Research Papers in Statistical Inference for Time Series and Related Models
Springer Verlag, Singapore
978-981-99-0805-9 (ISBN)
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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
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.
| Erscheinungsdatum | 04.06.2024 |
|---|---|
| Zusatzinfo | 55 Illustrations, color; 28 Illustrations, black and white |
| Verlagsort | Singapore |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| 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 | 981-99-0805-1 / 9819908051 |
| ISBN-13 | 978-981-99-0805-9 / 9789819908059 |
| Zustand | Neuware |
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