Daniel Peña – författare

New statistical methods and future directions of research in time seriesA Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time seriesShared balance between theory and applicationExercise series setsMany real data examplesConsistent style and clear, common notation in all contributions60 helpful graphs and tablesRequiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

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 seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting 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 data Perfect 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.

Derived from the renowned multi-volume International Encyclopaedia of Laws, this monograph provides a survey and analysis of the rules concerning Intellectual Property Law in Colombia. It covers every type of intellectual property right in depth – copyright and neighbouring rights, patents, utility models, trademarks, trade names, industrial designs, plant variety protection, chip protection, trade secrets, and confidential information. Particular attention is paid throughout to recent developments and trends.

The analysis approaches each right in terms of its sources in law and in legislation, and proceeds to such legal issues as subject matter of protection, conditions of protection, ownership, transfer of rights, licences, scope of exclusive rights, limitations, exemptions, duration of protection, infringement, available remedies, and overlapping with other intellectual property rights.

The book provides a clear overview of intellectual property legislation and policy, and at the same time offers practical guidance on which sound preliminary decisions may be based. Lawyers representing parties with interests in Intellectual Property Law in Colombia will welcome this very useful guide, and academics and researchers will appreciate its value in the study of comparative intellectual property law.