• Communications for Statistical Applications and Methods
• /
• v.29 no.4
• /
• pp.413-420
• /
• 2022

In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.

• Communications for Statistical Applications and Methods
• /
• v.26 no.3
• /
• pp.325-336
• /
• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• Communications for Statistical Applications and Methods
• /
• v.31 no.1
• /
• pp.55-64
• /
• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.

• The Korean Journal of Applied Statistics
• /
• v.19 no.3
• /
• pp.505-519
• /
• 2006

We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.

• Journal of Powder Materials
• /
• v.22 no.5
• /
• pp.337-343
• /
• 2015

Powder compaction is a continually and rapidly evolving technology where it is a highly developed method of manufacturing reliable components. To understand existing mechanisms for compaction, parameter investigation is required. Experimental investigations on powder compaction process, followed by numerical modeling of compaction are presented in this paper. The experimental work explores compression characteristics of soft and hard ductile powder materials. In order to account for deformation, fracture and movement of the particles, a discrete-finite element analysis model is defined to reflect the experimental data and to enable investigations on mechanisms present at the particle level. Effects of important simulation factors and process parameters, such as particle count, time step, particle discretization, and particle size on the powder compaction procedure have been explored.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.11
• /
• pp.2275-2280
• /
• 2009

u-Healthcare service is one of attractive applications in ubiquitous environment. In this paper, we propose a method to recognize exercises using a new accelerometer based body-attached platform for supporting u-Healthcare service. The platform consists of a device for measuring accelerometer data and a device for receiving the data. The former measures a user's motion data using a 3-axis accelerometer. The latter transmits the accelerometer data to a computer for recognizing the user's exercise. The algorithm for exercise recognition classifies the type of exercise using principle components analysis(PCA) from the accelerometer data transformed by discrete fourier transform(DFT), and estimates the repetition count of the recognized exercise using a peak detection algorithm. We evaluate the performance of the algorithm from the accuracy of the recognition of exercise type and the error rate of the estimation of repetition count. In our experimental result, the algorithm shows the accuracy about 98%.

• The Korean Journal of Applied Statistics
• /
• v.21 no.4
• /
• pp.603-613
• /
• 2008

We often encounter the situation that discrete count data have a large portion of zeros. In this case, it is not appropriate to analyze the data based on standard regression models such as the poisson or negative binomial regression models. In this article, we consider Bayesian analysis for two commonly used models. They are zero-inflated poisson and negative binomial regression models. We use the Bayes factor as a model selection tool and computation is proceeded via Markov chain Monte Carlo methods. Crash count data are analyzed to support theoretical results.

• Ocean and Polar Research
• /
• v.36 no.3
• /
• pp.289-302
• /
• 2014

This paper aims to evaluate the indirect economic damages to anglers of the marine recreational charter caused by marine pollution associated with the Herbei Spirit vessel, which spilled 12,547 kl of crude oil in Taean coastal areas in December 2007. In order to evaluate the indirect cost to anglers of the charter fishing, consumer surplus for charter fishing is estimated using a Poisson model (PM), a negative binomial model (NBM), a truncated Poisson model (TPM), and a truncated negative binomial model (TNBM), which account for the characteristics of count data (non-negative discrete data), for individual travel cost method (ITCM). Because of over-dispersion problem in PM and TPM, NBM and TNBM are considered to be more appropriate statistically. All parameters such as income, fishing careers, travel cost and catch that are estimated are statistically significant and theoretically valid. Based on TNBM results, consumer surplus per trip and per person was estimated to be 277 thousand won, total consumer surplus per person and per year about 2.3 million won, and the marginal effect of consumer surplus on % changes in catch rate is about 33 thousand won. The consumer surplus was converted into total indirect economic damages for aggregation which are evaluated to be 125 billion won, reflecting the number of anglers and damage rate.

• Ocean and Polar Research
• /
• v.30 no.2
• /
• pp.141-148
• /
• 2008

This paper aims at estimating consumer surplus for recreational sea fishing in Tongyeong coastal area using individual travel cost method. A Poisson model (PM), a negative binomial model (NBM), a truncated Poisson model (TPM), and a truncated negative binomial model (TNBM) are applied for individual travel cost method in order to account characteristics of count data (non-negative discrete data.) The survey was conducted for 462 inshore anglers using personal interview method in Tongyeong during July and October 2007. Respondents were asked about how often they do fishing, travel costs, catch, income, and so on. Because of over-dispersion problem in PM and TPM, NBM and TNBM were considered to be more appropriate statistically. All parameters estimated are statistically significant and theoretically valid. As the results based on TNBM, consumer surplus per trip was estimated to be 183,486 won, total consumer surplus per person and per year 3,399,658 won, and the marginal effect of consumer surplus on % changes in catch rate is 185,372 won.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.4
• /
• pp.851-858
• /
• 2012

We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.