• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.1-12
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• 2006

The aim of this study is to analyse a survey data on the number of charitable donations using a mixture of two Poisson regression models. The survey was conducted in 2002 by Volunteer 21, an nonprofit organization, based on Koreans, who were older than 20. The mixture of two Poisson distributions is used to model the number of donations based on the empirical distribution of the data. The mixture of two Poisson distributions implies the whole population is subdivided into two groups, one with lesser number of donations and the other with larger number of donations. We fit the mixture of Poisson regression models on the number of donations to identify significant covariates. The expectation-maximization algorithm is employed to estimate the parameters. We computed 95% bootstrap confidence interval based on bias-corrected and accelerated method and used then for selecting significant explanatory variables. As a result, the income variable with four categories and the volunteering variable (1: experience of volunteering, 0: otherwise) turned out to be significant with the positive regression coefficients both in the lesser and the larger donation groups. However, the regression coefficients in the lesser donation group were larger than those in larger donation group.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.743-753
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• 2014

Kim et al. (2006) and Kim et al. (2009) reported a set of explanatory variables affecting donation frequency when they analyzed nationwide survey data on donations collected in 2002 by Volunteer 21, a nonprofit organization in Korea. The primary purpose of this paper is to correct computational errors found in Kim et al. (2006) and Kim et al. (2009), to rectify major results in the Tables and Figures and to supplement Kim et al. (2009) by providing new results. We add two logistic regressions to the ZIP and a mixture of two Poisson regressions of Kim et al. (2009). Through these two logistic regressions we could detect a set of explanatory variables affecting donation activity (0 or 1) and another set of explanatory variables, in which the volunteer (0, 1) variable is common, discriminating the infrequent donor group from the frequent donor group.