• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• 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.19 no.3
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• pp.505-519
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• 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 the Korean Statistical Society
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• v.22 no.2
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• pp.171-190
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• 1993

포아송분포로부터 부의 이항분포로의 이탈을 검색하는 통계량들이 자료의 형태에 따라 여러가지 제시되었다. 그런데 대립가설인 부의 이항분포의 모수화 방법에 따라 분산과 평균의 구조가 변하고 국소 최적 검정 통계량도 달라진다는 것이 알려졌다. 본 논문에서는 대립가설을 일반적인 포아송 혼합분포로까지 확장시키고, 일반적인 형태의 분산과 평균의 구조에도 검정 가능한 새로운 통계량 L을 소개하고 있다. 또한 L 통계량은 포아송 분포로부터 부의 이항분포로의 이탈을 다루는 기존의 여러 통계량들의 일반화된 형태임을 보였다. 점근적 상대효율과 모의 실험을 통하여 L 통계량과 기존의 통계량들을 비교한 결과 분산과 평균사이의 구조에 상관없이 L 통계량이 우수한 것임을 입증하였다.

• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.835-843
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• 2008

In this paper, we investigate a Bayesian inference for software reliability models based on mean value functions which take the form of the mixture of beta distribution functions. The posterior simulation via the Markov chain Monte Carlo approach is used to produce estimates of posterior properties. Its applicability is illustrated with two real data sets. We compute the predictive distribution and the marginal likelihood of various models to compare the performance of them. The model comparison results show that the model based on the beta-mixture performs better than other models.

• Advances in materials Research
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• v.5 no.4
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• pp.263-277
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• 2016

Mechanical properties of $Al/Al_2O_3$ and $Al/B_4C$ composites prepared through powder metallurgy are estimated up to 50% $Al_2O_3$ and 35% $B_4C$ weight fractions using micromechanics models and experiments. The experimental Young's modulus up to 0.40 weight fraction of ceramic is found to lie closely between Ravichandran's/Hashin-Shtrikman lower/upper bounds, and close to self consistent method/Miller and Lannutti method/modified rule of mixture/fuzzy logic method single value predictions. Measured Poisson's ratio lies between rule of mixture/Ravichandran lower and upper bound/modified Ravichandran upper bounds. Experimental Charpy energy lies between Hopkin-chamis method/equivalent charpy energy/Ravichandran lower limit up to 20%, and close to the reciprocal rule of mixture for higher $Al_2O_3$ content. Rockwell hardness (RB) and Micro-hardness of $Al/Al_2O_3$ are closer to modified rule of mixture predictions.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.173-184
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• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1269-1276
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• 2004

The Parameter Estimation for software existing reliability models, Goel-Okumoto, Yamada-Ohba-Osaki model was reviewed and Rayleigh model based on Rayleigh distribution was studied. In this paper, we discusses comparison of parameter estimation using maximum likelihood estimator and Bayesian estimation based on Gibbs sampling to analysis of the estimator' pattern. Model selection based on sum of the squared errors and Braun statistic, for the sake of efficient model, was employed. A numerical example was illustrated using real data. The current areas and models of Superposition, mixture for future development are also employed.

• Structural Engineering and Mechanics
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• v.66 no.4
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• pp.495-504
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• 2018

The current study is dedicated to study the thermal effects of wave propagation in beams, reinforced by carbon nanotubes (CNT). Beams, made up of carbon nanotube reinforced composite (CNTRC) are the future materials in various high tech industries. Herein a Winkler elastic foundation is assumed in order to make the model more realistic. Mostly, CNTs are pervaded in cross section of beam, in various models. So, it is tried to use four of the most profitable reconstructions. The homogenization of elastic and thermal properties such as density, Yong's module, Poisson's ratio and shear module of CNTRC beam, had been done by the demotic rule of mixture to homogenize, which gives appropriate traits in such settlements. To make this investigation, a perfect one, various shear deformation theories had been utilized to show the applicability of this theories, in contrast to their theoretical face. The reigning equation had been derived by extended Hamilton principle and the culminant equation solved analytically by scattering relations for propagation of wave in solid bodies. Results had been verified by preceding studies. It is anticipated that current results can be applicable in future studies.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.