Research Papers in Statistical Inference for Time Series and Related Models: Essays in Honor of Masanobu Taniguchi - Rilegato

Rilegato

ISBN 10: 9819908027 ISBN 13: 9789819908028

Casa editrice: Springer Nature, 2023

Riassunto

This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models.  Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes.

The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.

Su questo libro

Informazioni sull?autore

Yan Liu, Waseda University

Junichi Hirukawa, Niigata University

Yoshihide Kakizawa, Hokkaido University

Dalla quarta di copertina

This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models.  Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes.

The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the readerwith comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9819908027
• ISBN 13: 9789819908028
• Rilegatura: Copertina rigida
• Numero di pagine: 608
• Redattore: Liu Yan, Hirukawa Junichi, Kakizawa Yoshihide
• Contatto del produttore: non disponibile
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