• Title, Summary, Keyword: gamma poisson model

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The Decision of Critical Population Size for Releasing Micro Data Files (마이크로데이터 제공에 따른 임계모집단 크기 결정)

  • NamKung, Pyong;So, Joung-Hyun
    • Communications for Statistical Applications and Methods
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    • v.17 no.6
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    • pp.791-801
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    • 2010
  • This study reviews the concept of disclosure, disclosure risks, and uniqueness. The number of uniqueness in the population is of great importance in evaluating the disclosure risk of micro data files. We approach this problem by considering some basic superpopulation models including the Multinomial-Dirichlet model, the Poisson- Gamma model of Bethlehem et al. (1990) and Takemura (1997), and the Modified Multinomial-Dirichlet model. We decided the critical population size of each superpopulation model for four different superpopulation models.

Sire Evaluation of Count Traits with a Poisson-Gamma Hierarchical Generalized Linear Model

  • Lee, C.;Lee, Y.
    • Asian-Australasian Journal of Animal Sciences
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    • v.11 no.6
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    • pp.642-647
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    • 1998
  • A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

Multivariate Gamma-Poisson Model and Parameter Estimation for Polytomous Data : Application to Defective Pixels of LCD (다가자료에 적합한 다변수 감마-포아송 모델과 파라미터 추정방법 : LCD 화소불량 응용)

  • Ha, Jung-Hoon
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.34 no.1
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    • pp.42-51
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    • 2011
  • Poisson model and Gamma-Poisson model are popularly used to analyze statistical behavior from defective data. The methods are based on binary criteria, that is, good or failure. However, manufacturing industries prefer polytomous criteria for classifying manufactured products due to flexibility of marketing. In this paper, I introduce two multivariate Gamma-Poisson(MGP) models and estimation methods of the parameters in the models, which are able to handle polytomous data. The models and estimators are verified on defective pixels of LCD manufacturing. Experimental results show that both the independent MGP model and the multinomial MGP model have excellent performance in terms of mean absolute deviation and the choice of method depends on the purpose of use.

Classification Analysis in Information Retrieval by Using Gauss Patterns

  • Lee, Jung-Jin;Kim, Soo-Kwan
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.1-11
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    • 2002
  • This paper discusses problems of the Poisson Mixture model which Is widely used to decide the effective words in judging relevant document. Gamma Distribution model and Gauss Patterns model as an alternative of the Poisson Mixture model are studied. Classification experiments by using TREC sub-collection, WSJ[1,2] with MGQUERY and AidSearch3.0 system are discussed.

Simulation of the Shifted Poisson Distribution with an Application to the CEV Model

  • Kang, Chulmin
    • Management Science and Financial Engineering
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    • v.20 no.1
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    • pp.27-32
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    • 2014
  • This paper introduces three different simulation algorithms of the shifted Poisson distribution. The first algorithm is the inverse transform method, the second is the rejection sampling, and the third is gamma-Poisson hierarchy sampling. Three algorithms have different regions of parameters at which they are efficient. We numerically compare those algorithms with different sets of parameters. As an application, we give a simulation method of the constant elasticity of variance model.

A Bayesian Approach for Record Value Statistics Model Using Nonhomogeneous Poisson Process

  • Kiheon Choi;Hee chual Kim
    • Communications for Statistical Applications and Methods
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    • v.4 no.1
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    • pp.259-269
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    • 1997
  • Bayesian inference for a record value statistics(RVS) model of nonhomogeneous Poisson process is considered. We seal with Bayesian inference for double exponential, Gamma, Rayleigh, Gumble RVS models using Gibbs sampling and Metropolis algorithm and also explore Bayesian computation and model selection.

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Regression models generated by gamma random variables with long-term survivors

  • Ortega, Edwin M.M.;Cordeiro, Gauss M.;Hashimoto, Elizabeth M.;Suzuki, Adriano K.
    • Communications for Statistical Applications and Methods
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    • v.24 no.1
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    • pp.43-65
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    • 2017
  • We propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest has the Poisson distribution and the time for the event follows the gamma-G family of distributions. The extended family of gamma-G failure-time models with long-term survivors is flexible enough to include many commonly used failure-time distributions as special cases. We consider a frequentist analysis for parameter estimation and derive appropriate matrices to assess local influence on the parameters. Further, various simulations are performed for different parameter settings, sample sizes and censoring percentages. We illustrate the performance of the proposed regression model by means of a data set from the medical area (gastric cancer).

Empirical Bayes Estimate for Mixed Model with Time Effect

  • Kim, Yong-Chul
    • Communications for Statistical Applications and Methods
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    • v.9 no.2
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    • pp.515-520
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    • 2002
  • In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

A Study on Optimal Release Time for Software Systems based on Generalized Gamma Distribution (일반화 감마분포에 근거한 소프트웨어 최적방출시기에 관한 비교 연구)

  • Kim, Jae-Wook;Kim, Hee-Cheul
    • Journal of the Korea Society of Digital Industry and Information Management
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    • v.6 no.1
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    • pp.55-67
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    • 2010
  • Decision problem called an optimal release policies, after testing a software system in development phase and transfer it to the user, is studied. The applied model of release time exploited infinite non-homogeneous Poisson process. This infinite non-homogeneous Poisson process is a model which reflects the possibility of introducing new faults when correcting or modifying the software. The failure life-cycle distribution used generalized gamma type distribution which has the efficient various property because of various shape and scale parameter. Thus, software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement becomes an optimal release policies. In a numerical example, after trend test applied and estimated the parameters using maximum likelihood estimation of inter-failure time data, estimated software optimal release time.

Accuracy Measures of Empirical Bayes Estimator for Mean Rates

  • Jeong, Kwang-Mo
    • Communications for Statistical Applications and Methods
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    • v.17 no.6
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    • pp.845-852
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    • 2010
  • The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.