Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling
2010/11/23
研討會日期 : 2010-11-23
時間 : 14:30
主講人 : Professor Myoung-Jae Lee
地點 : Conference Room B110
演講者簡介 : Myoung-Jae Lee got his Ph.D. in Economics from University of Wisconsin_Madison in 1989. He is currently serving as Professor of Department of Economics at Korea University. His research field is in econometrics, statistics, labor economics and health economics.
演講摘要 : Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y(‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’–a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EXMLE’ is introduced using an extra random sample on X(not on Y) to estimate the distribution of X. As information on Ymay be hard to get, this estimator’s data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-XMLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EXMLE does not estimate the full FXas it needs only a sample average using the extra sample, and Estimated-EXMLE can be almost as efficient as the ‘Known-FXMLE’. A small scale simulation study is provided to illustrate these points.
