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Judgement post-stratification for designed experiments

Abstract Details

2006, Doctor of Philosophy, Ohio State University, Statistics.
In many scientific studies, information that is not easily translated into covariates is ignored in the analysis. This type of information, however, may significantly improve inference. In this research, we apply the idea of judgment post-stratification to utilize such information. Specifically, we consider experiments that are conducted under a completely randomized design. Sets of experimental units are formed, and the units in a set are ranked. Estimation is performed conditional on the sets and ranks. We propose a new estimator for a treatment contrast. We improve the new estimator by Rao-Blackwellization. Asymptotic distribution theory and corresponding inferential procedures for both estimators are developed. Simulation studies quantify the superiority of the new estimators and show their desirable properties for small and moderate sample sizes. An example from the design literature illustrates the success of the new techniques. We also develop permutation tests for the new estimators and compare them with the testing procedures based on asymptotic theory.
Steven MacEachern (Advisor)
163 p.

Recommended Citations

Citations

  • Du, J. (2006). Judgement post-stratification for designed experiments [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1148650477

    APA Style (7th edition)

  • Du, Juan. Judgement post-stratification for designed experiments. 2006. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1148650477.

    MLA Style (8th edition)

  • Du, Juan. "Judgement post-stratification for designed experiments." Doctoral dissertation, Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1148650477

    Chicago Manual of Style (17th edition)