PARTIAL LINEAR MODELS WITH HETEROSCEDASTIC VARIANCES(Statistical Models for Biomedical Research)

スポンサーリンク

概要

• 論文の詳細を見る

• Consider the partial linear heteroscedastic model Y_i = X^T_iβ + g(T_i) + σ_ie_i1 ≤i≤n with random variables (X_i, T_i) and response variables Yi and unknown regression function g(・). We assume that the errors are heteroscedastic, i.e., σ^2_i≠const, e_i are i.i.d. random errors with mean zero and variance 1. In this partial linear heteroscedastic model, we consider the situations that the variance is an unknown smooth function of exogenous variables, or of nonlinear variables T_i, or of the mean response X^T_iβ + g(T_i). Under the general assumptions, we construct an estimator of the regression parameter vector (β which is asymptotically equivalent to the weighted least squares estimators with known variance. In procedure of constructing the estimators, the technique of splitting-sample is adopted.

• 日本計算機統計学会の論文

著者

• Liang Hua

  Department Of Biostatistics St. Jude Children's Research Hospital

• Hardle Wolfgang

  Institute Of Statistics And Econometrics Humboldt University Of Berlin

• Liang Hua

  Department Of Anesthesiology Nanfang Hospital Southern Medical University

関連論文

• A Novel Mutation (D631del) of the RET Gene Was Associated with MEN2A in a Chinese Pedigree
• Efficacy and risk of aeration to prevent self-heating powder bed from spontaneous ignition
• PARTIAL LINEAR MODELS WITH HETEROSCEDASTIC VARIANCES(Statistical Models for Biomedical Research)
• Sevoflurane inhibits invasion and migration of lung cancer cells by inactivating the p38 MAPK signaling pathway

スポンサーリンク