کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7546751 1489636 2018 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian hierarchical robust factor analysis models for partially observed sample-selection data
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
پیش نمایش صفحه اول مقاله
Bayesian hierarchical robust factor analysis models for partially observed sample-selection data
چکیده انگلیسی
This paper introduces a class of scale mixtures of normal selection factor (SMNSF) analysis models which are robust against departures from normality and designed to correct sample-selection bias. Various properties of this class of models are established, including a stochastic representation, a distributional hierarchy, and a quantification of sample-selection bias. A hierarchical Bayesian methodology is also developed for estimation purposes. It involves a simple and computationally feasible Markov Chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function. Results from simulation studies attest to the good finite-sample performance of the new model in terms of sample-selection bias reduction and robustness against outliers. A data illustration is included.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 164, March 2018, Pages 65-82
نویسندگان
,