Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling

Abstract

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-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may 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-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

Citation

Lee, M.-j. and Lee, S. (2011), "Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling", Drukker, D.M. (Ed.) Missing Data Methods: Cross-sectional Methods and Applications (Advances in Econometrics, Vol. 27 Part 1), Emerald Group Publishing Limited, Leeds, pp. 63-91. https://doi.org/10.1108/S0731-9053(2011)000027A006