MINIMUM DISTANCE ESTIMATION IN RANDOM COEFFICIENT REGRESSION-MODELS

• 成果类型: Article
• 署名作者: BERAN, R; MILLAR, PW
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325767
• 发表日期: 1994
• 页码: 1976-1992
• 关键词: prediction

摘要

Random coefficient regression models are important in modeling heteroscedastic eroscedastic multivariate linear regression in econometrics. The analysis of panel data is one example. In statistics, the random and mixed effects models of ANOVA, deconvolution models and affine mixture models are all special cases of random coefficient regression. Some inferential problems, such as constructing prediction regions for the modeled response, require a good nonparametric estimator of the unknown coefficient distribution. This paper introduces and studies a consistent nonparametric minimum distance method for estimating the coefficient distribution. Our estimator translates the difficult problem of estimating an inverse Radon transform into a minimization problem.