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Ker-Chau Li

Other affiliations: Purdue University
Bio: Ker-Chau Li is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Nonprobability sampling & Linear subspace. The author has an hindex of 2, co-authored 3 publications receiving 21 citations. Previous affiliations of Ker-Chau Li include Purdue University.

Papers
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Journal ArticleDOI
TL;DR: In this article, a class of optimality criteria similar to those well accepted in optimal experimental design theory is introduced to fill the gap between some different and conflicting approaches in survey sampling.
Abstract: SUMMARY This paper attempts to fill the gap between some different and conflicting approaches in survey sampling. Based on a fixed population regression-type model, a class of optimality criteria similar to those well accepted in optimal experimental design theory is introduced. The minimax and superpopulation approaches in survey sampling turn out to correspond to two extreme criteria in the proposed class. This helps understand the role of randomization. The strategy of simple random sampling with sample mean and the Rao-Hartley-Cochran strategy are shown to be criterion-robust.

14 citations

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TL;DR: In this article, the strong consistency of M -estimators in linear models is considered and under some conditions on the ratios of maximum and minimum eigenvalues of the information matrices the desired result is established.

8 citations

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TL;DR: In this article, the subspaces of RN which are invariant under πTWπ for all N × N permutation matrices π are characterized, and the related problem of characterizing the class of linear transformations that leave a given space and all permutations invariant is also solved.

Cited by
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TL;DR: In this article, the authors consider the large sample properties of estimators generated from samples that are not necessarily identically distributed and give general assumptions that lead to the existence, strong consistency, and asymptotic normality of the estimators.

105 citations

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TL;DR: The earliest important papers on planned experiments in Biometrika appeared within a year of each other in 1917 and 1918 as mentioned in this paper and were concerned with design optimality and industrial experiments, the other with agricultural trials and blocking.
Abstract: The earliest important papers on planned experiments in Biometrika appeared within a year of each other in 1917 and 1918. Roughly speaking, one was concerned with design optimality and industrial experiments, the other with agricultural trials and blocking. We find this approximate division of the subject into two parts helpful in describing its development and growth. As a result of the hostility between Fisher and Karl Pearson, much of the development of designs for agriculture in the 1920s and 1930s occurred off the pages of Biometrika. Despite this, we are able to trace a coherent history of the development of the subject from field trials and response surface methods to clinical trials and Bayesian versions of design optimality.

71 citations

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TL;DR: A survey is given of recent statistical work on the design of experiments, based on the literature of the last six years, with the emphasis on nonstandard applications of optimum design theory.
Abstract: Summary A survey is given of recent statistical work on the design of experiments, based on the literature of the last six years. The emphasis is on nonstandard applications of optimum design theory. Reference is made to surveys on the theory of optimum experimental design, crossover designs and incomplete block designs.

63 citations

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TL;DR: The parallel concepts for control in the two areas lead naturally to a discussion of embedding experiments in surveys or surveys in experiments as mentioned in this paper, and the parallel controversies between the two modes of inference, design-based and model-based, used in both the experimental design and sample survey literatures.
Abstract: Summary The design and analysis of randomized experiments and randomly selected sample surveys are traced to the work of Fisher, Neyman and Tchuprov in the 1920's and 1930's, although precursors to their work appeared many years earlier. This paper explores some of the developments flowing from their pioneering efforts with an emphasis on the parallels between the methodologies. After reviewing the basic parallels between concepts in the design of experiments and the design of sample surveys, the paper turns to a new class of parallels linking restricted forms of sampling to the design-of-experiments literature on treatment structures, such as that on balanced incomplete block designs. The parallel concepts for control in the two areas lead naturally to a discussion of embedding experiments in surveys or surveys in experiments. After speculating on the possible causes of the separation of the areas, the paper summarizes the parallel controversies between the two modes of inference, design-based and model-based, used in both the experimental design and sample survey literatures. In summary, the paper proposes how new intertwining concepts and constructs may emerge in future research and enrich future practice.

48 citations