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Adaptive M-estimation in Regression Model
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 Title & Authors
Adaptive M-estimation in Regression Model
Han, Sang-Moon;
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 Abstract
In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.
 Keywords
regression;adaptive M-estimators;selector statistics;
 Language
Korean
 Cited by
1.
Nonparametric Estimation using Regression Quantiles in a Regression Model,;;

응용통계연구, 2012. vol.25. 5, pp.793-802 crossref(new window)
1.
Nonparametric Estimation using Regression Quantiles in a Regression Model, Korean Journal of Applied Statistics, 2012, 25, 5, 793  crossref(new windwow)
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