Research Papers in Statistical Inference for Time Series and Related Models : Essays in Honor of Masanobu Taniguchi / edited by Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa.

Format:

Book

Author/Creator:

Liu, Yan.

Contributor:

Hirukawa, Junichi.

Kakizawa, Yoshihide.

Series:

Mathematics and Statistics Series

Language:

English

Subjects (All):

Time-series analysis.

Mathematical statistics.

Nonparametric statistics.

Time Series Analysis.

Parametric Inference.

Non-parametric Inference.

Mathematical Statistics.

Local Subjects:

Time Series Analysis.

Parametric Inference.

Non-parametric Inference.

Mathematical Statistics.

Physical Description:

1 online resource (591 pages)

Edition:

1st ed. 2023.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2023.

Summary:

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.

Contents:

Chapter 1. Frequency domain empirical likelihood method for infinite variance models

Chapter 2. Diagnostic testing for time series

Chapter 3. Statistical Inference for Glaucoma Detection

Chapter 4. On Hysteretic Vector Autoregressive Model with Applications

Chapter 5. Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression

Chapter 6. Exact topological inference on resting-state brain networks

Chapter 7. An Introduction to Geostatistics

Chapter 8. Relevant change points in high dimensional time series

Chapter 9. Adaptiveness of the empirical distribution of residuals in semi-parametric conditional location scale models

Chapter 10. Standard testing procedures for white noise and heteroskedasticity

Chapter 11. Estimation of Trigonometric Moments for Circular Binary Series

Chapter 12. Time series analysis with unsupervised learning

Chapter 13. Recovering the market volatility shocks in high-dimensional time series

Chapter14. Asymptotic properties of mildly explosive processes with locally stationary disturbance

Chapter 15. Multi-Asset Empirical Martingale Price Estimators for Financial Derivatives

Chapter 16. Consistent Order Selection for ARFIMA Processes

Chapter 17. Recursive asymmetric kernel density estimation for nonnegative data

Chapter 18. Fitting an error distribution in some heteroscedastic time series models

Chapter 19. Symbolic Interval-Valued Data Analysis for Time Series Based on Auto-Interval-Regressive Models

Chapter 20. ROBUST LINEAR INTERPOLATION AND EXTRAPOLATION OF STATIONARY TIME SERIES

Chapter 21. Non Gaussian models for fMRI data

Chapter 22. Robust inference for ordinal response models

Chapter 23. Change point problems for diffusion processes and time series models

Chapter 24. Empirical likelihood approach for time series

Chapter 25. Exploring the Dependence Structure Between Oscillatory Activities in Multivariate Time Series

Chapter 26. Projection-based nonparametric goodness-of-fit testing with functional data.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Liu, Yan Research Papers in Statistical Inference for Time Series and Related Models

ISBN:

9789819908035

9819908035

OCLC:

1381096935