LARGE-SAMPLE INFERENCE FOR CONDITIONAL EXPONENTIAL-FAMILIES WITH APPLICATIONS TO NONLINEAR TIME-SERIES

被引：6

作者：

HWANG, SY ^{[1
]}

BASAWA, IV ^{[1
]}

机构：

[1] UNIV GEORGIA,DEPT STAT,ATHENS,GA 30602

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 1994年 / 38卷 / 02期

关键词：

LOCAL ASYMPTOTIC NORMALITY; LIKELIHOOD RATIO; CONDITIONAL EXPONENTIAL FAMILY; NONLINEAR TIME SERIES; EFFICIENT ESTIMATION;

D O I：

10.1016/0378-3758(94)90032-9

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Let {Y(t)}, t=0, +/-1, +/-2,..., be a stationary ergodic Markov process taking values on the real line and such that the transition density function belongs to a conditional exponential family. In this paper, we establish the local asymptotic normality (LAN) of the log-likelihood ratio for this model. The LAN property leads to asymptotically optimal estimators and tests for the model parameters. The results are then applied to a class of nonlinear time series which includes viz. the random coefficient exponential and the random coefficient threshold autoregressive models.

引用

页码：141 / 157

页数：17

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