NONLINEAR ADDITIVE ARX MODELS

被引：106

作者：

CHEN, R ^{[1
]}

TSAY, RS ^{[1
]}

机构：

[1] UNIV CHICAGO,GRAD SCH BUSINESS,CHICAGO,IL 60637

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 1993年 / 88卷 / 423期

关键词：

ADDITIVITY; ALTERNATING CONDITIONAL EXPECTATION (ACE) ALGORITHM; BEST SUBSET REGRESSION; BRUTO ALGORITHM; RIVER FLOW; TIME SERIES; VARIABLE SELECTION;

D O I：

10.2307/2290787

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：

We consider in this article a class of nonlinear additive autoregressive models with exogenous variables for nonlinear time series analysis and propose two modeling procedures for building such models. The procedures proposed use two backfitting techniques (the ACE and BRUTO algorithms) to identify the nonlinear functions involved and use the methods of best subset regression and variable selection in regression analysis to determine the final model. Simulated and real examples are used to illustrate the analysis.

引用

页码：955 / 967

页数：13

共 20 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].