Bøker av Daniel Pena

El amor es ese lugar al que siempre volvemos, cuando estamos felices, cuando estamos heridos, cuando no nos queda nada, cuando lo queremos todo. A veces es un lugar común, lleno de melodrama y frases de cajón; otras veces, es un lugar secreto, lleno de miradas furtivas, silencios dicientes y sonrisas disimuladas; pero la mayoría de las veces es un lugar mágico, lleno de abrazos reconfortantes y corazones desbocados; la mayoría de las veces es ese amor el que mueve el mundo. Por eso, desde ITA les damos la bienvenida a esta antología en la que cada autor nos dejó una muestra del amor que vive, y te invitamos a que te dejes abrazar -y abrasar- por él. (c)Daniel Peña, (c)Mauricio Pedraza Silva, (c)Manuel Díaz, (c)Yurena Peñaloza, (c)James Ruiz, (c)Laura Marcela Reyes Barreto, (c)Ruth Valdelamar Hernández, (c) Cristina Nieto Oliveros, (c)Juliana Arias Suárez, (c)Jennifer Esquivel Sachica, (c)Edward Londoño, (c)Luz Támara Higuera, (c)Jennyfer Rodríguez, (c)Jair Sarmiento Pineda, (c)María Corredor Cruz, (c)Corina González, (c)Luis Robles Neuque, (c)María del Pilar Torres González, (c)Sharon Torres Marroquín, (c)Kevin Clemente Rosario, (c)Lucía Gabrielle González, (c)Karla Romero Marín, (c)Leslie Hassey y (c)David Góngora.

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resourceStatistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented.Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications.Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like:* New ways to plot large sets of time series* An automatic procedure to build univariate ARMA models for individual components of a large data set* Powerful outlier detection procedures for large sets of related time series* New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series* Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models* Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series* Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.* Introduction of modern procedures for modeling and forecasting spatio-temporal dataPerfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data.