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230601 ||| eng |
| 020 |
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|a 9789819908035
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| 100 |
1 |
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|a Liu, Yan
|e [editor]
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| 245 |
0 |
0 |
|a Research Papers in Statistical Inference for Time Series and Related Models
|h Elektronische Ressource
|b Essays in Honor of Masanobu Taniguchi
|c edited by Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa
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| 250 |
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|a 1st ed. 2023
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2023, 2023
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| 300 |
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|a XXXVII, 570 p. 83 illus., 55 illus. in color
|b online resource
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| 505 |
0 |
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|a 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 --
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| 505 |
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|a 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
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| 505 |
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|a 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 --
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| 653 |
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|a Mathematical statistics
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| 653 |
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|a Mathematical Statistics
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| 653 |
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|a Parametric Inference
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| 653 |
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|a Time-series analysis
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| 653 |
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|a Non-parametric Inference
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| 653 |
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|a Time Series Analysis
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| 653 |
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|a Nonparametric statistics
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| 700 |
1 |
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|a Hirukawa, Junichi
|e [editor]
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| 700 |
1 |
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|a Kakizawa, Yoshihide
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-981-99-0803-5
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| 856 |
4 |
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|u https://doi.org/10.1007/978-981-99-0803-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.55
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| 520 |
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|a 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
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