Bayesian hierarchical robust factor analysis models for partially observed sample-selection data

Kim, Hea-Jung

doi:10.1016/j.jmva.2017.11.003

Cited by 3 publications

(2 citation statements)

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“…For the analysis, we propose a robust sample-selection GPR (RSGPR) model based on a family of scale mixtures of normal (SMN) distributions (see [ 21 , 22 ] for details). This approach reflects the MNAR mechanism as well as its robustness against departures from the normality assumption (see, e.g., [ 11 , 12 ]), and proposes a robust GPR model to analyze the partially observed sample-selection data.…”

Section: Robust Sample-selection Gpr Modelmentioning

confidence: 99%

“…In this regard, several estimation procedures have been considered in the literature to produce robust linear regression models that are subject to sample selection including for instance [ 6 , 7 ], for frequentist methods, and [ 8 , 9 , 11 ] for Bayesian methods. See [ 9 , 12 ] to obtain robust Bayesian sample-selection models other than the regression model. In addition, no studies have generalized a nonparametric regression model to deal with non-normal data with the sample selection.…”

Section: Introductionmentioning

confidence: 99%

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On a Robust MaxEnt Process Regression Model with Sample-Selection

Kim

Bae

Jin

2018

Entropy

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Abstract:In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt) process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR) model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR) model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

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