• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.521-529
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• 2005

In this paper, we consider a bootstrap method to the 'row and column effects model' (RC model) to analyze a contingency table with ordered variables. We propose a bootstrap procedure for testing of independence, equality of intervals, and goodness of fit in the RC model. A real data example is included.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.519-527
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• 2004

Relationship between the partial likelihood ratio statistics for logisitic models and the partial goodness-of-fit statistics for corresponding log-linear models is discussed. This paper shows how definitions of suppression in logistic model can be adapted for log-linear model and how they are related to confounding in terms of collapsibility for categorical data. Several $2{times}2{times}2$ contingency tables are illustrated.

• 응용통계연구
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• 제17권2호
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• pp.337-346
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• 2004

랜덤표본에 관한 이원분할표의 독립성검정에는 통상 피어슨의 카이제곱적합도검정과 우도비검정을 사용한다. 그러나 랜덤표본이 아닌 집락자료에 관한 분할표의 경우에는 이들 검정법은 잘못된 결과를 나타낸다. 이러한 경우에는 공변량의 고정효과 외에 집락에 따른 변량효과를 함께 포함하는 일반화선형혼합모형을 고려함으로써 집락간의 이질성과 집락내의 종속성을 반영할 수 있다. 본 연구에서는 집락자료의 분할표에 대한 일반화선형혼합모형을 소개하고 실례를 통하여 이들 모형의 적합에 대해 논의한다.

• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.401-411
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• 2020

This study aims to analyze the agreement of service innovation using contingency approach (manager personality, organizational structure) moderated by leadership agility. The study has been carried out on Islamic banking companies' managers in Indonesia, from East Java and Yogyakarta region using purposive sampling technique with questionnaire and interviews as the method of data collection. The total number of respondents in the sample is 184. This sample is then analyzed using Euclidience Distance Simple Regression and Simple Regression Moderation method. The results prove that: (1) there is a partial fit between incremental strategy with reactive personality and mechanical organizational structure, which increases the service performance; (2) there is a partial fit between radical strategy with proactive personality and organic organizational structure, which increases the service performance; (3) leadership agility is able to strengthen the fit of the relationship between incremental innovation strategy and reactive personality toward service performance; (4) leadership agility is able to strengthen the fit the relationship between radical innovation strategy and proactive personality toward service performance; (5) leadership agility is able to strengthen the fit of the relationship between incremental innovation strategy and mechanical organizational structure toward service performance; and (6) leadership agility is able to strengthen the fit of the relationship between radical innovation strategy and organic organizational structure toward service performance.

• Journal of the Korean Data and Information Science Society
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• 제27권1호
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• pp.245-254
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• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1547-1555
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• 2015

We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.

• 한국전자거래학회지
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• 제16권4호
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• pp.283-299
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• 2011

본 연구에서는 태스크-기술 적합 이론에 기반하여 TV홈쇼핑, 인터넷 쇼핑, 브로드밴드 응용(IPTV) 등 3개의 가상 쇼핑 채널에 대한 소비자 선호에 영향을 주는 적합요인을 탐색하였다. 적합 요인 탐색은 오프라인 대비 웹에서 제품 구매 시 불확실성에 가장 큰 영향을 주는 품질 파악 관점에서의 제품 분류 유형인 quasi-commodity, look and feel 제품, 그리고 look and feel with variable quality 제품 등 3개의 제품군 별로 차별화하였다. 이론적으로 도출한, 3개의 가상 쇼핑 환경에서 3개의 유형별 제품을 구매할 때 소비자의 채널 선호에 영향을 주는 적합 속성에 대한 검증은 서베이 데이터를 이용해 단계별 회귀분석 기법을 적용하여 실증적으로 분석하였다. 또한 ANOVA와 Duncan 사후 분석을 통하여 3개의 가상 채널과 제품 유형 별로 적합 속성의 상대적 중요성을 비교 분석하여 시사점을 제시하였다. 본 연구를 통하여 3개의 가상 쇼핑 채널이 기반하고 있는 미디어 기술과 다양한 제품 구매 태스크의 적절한 매칭을 위한 4가지 주요 적합 속성과 상대적 차별성이 파악되었으며, 본 연구결과는 디지털 융합 기반 쇼핑 채널 설계에 유용한 시사점을 제공할 수 있다.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.197-212
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• 2002

When assessing goodness-of-fit of a model, a small subset of deviating observations can give rise to a significant lack of fit. It is therefore important to identify such observations and to assess their effects on various aspects of analysis. A Cook's distance measure is usually used to detect influential observation. But it sometimes is not fully effective in identifying truly influential set of observations because there may exist masking or swamping effects. In this paper we confine our attention to influential subset In GLMs such as logistic regression models and loglinear models. We modify a backwards stepping algorithm, which was originally suggested for detecting outlying cells in contingency tables, to detect influential observations in GLMs. The algorithm consists of two steps, the identification step and the testing step. In identification step we Identify influential observations based on influencial measures such as Cook's distances. On the other hand in testing step we test the subset of identified observations to be significant or not Finally we explain the proposed method through two types of dataset related to logistic regression model and loglinear model, respectively.

• Communications for Statistical Applications and Methods
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• 제13권2호
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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.