• 제목/요약/키워드: Mixed Methods

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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.

기존도시철도의 CBTC도입에 따른 병행운전방안 도출연구 (The Study for Mixed Operation of CBTC Train and Non-CBTC Ones)

  • 김유호;이수환;유종천;김종기;백종현
    • 한국철도학회:학술대회논문집
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    • 한국철도학회 2003년도 추계학술대회 논문집(III)
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    • pp.151-156
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    • 2003
  • The Communication Based Train Control(CBTC) System generally used to improving existing lines in the world is efficient and stable for improvement. Also it is efficient for improvement because it does not cause any problems in operation of existing lines. We examined the mixed operation scheme for more efficient and stable improvement when improvement is implemented depending on aged equipment and the increased number of passengers in the Korean city railroad. The biggest point in the mixed operation is to implement a stable test without interfering with the operation of the existing lines. For this purpose, we deduced a method of installing the equipment, a method of testing depending on the installed equipment, and the types of mixed operation, and presented a scheme of execution of the methods depending on the mixed operation.

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질적-양적 연구방법론의 혼합에 의한 의료사회복지사의 소진탄력성 및 소진위험성 척도개발 연구 (Creating and Validating Scale of Resilience to Burnout and Scale of Burnout Risk with Mixed Methods)

  • 최명민
    • 한국사회복지학
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    • 제59권4호
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    • pp.245-272
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    • 2007
  • 본 연구는 혼합방법론의 탐색설계 중 척도개발모델에 의거하여 소진탄력성척도와 소진위험성척도를 개발하고 그 과정에서 산출된 자료들의 의미를 탐색하기 위한 것이다. 이를 위하여 척도개발모델 1단계에서는 선행 질적연구를 통해 도출한 의료사회복지사의 소진 보호 및 위험요인에 기반을 두고 각 척도의 내용을 구성하였고, 2단계 양적 연구 단계에서는 의료사회복지사 185명의 조사자료를 분석하여 척도들의 구성타당도와 신뢰도를 검증하였다. 확인적 요인분석 결과 6요인 31문항의 소진탄력성척도와 6요인 27문항의 소진위험성척도의 구성타당도가 검증되었으며, 두 척도의 신뢰도는 소진탄력성 0.92, 소진위험성 0.90으로 나타났다. 더불어 각 척도 및 하위요인들 간의 상관관계 분석을 통해 소진탄력성과 소진위험성의 속성 및 관계에 대한 분석을 제시하였고, 인구사회학적 변인들에 의한 조사 결과도 살펴보았다. 이와 같은 혼합방법론에 의한 척도개발과정을 통해 현장의 목소리를 최대한 반영하면서 수량적으로도 타당도와 신뢰도가 검증된 척도를 개발할 수 있었다.

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Analysis of quantitative high throughput screening data using a robust method for nonlinear mixed effects models

  • Park, Chorong;Lee, Jongga;Lim, Changwon
    • Communications for Statistical Applications and Methods
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    • 제27권6호
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    • pp.701-714
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    • 2020
  • Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

A Survey of Satisfaction of Physical Therapy Course according to Teaching Ways after COVID-19

  • Lee, Han Do;Lee, Ji Hong;Kwon, Hyeok Gyu
    • The Journal of Korean Physical Therapy
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    • 제34권4호
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    • pp.135-139
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    • 2022
  • Purpose: We investigated the satisfaction of physical therapy course according to teaching ways after COVID-19. Methods: 336 students in major of physical therapy were recruited in this study. Based on the classification of subjects in the national examination, the questionnaire was divided into 6 subjects in the basic field of physical therapy, 2 subjects in the field of physical therapy diagnostic evaluation, 8 subjects in the field of physical therapy intervention, and 3 subjects in other fields. The Likert scale was used. Results: In the basic field of physical therapy, all subjects were shown the high score of the satisfactory in face-to-face classes except for the public health and medical law compared to the non-face-to-face classes and mixed classes. Regarding the field of physical therapy diagnostic evaluation, the principle of diagnostic evaluation was shown the high score of the satisfactory in face-to-face classes compared to the non-face-to-face classes and mixed classes. In the field of physical therapy intervention, all subjects were shown the high score of the satisfactory in face-to-face classes compared to the non-face-to-face classes and mixed classes. Conclusion: We found that the face-to-face classes in most of subjects was shown the high score of satisfactory. We believed that our results can be used as basic data for physical therapy major learning methods.

Bayesian modeling of random effects precision/covariance matrix in cumulative logit random effects models

  • Kim, Jiyeong;Sohn, Insuk;Lee, Keunbaik
    • Communications for Statistical Applications and Methods
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    • 제24권1호
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    • pp.81-96
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    • 2017
  • Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

Second-Order REML for Random Effects Models

  • 하일도;조건호
    • Journal of the Korean Data and Information Science Society
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    • 제12권1호
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    • pp.19-25
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    • 2001
  • Random effects models which describe the dependence via random effects in various correlated data have recently received considerable attention in the biomedical literature. They include mixed linear models (MLMs), generatized linear mixed models (GLMMS) and hierarchical generalized linear models (HGLMs). For the inference Lee and Nelder (2000) proposed the first-and second-order REML (restricted maximum likelihood) methods based on hierarchical-likelihood of tee and Welder (1996). In this paper, for Poisson-gamma HGLMs the new methods are theoretically compared with marginal likelihood methods and both methods are illustrated by two practical examples.

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A comparison of imputation methods using machine learning models

  • Heajung Suh;Jongwoo Song
    • Communications for Statistical Applications and Methods
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    • 제30권3호
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    • pp.331-341
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    • 2023
  • Handling missing values in data analysis is essential in constructing a good prediction model. The easiest way to handle missing values is to use complete case data, but this can lead to information loss within the data and invalid conclusions in data analysis. Imputation is a technique that replaces missing data with alternative values obtained from information in a dataset. Conventional imputation methods include K-nearest-neighbor imputation and multiple imputations. Recent methods include missForest, missRanger, and mixgb ,all which use machine learning algorithms. This paper compares the imputation techniques for datasets with mixed datatypes in various situations, such as data size, missing ratios, and missing mechanisms. To evaluate the performance of each method in mixed datasets, we propose a new imputation performance measure (IPM) that is a unified measurement applicable to numerical and categorical variables. We believe this metric can help find the best imputation method. Finally, we summarize the comparison results with imputation performances and computational times.

자이로의 불규칙 혼합잡음을 고려한 보조항법시스템 칼만 필터 설계 (Kalman Filter Design For Aided INS Considering Gyroscope Mixed Random Errors)

  • 성상만;강기호
    • 한국항공우주학회지
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    • 제34권4호
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    • pp.47-52
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    • 2006
  • 불규칙 혼합잡음의 등가 ARMA 모델 표현을 사용하여 자이로의 불규칙 혼합잡음을 고려하는 보조항법시스템 칼만필터 설계 방법을 제안한다. 필터 설계 절차는 먼저 보조항법 시스템에 사용되는 필터는 간접 되먹임 칼만필터임을 고려하여 등가 ARMA 모델로 표현된 자이로 불규칙 잡음의 시간 차분을 구한다. 다음으로 시간 차분된 ARMA 모델을 상태 방정식으로 표현하는데 AR과 MA 차수에 따라 두 가지로 나누어진다. 먼저 AR 차수가 큰 경우 가제어 혹은 가관측 특이형태를 사용한다. MA 차수가 큰 경우에는 몇 단계 이후의 예측치를 상태변수로 하는 상태방정식을 사용하는데, 이때 자이로 출력을 보상하는 값에 따라 다시 고차수 필터와 저차수 필터로 구분된다. 마지막으로 자이로 불규칙 잡음을 보조항법시스템 칼만필터에 포함시켜 최종적인 필터 모델을 얻는다. 시뮬레이션 결과를 통하여 제안된 고차수 및 저차수 필터 모두 혼합잡음을 백색잡음으로 간주한 기존의 필터보다 항법오차를 감소시킬 수 있음을 보임으로써 그 효용성을 제시한다.