• International Journal of Reliability and Applications
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• v.18 no.2
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• pp.65-82
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• 2017

Abstract. Camilo Dagum proposed several variants of a new model for the size distribution of personal income in a series of papers in the 1970s. He traced the genesis of the Dagum distributions in applied economics and points out parallel developments in several branches of the applied statistics literature. The main aim of this paper is to define a bivariate Dagum distribution so that the marginals have Dagum distributions. It is observed that the joint probability density function and the joint cumulative distribution function can be expressed in closed forms. Several properties of this distribution such as marginals, conditional distributions and product moments have been discussed. The maximum likelihood estimates for the unknown parameters of this distribution and their approximate variance-covariance matrix have been obtained. Some simulations have been performed to see the performances of the MLEs. One data analysis has been performed for illustrative purpose.

• Journal of applied mathematics & informatics
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• v.35 no.5_6
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• pp.477-493
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• 2017

The dual generalized order statistics is a unified model which contains the well known decreasingly ordered random variables like order statistics and lower record values. With this definition we give simple expressions for single and product moments of dual generalized order statistics from Dagum distribution. The results for order statistics and lower records are deduced from the relations derived and some computational works are also carried out. Further, a characterizing result of this distribution on using the conditional moment of the dual generalized order statistics is discussed. These recurrence relations enable computation of the means, variances and covariances of all order statistics for all sample sizes in a simple and efficient manner. By using these relations, we tabulate the means, variances, skewness and kurtosis of order statistics and record values of the Dagum distribution.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.403-411
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• 2011

This paper deals with a group acceptance sampling plan for time truncated tests which are based on the total number of failures from the whole group assuming that the life time of an item follows the Dagum (inverse Burr) distribution. This study is developed when a multiple number of items as a group can be tested simultaneously in a tester. The minimum number of groups required for a given group size and acceptance number is determined such that the producer and consumer risks are satisfied simultaneously at the specified quality level, while the termination time and the number of testers are specified. Comparisons are made between the proposed plan and the existing plan on the basis of size of the groups. Two real examples are provided.

• Journal of Korean Society of Rural Planning
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• v.20 no.3
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• pp.11-20
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• 2014

This research aims to identify regional inequitable development through the analysis of facilities distribution pattern. This study describes the concepts underlying the application of the Gini's coefficient and decomposition method to measure the regional inequitable development in Sun-chang County, Jeonbuk Province, Korea. We used the facility data surveyed for three years, from 2010 to 2012 for facility distribution pattern that RDA surveyed. These data have been serviced on the web. The Lorenz Curve presents a graphical view of the inequitable facility distribution and the Gini's Coefficients quantifies the distribution pattern. And furthermore, Gini Decomposition represents intra regional inequalities. These applied techniques can describe how the local development affects other district and change regional inequalities.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5287-5294
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• 2016

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.