Missing No More: Using the MCMC Procedure to Model Missing Data
نویسنده
چکیده
Missing data are often a problem in statistical modeling. The Bayesian paradigm offers a natural modelbased solution for this problem by treating missing values as random variables and estimating their posterior distributions. This paper reviews the Bayesian approach and describes how the MCMC procedure implements it. Beginning with SAS/STAT® 12.1, PROC MCMC automatically samples all missing values and incorporates them in the Markov chain for the parameters. You can use PROC MCMC to handle various types of missing data, including data that are missing at random (MAR) and missing not at random (MNAR). PROC MCMC can also perform joint modeling of missing responses and covariates.
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