Research Papers in Statistical Inference for Time Series and Related Models (eBook)

Essays in Honor of Masanobu Taniguchi

Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa (Herausgeber)

eBook Download: PDF

2023
570 Seiten
Springer Nature Singapore (Verlag)
978-981-99-0803-5 (ISBN)

Infos

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.

Yan Liu, Waseda University

Junichi Hirukawa, Niigata University

Yoshihide Kakizawa, Hokkaido University

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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, Levy 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.

Produkt-Details

• Erscheint lt. Verlag: 31.5.2023
• Zusatzinfo: XXXVII, 570 p. 83 illus., 55 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Schlagworte: Asymptotic Theory • Estimation theory • non-gaussian processes • Non-stationary Processes • Optimal Portfolio Estimation • Statistical hypothesis testing • Statistical Inference • Time Series Analysis
• ISBN-10: 981-99-0803-5 / 9819908035
• ISBN-13: 978-981-99-0803-5 / 9789819908035