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COMPARATIVE STUDY OF RESIDUAL VARIANCE ESTIMATORS CONSTRUCTED WITHOUT OR WITH THE PRESENCE OF ATYPICAL VALUES: CASE OF THE PARTIALLY LINEAR MODEL
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Error variance is a measure of the goodness of fit for a regression model. Ahoutou et al. [1] proposed estimators of the error variance under the assumption that the dimension of the nonlinear covariate is one. In this paper, assuming that the dimension of the nonlinear covariate is greater than or equal to 2, we propose estimators of error variance in homogeneous and heterogeneous cases. We propose estimators of the error variance that are constructed without taking atypical values into account and others that are constructed by taking all the data into account. Using simulations, we compare the performance of estimators designed without taking into account atypical values with estimators designed taking into account all the data.
Pushpa Publishing House
Title: COMPARATIVE STUDY OF RESIDUAL VARIANCE ESTIMATORS CONSTRUCTED WITHOUT OR WITH THE PRESENCE OF ATYPICAL VALUES: CASE OF THE PARTIALLY LINEAR MODEL
Description:
Error variance is a measure of the goodness of fit for a regression model.
Ahoutou et al.
[1] proposed estimators of the error variance under the assumption that the dimension of the nonlinear covariate is one.
In this paper, assuming that the dimension of the nonlinear covariate is greater than or equal to 2, we propose estimators of error variance in homogeneous and heterogeneous cases.
We propose estimators of the error variance that are constructed without taking atypical values into account and others that are constructed by taking all the data into account.
Using simulations, we compare the performance of estimators designed without taking into account atypical values with estimators designed taking into account all the data.
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