AN APPROXIMATE MAXIMUM-LIKELIHOOD-ESTIMATION FOR NON-GAUSSIAN NONMINIMUM PHASE MOVING AVERAGE PROCESSES

被引：30

作者：

LII, KS ^{[1
]}

ROSENBLATT, M ^{[1
]}

机构：

[1] UNIV CALIF SAN DIEGO,LA JOLLA,CA 92093

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 1992年 / 43卷 / 02期

关键词：

APPROXIMATE MAXIMUM LIKELIHOOD ESTIMATES; ASYMPTOTIC NORMALITY; MOVING AVERAGE; NONMINIMUM PHASE; NONINVERTIBLE; NON-GAUSSIAN;

D O I：

10.1016/0047-259X(92)90037-G

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：

An approximate maximum likelihood procedure is proposed for the estimation of parameters in possibly nonminimum phase (noninvertible) moving average processes driven by independent and identically distributed non-Gaussian noise. Under appropriate conditions, parameter estimates that are solutions of likelihood-like equations are consistent and are asymptotically normal. A simulation study for MA(2) processes illustrates the estimation procedure. © 1992.

引用

页码：272 / 299

页数：28

共 15 条

[1]

[Anonymous], 1985, STATIONARY SEQUENCES

[2] PHASE AND AMPLITUDE RECOVERY FROM BISPECTRA [J].