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
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آنالیز عددی
پیش نمایش صفحه اول مقاله
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
Journal: Journal of Multivariate Analysis - Volume 164, March 2018, Pages 65-82
نویسندگان
Hea-Jung Kim,