Revised empirical likelihood
Revised empirical likelihood
Empirical Likelihood (EL) and other methods that operate within the Empirical Estimating Equations (E3) approach to estimation and inference are challenged by the Empty Set Problem (ESP). ESP concerns the possibility that a model set, which is data-dependent, may be empty for some data sets. To avoid ESP we return from E3 back to the Estimating Equations, and explore the Bayesian infinite-dimensional Maximum A-posteriori Probability (MAP) method. The Bayesian MAP with Dirichlet prior motivates a Revised EL (ReEL) method. ReEL i) avoids ESP as well as the convex hull restriction, ii) attains the same basic asymptotic properties as EL, and iii) its computation complexity is comparable to that of EL.
estimating equations, maximum aposteriori probability, empirical likelihood, generalized minimum contrast, convex hull restriction, empirical estimating equations, generalized empirical likelihood, Research Methods/ Statistical Methods, empty set problem
estimating equations, maximum aposteriori probability, empirical likelihood, generalized minimum contrast, convex hull restriction, empirical estimating equations, generalized empirical likelihood, Research Methods/ Statistical Methods, empty set problem
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