2011/10/09 by Rawane Samb, Samb, Rawane, Cédric Heuchenne +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1110.1846
arxiv created 2011/10/09 · openalex publication_date 2011/10/09 · arxiv updated 2011/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Consider the semiparametric transformation model Λθo(Y)=m(X)+ε, where θo is an unknown finite dimensional parameter, the functions Λθo and m are smooth, ε is independent of X, and \esp(ε)=0. We propose a kernel-type estimator of the density of the error ε, and prove its asymptotic normality. The estimated errors, which lie at the basis of this estimator, are obtained from a profile likelihood estimator of θo and a nonparametric kernel estimator of m. The practical performance of the proposed density estimator is evaluated in a simulation study.