• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.33-45
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• 2016

Variance components should be estimated based on mean change when the mean of the observations drift gradually over time. Consistent estimators for the variance components are studied for a particular modeling situation with some underlying functions or drift. We propose a new variance estimator with Fourier estimation of variations. The consistency of the proposed estimator is proved asymptotically. The proposed procedures are studied and compared empirically with the variance estimators removing trends. The result shows that our variance estimator has a smaller mean square error and depends on drift patterns. We estimate and apply the variance to Nile River flow data and resting state fMRI data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2110-2117
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• 2003

A physics-based material removal model in various scales is described and a feature scale simulation for a chemical mechanical polishing (CMP) process is performed in this work. Three different scales are considered in this model, i.e., abrasive particle scale, asperity scale and wafer scale. The abrasive particle and the asperity scales are combined together and then homogenized to result in force balance conditions to be satisfied in the wafer scale using an extended Greenwood-Williamson and Whitehouse-Archard statistical model that takes into consideration the joint distribution of asperity heights and asperity tip radii. The final computation is made to evaluate the material removal rate in wafer scale and a computer simulation is performed for detailed surface profile variations on a representative feature. The results show the dependence of the material removal rate on the joint distribution, applied external pressure, relative velocity, and other operating conditions and design parameters.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2355-2358
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• 2003

The purpose of this paper is to investigate two major feature extraction techniques based on generic modular face recognition system. Detailed algorithms are described for principal component analysis (PCA) and independent component analysis (ICA). PCA and ICA ate statistical techniques for feature extraction and their incorporation into a face recognition system requires numerous design decisions. We explicitly state the design decisions by introducing a modular-based face recognition system since some of these decision are not documented in the literature. We explored different implementations of each module, and evaluate the statistical feature extraction algorithms based on the FERET performance evaluation protocol (the de facto standard method for evaluating face recognition algorithms). In this paper, we perform two experiments. In the first experiment, we report performance results on the FERET database based on PCA. In the second experiment, we examine performance variations based on ICA feature extraction algorithm. The experimental results are reported using four different categories of image sets including front, lighting, and duplicate images.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.70-88
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• 2000

Short runs where it is neither possible nor practical to obtain sufficient subgroups to estimate accurately the control limit are common in modem business environments. In this study, the standardized control chart, Hillier's exact method, Q chart, EWMA(Exponentially Weighted Moving Average) chart for Q statistics and EWMA chart for mean and absolute deviation among many SPC(Statistical Process Control) techniques for short runs have been reviewed and advantages and disadvantages of these techniques are discussed. The simulation experiments to compare performances of these variable charts for process mean and variations are conducted for combination of subgroup size, scale and timing of shifts of process mean an/or standard deviation. Based upon simulation results, some guidelines for practitioners to choose short run SPC techniques are recommended.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.263-274
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• 2018

One of the common issues in large dataset analyses is to detect and construct homogeneous groups of objects in those datasets. This is typically done by some form of clustering technique. In this study, we present a divisive hierarchical clustering method for two monothetic characteristics of histogram data. Unlike classical data points, a histogram has internal variation of itself as well as location information. However, to find the optimal bipartition, existing divisive monothetic clustering methods for histogram data consider only location information as a monothetic characteristic and they cannot distinguish histograms with the same location but different internal variations. Thus, a divisive clustering method considering both location and internal variation of histograms is proposed in this study. The method has an advantage in interpreting clustering outcomes by providing binary questions for each split. The proposed clustering method is verified through a simulation study and applied to a large U.S. house property value dataset.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.235-240
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• 2013

Generalized linear mixed models(GLMMs) are frequently used for the analysis of longitudinal categorical data when the subject-specific effects is of interest. In GLMMs, the structure of the random effects covariance matrix is important for the estimation of fixed effects and to explain subject and time variations. The estimation of the matrix is not simple because of the high dimension and the positive definiteness; subsequently, we practically use the simple structure of the covariance matrix such as AR(1). However, this strong assumption can result in biased estimates of the fixed effects. In this paper, we introduce Bayesian modeling approaches for the random effects covariance matrix using a modified Cholesky decomposition. The modified Cholesky decomposition approach has been used to explain a heterogenous random effects covariance matrix and the subsequent estimated covariance matrix will be positive definite. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using these methods.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.285-295
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• 2004

Statistical process control(SPC) and engineering process control(EPC) are two complementary strategies for quality improvement. An integrated process control(IPC) can use EPC to reduce the effect of predictable quality variations and SPC to monitor the process for detection of special causes. In this paper we assume an IMA(1,1) model as a disturbance process and an occurrence of a level shift in the process, and we consider the economic performance for applying an EWMA chart to monitor MMSE-controlled processes. The numerical results suggest that the IPC scheme in an IMA(1,1) disturbance model does not give additional advantages in the economic aspect.

• Genomics & Informatics
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• v.9 no.1
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• pp.28-36
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• 2011

Most statistical methods for finding interesting genes are focusing on the summary values with large fold-changes or large variations. Very few methods consider the probe level data. We developed a new measure to detect reliability that incorporates the probe level data. This reliability measure is useful for exploring the microarray data without ignoring the probe level data. It is easy to calculate, and it can be used for all the other statistical methods as a good guideline to find real differentially expressed genes. Instead of filtering out genes before the analysis, we use whole genes in the analysis and make decisions with new reliability measures.