• Journal of Environmental Health Sciences
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• v.38 no.6
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• pp.550-560
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• 2012

Objectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water. Methods: A subset of calculated descriptors was selected from Dragon$^{(R)}$ software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results: The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory. Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.

• Journal of Distribution Science
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• v.13 no.4
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• pp.29-33
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• 2015

Purpose - This paper aims to explain the interactive relationship between small and medium-sized enterprises' clusters and regional economic growth, with Jiangsu Province as an illustrative example. It focuses on studying the promotional effects, if any, of small and medium-sized enterprises' cluster-development on regional economic growth, and vice-versa. Research design, data, and methodology - Data were collected from the Jiangsu Statistical Yearbook and the China Industrial Economic Statistical Yearbook, by selecting 26 industries as the research subjects. The sample interval selection is 1981-2012. The data were analyzed with the dynamic panel system using stata 12.0. Results - 1) The small and medium-sized enterprises' cluster degree and Jiangsu's economic growth have a long-term stable equilibrium relationship. 2) In the short term, they have a dynamic adjustment 3) The enterprises' cluster degree leads to regional economic growth in Jiangsu, but not vice-versa. Conclusions - Small and medium-sized enterprises' clusters have an important promotional effect on Jiangsu's economic growth, especially industries with high degree of agglomeration. Therefore, the formation of these clusters can significantly improve economic growth.

• Asian Journal of Business Environment
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• v.4 no.3
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• pp.13-18
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• 2014

Purpose - This study examines students' perspectives (stream-wise) of parameters affecting education quality in an affiliated undergraduate engineering institution in NCR, Haryana. Research design, data, and methodology - This study involves descriptive research and data collection using a structured questionnaire based on the Likert scale. The sample comprises 500 student respondents. For data analysis, an f-test was performed using high quality SPSS software. Results - For "Selection" and "Personality Development & Industry Exposure," the analysis of variance revealed a statistical difference between the mean values of the groups. Whereas, for "Academic Excellence," "Infrastructure," "Placements," and "Management & Administration," the analysis of variance revealed no statistical difference between the mean values of the groups. Conclusions - Students' perceptions about the "Selection" and "Personality Development & Industry Exposure" change according to the various specializations they opt for in their undergraduate engineering education in Haryana. Whereas, for "Academic Excellence," "Infrastructure," "Placements," and "Management & Administration," the perceptions of the students do not vary because of the different specializations they have opted for in their undergraduate engineering education.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.425-448
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• 2003

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution proposed originally by Chen et al. (1999) and Branco and Dey (2001). These rich classes of models combine the information of independent studies, allowing investigation of variability both between and within studies, and incorporate weight function. Here, the testing for the skewness parameter is discussed. The score test statistic for such a test can be shown to be expressed as the posterior expectations. Also, we consider the detail computational scheme under skewed normal and skewed Student-t distribution using MCMC method. Finally, we introduce one example from Johnson (1993)'s real data and apply our proposed methodology. We investigate sensitivity of our results under different skewed errors and under different prior distributions.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.121-135
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• 2015

We consider testing equality of mean functions from two samples of functional data. A novel test based on the adaptive Neyman methodology applied to the Hotelling's T-squared statistic is proposed. Under the enlarged null hypothesis that the distributions of the two populations are the same, randomization methods are proposed to find a null distribution which gives accurate significance levels. An extensive simulation study is presented which shows that the proposed test works very well in comparison with several other methods under a variety of alternatives and is one of the best methods for all alternatives, whereas the other methods all show weak power at some alternatives. An application to a real-world data set demonstrates the applicability of the method.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.205-220
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• 2012

The construction industry in Korea after the Korean-war has evolved until these days. But the construction industry accident severity rate and frequency is over then the All industry rate. This study analyzed the 'Disaster Statistical Yearbook' of the Korea Occupational Safety and Health Agency, based on the factors that affect construction accidents that is selected and fined the some factors the construction Disaster Prevention Factors. This study will develop the methodology for analyzes that the national qualification is effected to the construction industrial machine disaster prevention status. It suggest two ways to the establishment of disaster trends. First way is the disaster quantitative analysis and second way is comparing the statistical data and the analysis of expert opinion.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.103-112
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• 2012

The construction industry in Korea after the Korean-war has evolved until these days. But the construction industry accident severity rate and frequency is over then the All industry rate. This study analyzed the 'Disaster Statistical Yearbook' of the Korea Occupational Safety and Health Agency, based on the factors that affect construction accidents that is selected and fined the some factors the construction Disaster Prevention Factors. This study will develop the methodology for analyzes that the national qualification is effected to the construction industrial machine disaster prevention status. It suggest two ways to the establishment of disaster trends. First way is the disaster quantitative analysis and second way is comparing the statistical data and the analysis of expert opinion.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.151-159
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• 1990

Regression diagnostics often involves assesment of the changes that result from deleting multiple cases. Diagnostic mehtodology based on global influence measure, however, needs prohibitive computing time. As an alternative, Cook (1986) developed influence approach in which it is checked whether a minor modification of specifiation influences key results of an analysis. In line with Cook's development, we propose and study an inflence derivative method that yields both the magnitude and direction of case influences. The utility of our methodology is highlighted when case influence derivatives are plotted in a lower demensional space. Such plots are especially effective in unmasking "masked" observations in least squares regression and in robust regression also. We give several illustrations.strations.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.801-812
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• 1999

Change-point hazard rate models arise for example in applying "burn-in" techniques to screen defective items and in studing times until undesirable side effects occur in clinical trials. Sometimes in screening defectives it might be sensible to model two stages of burn-in. In a clinical trial there might be an initial hazard rate for a side effect which after a period of time changes to an intermediate hazard rate before settling into a long term hazard rate. In this paper we consider the multiple change points hazard rate model. The classical approach's asymptotics can be poor for the small to all moderate sample sizes often encountered in practice. We propose a Bayesian approach avoiding asymptotics to provide more reliable inference conditional only upon the data actually observed. The Bayesian models can be fitted using simulation methods. Model comparison is made using recently developed Bayesian model selection criteria. The above methodology is applied to a generated data and to a generated data and the Lawless(1982) failure times of electrical insulation.