• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.211-219
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• 2015

Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.815-825
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• 2016

Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.184-191
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• 2015

Prostate ultrasound is used to diagnose prostate cancer, BPH, prostatitis and biopsy of prostate cancer to determine the size of prostate. BPH is one of the common disease in elderly men. Prostate is divided into 4 blocks, peripheral zone, central zone, transition zone, anterior fibromuscular stroma. BPH is histologically transition zone urethra accompanying excessive nodular hyperplasia causes a lower urinary tract symptoms(LUTS) caused by urethral closure as causing the hyperplastic nodule characterized finding progressive ambient. Therefore, in this study normal transition zone image for hyperplasia prostate and normal transition zone image is analyzed quantitatively using a computer algorithm. We applied texture features of GLCM to set normal tissue 60 cases and BPH tissue 60cases setting analysis area $50{\times}50pixels$ which was analyzed by comparing the six parameters for each partial image. Consequently, Disease recognition detection efficiency of Autocorrelation, Cluster prominence, entropy, Sum average, parameter were high as 92~98%.This could be confirmed by quantitative image analysis to nodular hyperplasia change transition zone of the prostate. This is expected secondary means to diagnose BPH and the data base will be considered in various prostate examination.