• Tribology and Lubricants
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• 제16권6호
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• pp.448-454
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• 2000

The roughness effects are very important due to the presence of interacting asperities in partial lubrication regime. An average Reynolds equation using flow factors is very useful to determine the effects of surface roughness on mixed lubrication. In this paper, the pressure flow factors for surfaces having Gaussian and non-Gaussian distribution of roughness height are evaluated in terms of various kurtosis. The effect of kurtosis on pressure flow factors is investigated using random rough surface generated numerically. The pressure flow factor increases with increasing kurtosis in mixed lubrication regime (h/$\sigma$<3). As h/$\sigma$ increases, the pressure flow factors approach to 1 asymptotically regardless of kurtosis.

• 응용통계연구
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• 제15권2호
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• pp.405-414
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• 2002

반복이 같은 이원배치 혼합효과 분산분석모형에서 무정보 사전분포를 이용하여 오차분산을 추정하는 문제를 생각하고자 한다. 먼저 무정보 사전분포로 제프리스사전분포, 준거 사전분포 그리고 확률일치 사전분포를 유도하고 이들 각각의 사전분포들에 대하여 주변사후분포를 제시하였다. 끝으로 실제 자료를 근거로 오차분산의 주변사후밀도함수에 대한 그래프와 오차분산에 대한 신용구간들을 구하고 이 구간들을 비교한다.

• KSTLE International Journal
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• 제1권2호
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• pp.83-90
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• 2000

The surface roughness between a piston ring pack and a cylinder liner directly affects the fuel economy, the oil consumption, and the emission of the engine so that it is very important to clarify the surface roughness effects on the lubrication characteristics. The friction characteristics of the piston ring during engine operations are known to as mixed lubrication experimentally. In this study to simulate the effects of the surface roughness of the piston ring pack on the lubrication characteristics, the mixed lubrication analysis of piston rings was performed using the simplified average Reynolds equation. From the results the surface roughness was found be considerably affects minimum oil film thickness as well as FMEP(Friction Mean Effective Pressure). Especially, the oil ring was the most sensitive on the surface roughness.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.301-311
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• 2020

Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.523-533
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• 2020

This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.199-205
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• 1999

본 논문은 초과변동을 갖는 이항자료의 효과적인 분석을 위하여 자료수집 과정에서 반응에 영향을 미치는 요인들을 고려한 혼합효과 모형을 제시하고 이 탈도(deviance)를 이용하여 그 타당성을 논의하고 있다.

• Journal of Periodontal and Implant Science
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• 제45권1호
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• pp.2-7
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• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2001년도 추계학술대회
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• pp.25-33
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• 2001

본 논문은 개체의 반응에 영향을 미치는 독립변수들중 일부는 고정요인들이고 일부는 확률요인들로 간주되며 반응연수가 다가범주를 갖는 명목형 변수일때, 다원분류표에서 자료를 분석하기 위한 모형으로 혼합효과 모형을 제시하고 모형내 미지모수들을 추정하는 방법을 다루고 있다.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.969-976
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• 2008

Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.