2005). Statistical Methods for Spatial Data Analysis. Chapman & Hall/CRC.
Schapire, R. E., & Freund, Y. (2012). Boosting: Foundations and algorithms. The MIT Press.
Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A., et al. (2021). GGally: Extension to ggplot2.
Schölkopf, B., Simard, P., Smola, A., & Vladimir, V. (1997). Prior Knowledge in Support Vector Kernels. Advances in Neural Information Processing Systems, 10.
Schölkopf, B., Sung, K.-K., Burges, C. J., Girosi, F., Niyogi, P., Poggio, T., & Vladimir, V. (1997). Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers. IEEE Transactions on Signal Processing, 45(11), 2758–2765.
Schubert, E., & Rousseeuw, P. J. (2021). Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA and CLARANS algorithms. Information Systems, 101, 101804. https://doi.org/https://doi.org/10.1016/j.is.2021.101804
Scott, D. W. (1992). Multivariate density estimation: Theory, practice, and visualization. John Wiley & Sons.
Shafique, U., & Qaiser, H. (2014). A comparative study of data mining process models (KDD, CRISP-DM and SESMA). International Journal of Innovation and Scientific Research, 12(1), 217–222.
Shorten, C., & Khoshgoftaar, T. M. (2019). A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 1–48.
Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: With R examples. Springer International Publishing. https://doi.org/10.1007/978-3-319-52452-8
Silge, J., & Robinson, D. (2017). Text Mining with R: A Tidy Approach. O’Reilly Media, Inc.
Silverman, B. W. (1982). Algorithm AS 176: Kernel Density Estimation Using the Fast Fourier Transform. Journal of the Royal Statistical Society. Series C (Applied Statistics), 31(1), 93–99.
Silverman, B. W. (1986). Density estimation for statistics and data analysis. Routledge.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195152968.001.0001
Slocum, T. A., McMaster, R. B., Kessler, F. C., & Howard, H. H. (2022). Thematic Cartography and Geovisualization. CRC Press.
Snijders, T. A. (2005). Fixed and Random Effects. Encyclopedia of Statistics in Behavioral Science, 2(2), 664–665.
Snow, J. (1856). Cholera and the Water Supply in the South Districts of London in 1854. Journal of Public Health, and Sanitary Review, 2(7), 239.
Soares, S. (2010). The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices. MC Press.
Soares, S. (2015). The Chief Data Officer Handbook for Data Governance. Mc Press.
Sokal, R. R., & Rolf, F. J. (2012). Biometry. The principles and practice in statistics in biological research, 4th edition. W.H. Freeman; Company.
Soler, W., Gómez, M., Bragulat, E., & Álvarez, A. (2010). El triaje: Herramienta fundamental en urgencias y emergencias. Anales Del Sistema Sanitario de Navarra, 33(1), 55–68.
Späth, H. (1975). Cluster-analyse-algorithmen zur objektklassifizierung und datenreduktion. Verfahren der Datenverarbeitung.
Staniak, M., & Biecek, P. (2019). The Landscape of R Packages for Automated Exploratory Data Analysis. arXiv Preprint arXiv:1904.02101.
Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997a). 10 Potholes in the Road to Information Quality. Computer, 30(8), 38–46. https://doi.org/10.1109/2.607057
Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997b). Data Quality in Context. Communications of the ACM, 40(5), 103–110.
Strozzi, C. (1998). NoSQL-A Relational Database Management System.
Tennekes, M. (2018). tmap: Thematic Maps in R. Journal of Statistical Software, 84(6), 1–39. https://doi.org/10.18637/jss.v084.i06
Tetko, I. V., Livingstone, D. J., & Luik, A. I. (1995). Neural Network Studies. 1. Comparison of Overfitting and Overtraining. Journal of Chemical Information and Computer Sciences, 35(5), 826–833.
Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B, 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Tierney, N. J., & Cook, D. H. (2018). Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations. arXiv Preprint arXiv:1809.02264.
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(sup1), 234–240.
Toharia, L. (2012). El mercado de trabajo en la obra de luis toharia. Ministerio de Empleo y Seguridad Social.
Treder, M. (2020). The Chief Data Officer Management Handbook (M. Treder, Ed.). Apress. https://doi.org/10.1007/978-1-4842-6115-6_6
Tribunale di Lecco, 2a Sezione Penale. (2009). Sentenza nei confronti di Alcaro Luigi + 56 (Operazione Oversize). Procedimento n. 31149/01 + 10309/03 + 42859/03 + 3885/05 RGNR.
Tribunale di Milano, Ufficio del giudice per le indagini preliminari. (2006). Ordinanza di applicazione della misura della custodia cautelare in carcere nei confronti di Alcaro Luigi + 56 (Operazione Oversize). Procedimento n. 31149/01 + 10309/03 + 42859/03 + 3885/05 RGNR.
Tukey, J. W. (1962). The Future of Data Analysis. The Annals of Mathematical Statistics, 33(1), 1–67.
Uriel Jiménez, E., & Peiro Giménez, A. (2000). Introducción al Análisis de Series Temporales. Alfa Centauro.
Ushey, K., Allaire, J., & Tang, Y. (2022). Reticulate: Interface to Python.
Vallone, A., & Chasco, C. (2020). Spatiotemporal Methods for Analysis of Urban System Dynamics: An Application to Chile. The Annals of Regional Science, 64(2), 421–454. https://doi.org/10.1007/s00168-019-00960-9
Vapnik, V. N. (1997). The Support Vector Method. International Conference on Artificial Neural Networks, 261–271.
Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S. Springer.
Vergara, J. R., & Estévez, P. A. (2014). A Review of Feature Selection Methods Based on Mutual Information. Neural Computing and Applications, 24(1), 175–186.
Villas, M., & Camacho, J. (2022). Manual de Ética Aplicada en Inteligencia Artificial. Anaya Multimedia.
Vujović, Ž. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6), 599–606.
Wade, C. (2020). Hands-on gradient boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with python. Packt Publishing Ltd.
Walker, K. (2022). Crsuggest: Obtain suggested coordinate reference system information for spatial data. https://CRAN.R-project.org/package=crsuggest
Wang, R. Y. (1998). A Product Perspective on total data quality management. Communications of the ACM, 41(2), 58–65.
Wasserman, S., & Faust, K. (1995). Social network analysis: Methods and applications. Cambridge University Press.
Webb, G. I. (2011). Filtered–top–k Association Discovery. WIREs Data Mining and Knowledge Discovery, 1(3), 183–192.
Weber, K., Otto, B., & Österle, H. (2009). One Size Does Not Fit AllA Contingency Approach to Data Governance. Journal of Data and Information Quality, 1(1), 1–17. https://doi.org/10.1145/1515693.1515696
Wei, T., Simko, V., Levy, M., Xie, Y., Jin, Y., Zemla, J., Freidank, M., et al. (2017). Package corrplot. Statistician, 56(316), e24.
Wickham, H. (2015). Advanced R. CRC Press.
Wickham, H. (2016). Ggplot2: Elegant Graphics for Data Analysis (p. 260). Springer International Publishing, second edition. https://doi.org/10.1007/978-3-319-24277-4
Wickham, H. (2021). Mastering shiny. O’Reilly Media, Inc.
Wickham, H., & Grolemund, G. (2016). R for data science. O’Reilly Media. http://r4ds.had.co.nz/
Wickham, H., Ooms, J., & Müller, K. (2023). RPostgres: Rcpp interface to PostgreSQL. https://CRAN.R-project.org/package=RPostgres
Wikle, C. K., Zammit-Mangion, A., & Cressie, N. (2019). Spatio-temporal statistics with r. Chapman & Hall/CRC.
Wilks, S. S. (1935). The Likelihood Test of Independence in Contingency Tables. Annals of Mathematical Statistics, 6(4), 190–196. https://doi.org/10.121/aoms/1177732564
Wismüller, A., Verleysen, M., Lee, J. A., & Aupetit, M. (2010). Recent advances in nonlinear dimensionality reduction, manifold and topological learning. Conference: ESANN 2010, 18th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 28-30, 2010, Proceedings, 71–80.
Wood, S. N. (2006). Generalized additive models - an introduction with r. Chapman & Hall. https://doi.org/10.1201/9781315370279
Wu, C., & Thompson, M. E. (2020). Sampling theory and practice (J. Chen & D.-G. Chen, Eds.). Springer.
Xiao, N. (2018). Awesome Shiny Extensions. GitHub.
Xie, Y. (2017). Dynamic documents with R and knitr. CRC Press.
Xie, Y., Allaire, J. J., & Grolemund, G. (2019). R Markdown: The Definitive Guide. Taylor & Francis, CRC Press. https://bookdown.org/yihui/rmarkdown/
Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms. CRC Press.
Zivkovic, J. (2023). worldfootballR: Extract and Clean World Football (Soccer) Data. https://github.com/JaseZiv/worldfootballR
Zou, H., & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society. Series B, 67, 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x