Clarifying missing at random and related definitions, and implications when coupled with exchangeability.

Autor:	MEALLI, FABRIZIA, RUBIN, DONALD B.
Předmět:	MISSING data (Statistics) DATA analysis PRESUPPOSITION (Logic) BAYESIAN analysis RANDOM effects model
Zdroj:	Biometrika; Dec2015, Vol. 102 Issue 4, p995-1000, 6p
Abstrakt:	We clarify the key concept of missingness at random in incomplete data analysis. We first distinguish between data being missing at random and the missingness mechanism being a missing-at-random one, which we call missing always at random and which is more restrictive.We further discuss how, in general, neither of these conditions is a statement about conditional independence.We then consider the implication of the more restrictive missing-always-at-random assumption when coupled with full unit-exchangeability for the matrix of the variables of interest and the missingness indicators: the conditional distribution of the missingness indicators for any variable that can have a missing value can depend only on variables that are always fully observed. We discuss implications of this for modelling missingness mechanisms. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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