Strong consistency in nonlinear stochastic regression models
成果类型：
Article
署名作者：
Skouras, K
署名单位：
University of London; University College London
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/aos/1015952002
发表日期：
2000
页码：
871-879
关键词：
摘要：
The class of nonlinear stochastic regression models includes most of the linear and nonlinear models used in time series, stochastic control and stochastic approximation schemes. The consistency of least squares estimators was established first by Lai. We present another set of sufficient conditions for consistency, which avoid the use of partial derivatives and are closer in spirit to the conditions presented by Wu for non-stochastic regression models with independent errors.