A limit theory for long-range dependence and statistical inference on related models
| Autor: | Yuzo Hosoya |
|---|---|
| Rok vydání: | 1997 |
| Předmět: |
Statistics and Probability
Stationary process 62E30 maximum likelihood estimation quadratic forms martingale difference Asymptotic theory 60F05 long-range dependence Calculus Statistical inference Applied mathematics Limit (mathematics) Central limit theorem Mathematics mixing conditions bracketing function Statistical model likelihood ratio test Asymptotic theory (statistics) central limit theorems 62M15 Quadratic form serial covariances 62M10 Martingale difference sequence Statistics Probability and Uncertainty 60G10 |
| Zdroj: | Ann. Statist. 25, no. 1 (1997), 105-137 |
| ISSN: | 0090-5364 |
| DOI: | 10.1214/aos/1034276623 |
| Popis: | This paper provides limit theorems for multivariate, possibly non-Gaussian stationary processes whose spectral density matrices may have singularities not restricted at the origin, applying those limiting results to the asymptotic theory of parameter estimation and testing for statistical models of long-range dependent processes. The central limit theorems are proved based on the assumption that the innovations of the stationary processes satisfy certain mixing conditions for their conditional moments, and the usual assumptions of exact martingale difference or the (transformed) Gaussianity for the innovation process are dispensed with. For the proofs of convergence of the covariances of quadratic forms, the concept of the multiple Fejér kernel is introduced. For the derivation of the asymptotic properties of the quasi-likelihood estimate and the quasi-likelihood ratio, the bracketing function approach is used instead of conventional regularity conditions on the model spectral density. |
| Databáze: | OpenAIRE |
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