• Title/Summary/Keyword: 이항모형

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일반혼합이항모형에서 평가일치도의 로버스트 추정

  • 엄종석
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.74-84
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    • 1995
  • 혼합이항모형은 생물학, 혹은 심리학분야에서 많이 다루는 모형이다. 이 혼합모형에서 진단자간의 일치도를 나타내는 k 는 이항모형에 혼합되어지는 사전분포 $\xi$(p)에 따라 다른 형태를 갖는다. 그래서 $\xi$(p)에 의존적이지 않은 모수를 정의 하고, 이에 대한 실증적 추정값 $\hat k$을 일반혼합이항모형에서 k에 대한 추정값으로 사용하였다. 매개모수의 영향을 줄이기 위하여 모수를 직교화하였다. 베타이항모형으로 부터 표본을 추출하여 구한 최우추정값 $\hat k_m$과 이 표본을 이용하여 구한 $\hat k$을 비교하여 본 결과 k와 $\lambda$가 직교하는 영역에서 $\hat k$$\hat k_m$보다 편기가 작아지는 경우가 있을 만큼 $\hat k$이 효과적이었다.

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Comparative Simulation Studies on Generalized Binomial Models (일반화 이항모형의 적합도 평가)

  • Baik, E.J.;Kim, K.Y.
    • Communications for Statistical Applications and Methods
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    • v.18 no.4
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    • pp.507-516
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    • 2011
  • Comparative studies on generalized binomial models (Moon, 2003; Ng, 1989; Paul, 1985; Kupper and Haseman, 1978; Griffiths, 1973) are restrictive in that the models compared are rather limited and MSE of the estimates is the only measure considered for the model adequacy. This paper is aimed to report simulation results which provide possible guidelines for selecting a proper model. We examine Pearson type of goodness-of-fit statistic to its degrees of freedom and AIC for the overall model quality. MSE and Bias of the individual estimates are also considered as the component fit measures. Performance of some models varies widely for a certain range of the parameter space while most of the models are quite competent. Our evaluation shows that the Extended Beta-Binomial model (Prentice, 1986) turns out to be particularly favorable in the point that it provides consistently excellent fit almost all over the values of the intra-class correlation coefficient and the probability of success.

Fitting Distribution of Accident Frequency of Freeway Horizontal Curve Sections & Development of Negative Binomial Regression Models (고속도로 평면선형상 사고빈도분포 추정을 통한 음이항회귀모형 개발 (기하구조요인을 중심으로))

  • 강민욱;도철웅;손봉수
    • Journal of Korean Society of Transportation
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    • v.20 no.7
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    • pp.197-204
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    • 2002
  • 교통사고예측 및 예방을 위해서는 실제적으로 도로설계과정에서 제어가 가능한 도로 기하구조요소에 대한 사고관계를 파악함이 타당하다. 즉, 도로의 설계자는 도로건설에 앞서 기하구조요소와 사고와의 관계를 현장자료를 통해 정확히 밝혀 도로설계에 반영해야 한다. 이를 위해, 교통사고의 빈도분포를 박히는 것은 가장 기본이 되는 일이며, 교통사고 예측모형개발에 선행되어야 한다. 일반적으로 교통사고건수의 경우 분산이 평균보다 큰 과분산(overdispersion)의 특징을 가지고 있어 음이항 분포를 따른다고 알려져 있다. 따라서 본 논문은 사고모형의 개발에 앞서, 사고발생지점에 대한 도로설계요소와 기타 잠재적인 사고발생 관련요인이 비교적 잘 파악되어있는 호남고속도로를 중심으로 평면 선형상 곡선부에 대하여 교통사고의 분포를 적합도 검정을 통해 알아보고자 하였다. 사고자료는 한국도로송사의 호남고속도로 5년(1996∼2000)간 자료를 분석에 맞게 정리하였으며, 강민욱과 송봉수(2002)에서 제시한 평면선형에 있어서의 구간분할법을 이용하여 배향곡선구간과 단일곡선구간에 대한 사고분석을 하였다. 적합도 분석결과, 예상대로 음이항분포가 사고건수를 설명하기에 가장 적합한 확률분포로 제시되었으며, 이를 통해 최우추정법을 이용한 음이항회귀모형을 개발하였다. 구간분할법을 적용한 음이항회귀모형의 경우, 기존의 확률회귀토형에 비하여 높은 결정계수를 갖았으며, 모형에서 적용된 기하구조요소로는 차량 노출계수, 곡선반경, 단위거리 당 편경사변화값 등이다.

Fitting Bivariate Generalized Binomial Models of the Sarmanov Type (Sarmanov형 이변량 일반화이항모형의 적합)

  • Lee, Joo-Yong;Kim, Kee-Young
    • The Korean Journal of Applied Statistics
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    • v.22 no.2
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    • pp.271-280
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    • 2009
  • For bivariate binomial data with both intra and inter-class correlation, Danaher and Hardie (2005) proposed a bivariate beta-binomial model. However, the model is limited to the situation where the intra-class correlation is strictly positive. Thus it might be seriously inadequate for data with a negative intra-class correlation. Several authors have considered generalized binomial distributions covering a wider range of intra-class correlation which could relax the possible model restrictions imposed. Among others there are the additive/multiplicative and the beta/extended beta binomial model. In this study, bivariate models of the Sarmanov (1966) type are formed by combining each of those univariate models to take care of the inter-class correlation, and are evaluated in terms of the goodness-of-fit. As a result, B-mB and B-ebB are fitted, successfully, to real data and that B-mB, which has a wider permissible range than B-ebB for the intra-class correlation is relatively preferred.

Comparison of Estimators of Dependence Related Parameter in Generalized Binomial Distribution (일반화 이항분포모형에서 시행간 종속성 규정모수의 추정량 비교 연구)

  • Moon, Myung-Sang
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.279-288
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    • 1999
  • In many cases where the conventional binomial distribution fails to apply to real world data, it is mainly due to the lack of independence among Bernoulli trials. Several authors have proposed models that are useful when independence assumption is not satisfied. In this paper, one proposed model is adapted, and estimators of dependence related parameter that is crucial in defining that model are considered. Simulation is performed to compare two estimators(method of moment estimator and maximum likelihood estimator) of dependence related parameter, and conclusions are made.

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Bivariate Zero-Inflated Negative Binomial Regression Model with Heterogeneous Dispersions (서로 다른 산포를 허용하는 이변량 영과잉 음이항 회귀모형)

  • Kim, Dong-Seok;Jeong, Seul-Gi;Lee, Dong-Hee
    • Communications for Statistical Applications and Methods
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    • v.18 no.5
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    • pp.571-579
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    • 2011
  • We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.

Analyzing financial time series data using the GARCH model (일반 자기회귀 이분산 모형을 이용한 시계열 자료 분석)

  • Kim, Sahm;Kim, Jin-A
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.3
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    • pp.475-483
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    • 2009
  • In this paper we introduced a class of nonlinear time series models to analyse KOSPI data. We introduce the Generalized Power-Transformation TGARCH (GPT-TGARCH) model and the model includes Zakoian (1993) and Li and Li (1996) models as the special cases. We showed the effectiveness and efficiency of the new model based on KOSPI data.

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Fit of the number of insurance solicitor's turnovers using zero-inflated negative binomial regression (영과잉 음이항회귀 모형을 이용한 보험설계사들의 이직횟수 적합)

  • Chun, Heuiju
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.5
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    • pp.1087-1097
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    • 2017
  • This study aims to find the best model to fit the number of insurance solicitor's turnovers of life insurance companies using count data regression models such as poisson regression, negative binomial regression, zero-inflated poisson regression, or zero-inflated negative binomial regression. Out of the four models, zero-inflated negative binomial model has been selected based on AIC and SBC criteria, which is due to over-dispersion and high proportion of zero-counts. The significant factors to affect insurance solicitor's turnover found to be a work period in current company, a total work period as financial planner, an affiliated corporation, and channel management satisfaction. We also have found that as the job satisfaction or the channel management satisfaction gets lower as channel management satisfaction, the number of insurance solicitor's turnovers increases. In addition, the total work period as financial planner has positive relationship with the number of insurance solicitor's turnovers, but the work period in current company has negative relationship with it.

Accident Models of Rotary by Vehicle Type (차량유형별 로터리 사고모형)

  • Han, Su-San;Park, Byeong-Ho
    • Journal of Korean Society of Transportation
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    • v.29 no.6
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    • pp.67-74
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    • 2011
  • This study deals with the traffic accidents data from the Korean rotaries (circular intersections) to verify their characteristics affected by different vehicle types. This paper categorized the data into three groups based on vehicle types, and developed a set of accident models. The paper proposed two ZIP models and one negative binomial model through a statistical analysis for three vehicle types: automobile, truck and van, and others. The differences among those models were then statistically compared.

A Bayesian zero-inflated negative binomial regression model based on Pólya-Gamma latent variables with an application to pharmaceutical data (폴랴-감마 잠재변수에 기반한 베이지안 영과잉 음이항 회귀모형: 약학 자료에의 응용)

  • Seo, Gi Tae;Hwang, Beom Seuk
    • The Korean Journal of Applied Statistics
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    • v.35 no.2
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    • pp.311-325
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    • 2022
  • For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.