Estimation and test of linearity for a class of additive nonlinear models
✍ Scribed by Nathalie Chèze-Payaud; Jean-Michel Poggi; Bruno Portier
- Book ID
- 104302898
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 619 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
✦ Synopsis
This paper deals with the estimation and the test for linearity of models belonging to a class of additive nonlinear ones. We prove the joint asymptotic normality for a kernel estimator and provide a test for linearity of each function defining the model.
📜 SIMILAR VOLUMES
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
In this paper we derive general formulae for second-order biases of maximum-likelihood estimates in a class of symmetric nonlinear regression models. This class of models is commonly used for the analysis of data containing extreme or outlying observations in samples from a supposedly normal distrib