New Estimation and Inference Procedures for a Single-Index Conditional Distribution Model

計畫名稱:New Estimation and Inference Procedures for a Single-Index Conditional Distribution Model

所屬單位:數學系

研究團隊:天文數學館559研究室

計畫主持人:江金倉

研究人員:吳允中

資源需求:R

使用期間:2011/04~

研究主題:
New Estimation and Inference Procedures for a Single-Index Conditional Distribution Model

研究內容概述:
In this study, we employed more flexible single-index and multiple-index regression models to characterize conditional distributions. Different pseudo estimation approaches are proposed to estimate the index coefficients. The numerical results will be used to show that our estimators outperform the existing ones in terms of the mean squared error. Moreover, we will provide the generalized cross-validation criteria for bandwidth selection and utilize the frequency distributions of weighted bootstrap analogues for the estimation of asymptotic variance and the construction of confidence intervals. With a defined residual process, a test rule is established to check the adequacy of an applied conditional distribution model. To tackle with the problem of sparse variables, a multi-stage adaptive Lasso algorithm is developed to enhance the ability of identifying significant variables. All of our procedures are found to be easily implemented, numerically stable, and highly adaptive to a variety of data structures.

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