In this paper we propose a new approach for estimating the unknown parameter in the stochastic linear regressive model with stationary ergodic sequence of covariates. Under mild conditions on the joint distribution of the covariate and the error, the estimator constructed is shown to be strongly con
Consistency of a Class of Information Criteria for Model Selection in Nonlinear Regression
โ Scribed by Haughton, D.
- Book ID
- 118226842
- Publisher
- Society for Industrial and Applied Mathematics
- Year
- 1993
- Tongue
- English
- Weight
- 729 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0040-585X
- DOI
- 10.1137/1137009
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
In this paper, we derive general formulae for second-order biases of maximum likelihood estimates which can be applied to a wide class of multivariate nonlinear regression models. The class of models we consider is very rich and includes a number of commonly used models in econometrics and statistic
A broad range of nonlinear (linear) time series and stochastic processes can be described by the stochastic regression model y. = r.(O)+ e., where {en} are independent random disturbances and r. is a random function of an unknown parameter 0 measurable with respect to the a-field ~r(yl ..... y.-l).