• 제목/요약/키워드: mixed-effects model

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A Cumulative Logit Mixed Model for Ordered Response Data

  • Choi, Jae-Sung
    • Journal of the Korean Data and Information Science Society
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    • 제17권1호
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    • pp.123-130
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    • 2006
  • This paper discusses about how to build up a mixed-effects model using cumulative logits when some factors are fixed and others are random. Location effects are considered as random effects by choosing them randomly from a population of locations. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

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로버스트 선형혼합모형을 이용한 필드시험 데이터 분석 (Analysis of Field Test Data using Robust Linear Mixed-Effects Model)

  • 홍은희;이영조;옥유진;나명환;노맹석;하일도
    • 응용통계연구
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    • 제28권2호
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    • pp.361-369
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    • 2015
  • 연속측도의 반응변수가 반복측정된 실험 자료의 분석을 위해 흔히 선형혼합모형이 사용된다. 그러나, 잔차의 분포가 이분산성이거나 비정규성을 가질 때 표준적인 선형혼합모형은 적절하지 않은 결과를 가져온다. 잔차의 분포가 두터운 꼬리를 가진 비정규분포를 보이는 타이어 필드시험 데이터를 로버스트 선형혼합모형에 적합시킴으로써 보다 더 정확하고 신뢰할 수 있는 분석결과를 얻을 수 있다. 추가적으로 신뢰성 분석 결과를 제시한다.

Mixed-effects LS-SVR for longitudinal dat

  • Cho, Dae-Hyeon
    • Journal of the Korean Data and Information Science Society
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    • 제21권2호
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    • pp.363-369
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    • 2010
  • In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

A Proportional Odds Mixed - Effects Model for Ordinal Data

  • Choi, Jae-Sung
    • Journal of the Korean Data and Information Science Society
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    • 제18권2호
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    • pp.471-479
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    • 2007
  • This paper discusses about how to build up mixed-effects model for analysing ordinal response data by using cumulative logits. Random factors are assumed to be coming from the designed sampling scheme for choosing observational units. Since the observed responses of individuals are ordinal, a proportional odds model with two random effects is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

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Small Area Estimation via Nonparametric Mixed Effects Model

  • Jeong, Seok-Oh;Shin, Key-Il
    • 응용통계연구
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    • 제25권3호
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    • pp.457-464
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    • 2012
  • Small area estimation is a statistical inference method to overcome the large variance due to the small sample size allocated in a small area. Recently some nonparametric estimators have been applied to small area estimation. In this study, we suggest a nonparametric mixed effect small area estimator using kernel smoothing and compare the small area estimators using labor statistics.

Credibility estimation via kernel mixed effects model

  • Shim, Joo-Yong;Kim, Tae-Yoon;Lee, Sang-Yeol;Hwa, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제20권2호
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    • pp.445-452
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    • 2009
  • Credibility models are actuarial tools to distribute premiums fairly among a heterogeneous group of policyholders. Many existing credibility models can be expressed as special cases of linear mixed effects models. In this paper we propose a nonlinear credibility regression model by reforming the linear mixed effects model through kernel machine. The proposed model can be seen as prediction method applicable in any setting where repeated measures are made for subjects with different risk levels. Experimental results are then presented which indicate the performance of the proposed estimating procedure.

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A Mixed-effects Height-Diameter Model for Pinus densiflora Trees in Gangwon Province, Korea

  • Lee, Young Jin;Coble, Dean W.;Pyo, Jung Kee;Kim, Sung Ho;Lee, Woo Kyun;Choi, Jung Kee
    • 한국산림과학회지
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    • 제98권2호
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    • pp.178-182
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    • 2009
  • A new mixed-effects model was developed that predicts individual-tree total height for Pinus densiflora trees in Gangwon province as a function of individual-tree diameter (cm). The mixed-effects model contains two random-effects parameters. Maximum likelihood estimation was used to fit the model to 560 height-diameter observations of individual trees measured throughout Gwangwon province in 2007 as part of the National Forest Inventory Program in Korea. The new model is an improvement over fixed-effects models because it can be calibrated to a local area, such as an inventory plot or individual stand. The new model also appears to be an improvement over the Forest Resources Evaluation and Prediction Program for the ten calibration trees used in this study. An example is provided that describes how to estimate the random-effects parameters using ten calibration trees.

다가자료에 대한 혼합효과모형 (A generalized logit model with mixed effects for categorical data)

  • 최재성
    • 응용통계연구
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    • 제15권1호
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    • pp.129-137
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    • 2002
  • 본 논문은 개체의 반응에 영향을 미치는 독립변수들중 일부는 고정요인들이고 일부는 확률요인들로 간주되며 반응변수가 다가범주를 갖는 명목형 변수일 때, 다원분류표에서 자료를 분석하기 위한 모형으로 혼합효과모형을 제시하고 모형내 미지모수들을 추정하는 방법을 다루고 있다

비모수와 준모수 혼합모형을 이용한 소지역 추정 (Semiparametric and Nonparametric Mixed Effects Models for Small Area Estimation)

  • 정석오;신기일
    • 응용통계연구
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    • 제26권1호
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    • pp.71-79
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    • 2013
  • 지역 또는 도메인에 작은 크기의 표본이 배정되어 추정의 정도가 나쁜 경우에 사용되는 준모수적 또는 비모수적 소지역 추정법은 최근 많은 연구가 진행되고 있다. 본 논문에서는 커널을 이용한 국소다항 혼합모형 소지역 추정법과 벌점 스플라인을 이용한 혼합모형 소지역 추정법이 연구되었다. 이 두 방법과 소지역추정에 흔히 사용되고 있는 선형 혼합모형을 모의실험을 통해 그 우수성을 비교하였다.

A marginal logit mixed-effects model for repeated binary response data

  • Choi, Jae-Sung
    • Journal of the Korean Data and Information Science Society
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    • 제19권2호
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    • pp.413-420
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    • 2008
  • This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.

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