Multiple inverse sampling in post-stratification

✍ Scribed by Kuang-Chao Chang; Jeng-Fu Liu; Chien-Pai Han

• Book ID: 104340511
• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 725 KB
• Volume: 69
• Category: Article
• ISSN: 0378-3758
• DOI: 10.1016/s0378-3758(97)00157-2

✦ Synopsis

In sample survey, post-stratification is often used when the identification of stratum cannot be achieved in advance of the survey. If the sample size is large, post-stratification is usually as effective as the ordinary stratification with proportional allocation. However, in the case of small samples, no general acceptable theory or technique has been well developed. One of the main difficulties is the possibility of obtaining zero sample sizes in some strata for small samples. In this paper, we overcome this difficulty by employing a sampling scheme referred to as the multiple inverse sampling such that each stratum is ensured to be sampled a specified number of observations. A Monte Carlo simulation is carried out to compare the estimator obtained from the multiple inverse sampling with some other existing estimators. The estimator under multiple inverse sampling is superior in the sense that it is unbiased and its variance does not depend on the values of stratum means in the population. (~) 1998 Elsevier Science B.V. All rights reserved.