Wiley Series in Probability and Statistics: Time Series Analysis by Wilfredo Palma read ebook FB2, PDF
9781118634325 1118634322 A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad CatÓlica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley., A self-contained, contemporary treatment of the analysis of introductory time series data in an accessible and highly readable style.Time Series Analysis is the result of more than 20 years of teaching courses at both the upper-undergraduate and beginning-graduate levels. This book provides a broad coverage of the most fundamental aspects of time series analysis and its applications at an introductory level. As a consequence, the text focuses only on the treatment of univariate time series, covering a number of well-known models such as ARMA and ARIMA. It also provides an updated coverage of several useful and newly-developed techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, and local stationarity, missing values and outliers, threshold models, among others. The topics are systematically organized in a progressive manner so as to provide suitable continuity from beginning to end. Examples, exercise sets, and their corresponding solutions are plentiful. Theory is discussed when relevant.A companion Web site is available for readers to access the R data sets used within the text., Time Series is the result of more than 20 years of teaching courses at both the upper-undergraduate and beginning-graduate levels. The main motivation is to provide a broad coverage of the most fundamental aspects of time series analysis and its applications at an introductory level. As a consequence, the text focuses only on the treatment of univariate time series, covering a number of well-known models such as ARMA and ARIMA. The text also provides an updated coverage of several useful and newly-developed techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, and local stationarity, missing values and outliers, threshold models, among others. The topics are systematically organized in a progressive manner so as to provide suitable continuity from beginning to end. Examples, exercise sets, and their corresponding solutions are plentiful. Theory is discussed when relevant. Every effort is made to make the book self-contained. A companion Web site is available for readers to access the R data sets used within the text.
9781118634325 1118634322 A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad CatÓlica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley., A self-contained, contemporary treatment of the analysis of introductory time series data in an accessible and highly readable style.Time Series Analysis is the result of more than 20 years of teaching courses at both the upper-undergraduate and beginning-graduate levels. This book provides a broad coverage of the most fundamental aspects of time series analysis and its applications at an introductory level. As a consequence, the text focuses only on the treatment of univariate time series, covering a number of well-known models such as ARMA and ARIMA. It also provides an updated coverage of several useful and newly-developed techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, and local stationarity, missing values and outliers, threshold models, among others. The topics are systematically organized in a progressive manner so as to provide suitable continuity from beginning to end. Examples, exercise sets, and their corresponding solutions are plentiful. Theory is discussed when relevant.A companion Web site is available for readers to access the R data sets used within the text., Time Series is the result of more than 20 years of teaching courses at both the upper-undergraduate and beginning-graduate levels. The main motivation is to provide a broad coverage of the most fundamental aspects of time series analysis and its applications at an introductory level. As a consequence, the text focuses only on the treatment of univariate time series, covering a number of well-known models such as ARMA and ARIMA. The text also provides an updated coverage of several useful and newly-developed techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, and local stationarity, missing values and outliers, threshold models, among others. The topics are systematically organized in a progressive manner so as to provide suitable continuity from beginning to end. Examples, exercise sets, and their corresponding solutions are plentiful. Theory is discussed when relevant. Every effort is made to make the book self-contained. A companion Web site is available for readers to access the R data sets used within the text.