• ETRI Journal
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• v.32 no.5
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• pp.687-694
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• 2010

Breaks in presence (BIP) are those moments during virtual environment (VE) exposure in which participants become aware of their real world setting and their sense of presence in the VE becomes disrupted. In this study, we investigate participants' experience when they encounter technical anomalies during game play. We induced four technical anomalies and compared the BIP responses of a navigation mode game to that of a combat mode game. In our analysis, we applied a linear mixed model (LMM) and compared the results with those of a conventional regression model. Results indicate that participants felt varied levels of impact and recovery when experiencing the various technical anomalies. The impact of BIPs was clearly affected by the game mode, whereas recovery appears to be independent of game mode. The results obtained using the LMM did not differ significantly from those obtained using the general regression model; however, it was shown that treatment effects could be improved by consideration of random effects in the regression model.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.459-471
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• 2002

In applications using a linear regression model with nested error structure, one might be interested in making inferences concerning variance components. This article proposes approximate confidence intervals on the variance component of the primary level in a simple linear regression model with an unbalanced nested error structure. The intervals are compared using computer simulation and recommendations are provided for selecting an appropriate interval.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.361-370
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• 2003

Those who are interested in making inferences concerning linear combination of valiance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods. The methods are applied to a numerical example and recommendations are given for choosing a proper interval.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.75-78
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• 2003

Those who are interested in making inferences concerning linear combination of variance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.141-146
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• 2002

In this article we consider the problem of constructing confidence intervals for a linear regression model with nested error structure. A popular approach is the likelihood-based method employed by PROC MIXED of SAS. In this paper, we examine the ability of MIXED to produce confidence intervals that maintain the stated confidence coefficient. Our results suggest the intervals for the regression coefficients work well, but the intervals for the variance component associated with the primary level cannot be recommended. Accordingly, we propose alternative methods for constructing confidence intervals on the primary level variance component. Computer simulation is used to compare the proposed methods. A numerical example and SAS code are provided to demonstrate the methods.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1231-1239
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• 2012

An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Child Studies in Asia-Pacific Contexts
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• v.5 no.1
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• pp.51-62
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• 2015

This study examined the role of ethnicity and extended family relationship in the longitudinal patterns of unmarried resident fathers' engagement with young children in the U.S. Using three waves of panel data from the Fragile Families and Child Wellbeing Study (FFCWS), data obtained from 292 respondents were analyzed by the Linear Mixed Effects Regression (LMER) method. Findings suggested that Black unmarried resident fathers exhibited more engagement with their infants at age one than their White counterparts did, but the initial difference reversed by age five as a result of decreasing engagement among Black fathers over time and comparatively increasing engagement among Whites. Results also suggested that Black unmarried resident fathers with a high relationship level with their extended family members exhibited less engagement with their infants than Black fathers with a low relationship level, but the initial difference reversed by age five as a result of decreasing engagement among Black fathers with a low relationship level over time and comparatively increasing engagement among Black fathers with a high relationship level.