George C. Tiao – 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.

Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

Set that includes three works covering statistical decision theory and analysis The three books within this set are Optimal Statistical Decisions, Bayesian Inference in Statistical Analysis, and Applied Statistical Decision Theory. Optimal Statistical Decisions discusses the theory and methodology of decision-making in the field. The volume stands as a clear introduction to Bayesian statistical decision theory. A second book, Bayesian Inference in Statistical Analysis, examines the application and relevance of Bayes' theorem to problems that occur during scientific investigations, where inferences must be made regarding parameter values about which little is known. Key aspects of the Bayesian approach are discussed, including the choice of prior distribution, the problem of nuisance parameters, and the role of sufficient statistics. Applied Statistical Decision Theory covers the development of analytic techniques in the field of statistical decision theory. This classic book was first published in the 1960s.

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. 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 series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables

Requiring 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.

Its main objective is to examine the application and relevance of Bayes'' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.