• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.55-69
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• 2003

In this article, we propose the weighted LAD (least absolute deviations) estimators for multi-dimensional contingency tables and drive an estimation method to estimate the proposed estimators. To illustrate the robustness of the estimators, simulation results are presented for several models Including log-linear models and models for ordinal variables in multidimensional contingency tables. Examples were also introduced.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1235-1243
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• 2010

This study proposes a trimmed LAD(least absolute deviation) estimators for multi-dimensional contingency tables and suggests an algorithm to estimate it. In addition, a method to determine the trimming quantity of the estimators is suggested. A Monte Carlo study shows that the propose method yields a better trimming rate and coverage rate than the previously suggest method based on the determinant of the covariance matrix.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.69-74
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• 1994

Kim (1992) derived influence functions of rows and columns on the eigenvalues obtained in correspondence analysis (CA) of two-way contingency tables. As in principal component analysis, the eigenvalues are of great importance in CA. The goodness of a two dimensional correspondence plot is determined by the ratio of the sum of the two largest eigenvalues to the sum of all the eigenvalues. By investigating those rows and columns with high influence, a correspondence plot may be improved. In this paper, we extend the influence functions of CA to multiple correspondence analysis (MCA), which is a CA of multi-way contigency tables. An explicit formula of the influence function is given.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.297-307
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• 2006

Most methods for describing the relationship among random variables require specific probability distributions and some assumptions of random variables. The mutual information based on the entropy to measure the dependency among random variables does not need any specific assumptions. And the redundancy which is a analogous version of the mutual information was also proposed. In this paper, the redundancy and mutual information are explored to multi-dimensional categorical data. It is found that the redundancy for categorical data could be expressed as the function of the generalized likelihood ratio statistic under several kinds of independent log-linear models, so that the redundancy could also be used to analyze contingency tables. Whereas the generalized likelihood ratio statistic to test the goodness-of-fit of the log-linear models is sensitive to the sample size, the redundancy for categorical data does not depend on sample size but its cell probabilities itself.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.29-40
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• 2019

Categorical data with non-responses are frequently observed in election poll surveys, and can be represented by incomplete contingency tables. To estimate supporting rates of candidates, the identification of the missing mechanism should be pre-determined because the estimates of non-responses can be changed depending on the assumed missing mechanism. However, it has been shown that it is not possible to identify the missing mechanism when using observed data. To overcome this problem, sensitivity analysis has been suggested. The previously proposed sensitivity analysis can be applicable only to two-way incomplete contingency tables with binary variables. The previous sensitivity analysis is inappropriate to use since more than two of the factors such as region, gender, and age are usually considered in election poll surveys. In this paper, sensitivity analysis suitable to an multi-dimensional incomplete contingency table is devised, and also applied to the 19th Korean presidential election poll survey data. As a result, the intervals of estimates from the sensitivity analysis include actual results as well as estimates from various missing mechanisms. In addition, the properties of the missing mechanism that produce estimates nearest to actual election results are investigated.