• Title/Summary/Keyword: 베이지안 자기 포아송 모형

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Small Area Estimation Using Bayesian Auto Poisson Model with Spatial Statistics (공간통계량을 활용한 베이지안 자기 포아송 모형을 이용한 소지역 통계)

  • Lee, Sang-Eun
    • The Korean Journal of Applied Statistics
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    • v.19 no.3
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    • pp.421-430
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    • 2006
  • In sample survey sample designs are performed by geographically-based domain such as countries, states and metropolitan areas. However mostly statistics of interests are smaller domain than sample designed domain. Then sample sizes are typically small or even zero within the domain of interest. Shin and Lee(2003) mentioned Spatial Autoregressive(SAR) model in small area estimation model-based method and show the effectiveness by MSE. In this study, Bayesian Auto-Poisson Model is applied in model-based small area estimation method and compare the results with SAR model using MSE ME and bias check diagnosis using regression line. In this paper Survey of Disability, Aging and Cares(SDAC) data are used for simulation studies.

A Bayesian Poisson model for analyzing adverse drug reaction in self-controlled case series studies (베이지안 포아송 모형을 적용한 자기-대조 환자군 연구에서의 약물상호작용 위험도 분석)

  • Lee, Eunchae;Hwang, Beom Seuk
    • The Korean Journal of Applied Statistics
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    • v.33 no.2
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    • pp.203-213
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    • 2020
  • The self-controlled case series (SCCS) study measures the relative risk of exposure to exposure period by setting the non-exposure period of the patient as the control period without a separate control group. This method minimizes the bias that occurs when selecting a control group and is often used to measure the risk of adverse events after taking a drug. This study used SCCS to examine the increased risk of side effects when two or more drugs are used in combination. A conditional Poisson model is assumed and analyzed for drug interaction between the narcotic analgesic, tramadol and multi-frequency combination drugs. Bayesian inference is used to solve the overfitting problem of MLE and the normal or Laplace prior distributions are used to measure the sensitivity of the prior distribution.

Hurdle Model for Longitudinal Zero-Inflated Count Data Analysis (영과잉 경시적 가산자료 분석을 위한 허들모형)

  • Jin, Iktae;Lee, Keunbaik
    • The Korean Journal of Applied Statistics
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    • v.27 no.6
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    • pp.923-932
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    • 2014
  • The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

Analysis of Total Crime Count Data Based on Spatial Association Structure (공간적 연관구조를 고려한 총범죄 자료 분석)

  • Choi, Jung-Soon;Park, Man-Sik;Won, Yu-Bok;Kim, Hag-Yeol;Heo, Tae-Young
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.335-344
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    • 2010
  • Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.