• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.173-191
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• 2017

This study develops a new type of latent class analysis (LCA) in order to explain the associations between one latent variable and several other categorical latent variables. Our model postulates that the prevalence of the latent variable of interest is affected by another latent variable composed of other several latent variables. For the parameter estimation, we propose deterministic annealing EM (DAEM) to deal with local maxima problem in the proposed model. We perform simulation study to demonstrate how DAEM can find the set of parameter estimates at the global maximum of the likelihood over the repeated samples. We apply the proposed LCA model in an investigation of the effect of and joint patterns for drug-using behavior to violent behavior among US high school male students using data from the Youth Risk Behavior Surveillance System 2015. Considering the age of male adolescents as a covariate influencing violent behavior, we identified three classes of violent behavior and three classes of drug-using behavior. We also discovered that the prevalence of violent behavior is affected by the type of drug used for drug-using behavior.

• Journal of Animal Science and Technology
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• v.48 no.1
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• pp.39-48
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• 2006

This study utilized test day of somatic cell score data of dairy cattle from 2000 to 2004. The number of data used were 124,635 of first parity, 134,308 of second parity, 77,862 of third parity, 41,787 of forth parity and 37,412 of fifth parity. The data was analyzed by least square mean method using GLM to estimate the effects of calving year, age, lactation stage, parity and season on somatic cell score. Variance component estimation using test day model was determined by using expectation maximization algorithm- restricted maximum likelihood (EM-REML) analysis method. In each parity, somatic cell score was low for younger group and was relatively high in older groups. Likewise, for lactation stage, the score was low in early-lactation and high in late-lactation in first parity and second parity. Nevertheless, for the third, fourth and fifth parity, however, high somatic cell score was observed in mid-lactation. Generally, the score was high in the peak. Although in fourth and fifth parity, the score was low in late-lactation. Environmental effect of season, somatic cell score was generally low from September to November for all parities. The score was high between June and August when the milk production is usually low. The heritability in each parity were 0.05, 0.09, 0.10, 0.05 and 0.05 for parity 1, 2, 3, 4, 5, respectively. Genetic variance value was estimated to be high in second, third and fifth parity in early-lactation and to be low in first and forth parity.

• Toxicological Research
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• v.30 no.1
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• pp.7-11
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• 2014

Malondialdehyde (MDA), used as an oxidative stress marker, is commonly assayed by measuring the thiobarbituric acid reactive substances (TBARS) using HPLC, as an indicator of the MDA concentration. Since the HPLC method, though highly specific, is time-consuming and expensive, usually it is not suitable for the rapid test in large-scale environmental epidemiologic surveys. The purpose of this study is to develop a simple and rapid method for estimating TBARS levels by using a multiple regression equation that includes TBARS levels measured with a microplate reader as an independent variable. Twelve hour urine samples were obtained from 715 subjects. The concentration of TBARS was measured at three different wavelengths (fluorescence: ${\lambda}-_{ex}$ 530 nm and ${\lambda}-_{em}$ 550 nm; ${\lambda}-_{ex}$ 515 nm and ${\lambda}-_{em}$ 553 nm; and absorbance: 532 nm) using microplate reader as well as HPLC. 500 samples were used to develop a regression equation, and the remaining 215 samples were used to evaluate the validity of the regression analysis. The induced multiple regression equation is as follows: TBARS level (${\mu}M$) = -0.282 + 1.830 ${\times}$ (TBARS level measured with a microplate reader at the fluorescence wavelengths ${\lambda}-_{ex}$ 530 nm and ${\lambda}-_{em}$ 550 nm, ${\mu}M$) -0.685 ${\times}$ (TBARS level measured with a microplate reader at the fluorescence wavelengths ${\lambda}-_{ex}$ 515 nm and ${\lambda}-_{em}$ 553 nm, ${\mu}M$) + 0.035 ${\times}$ (TBARS level measured with a microplate reader at the absorbance wavelength 532 nm, ${\mu}M$). The estimated TBARS levels showed a better correlation with, and are closer to, the corresponding TBARS levels measured by HPLC compared to the values obtained by the microplate method. The TBARS estimation method reported here is simple and rapid, and that is generally in concordance with HPLC measurements. This method might be a useful tool for monitoring of urinary TBARS level in environmental epidemiologic surveys with large sample sizes.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1309-1317
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• 2013

There are various parameter estimation methods for the generalized exponential distribution under progressive type I interval censoring. Chen and Lio (2010) studied the parameter estimation method by the maximum likelihood estimation method, mid-point approximation method, expectation maximization algorithm and methods of moments. Among those, mid-point approximation method has the smallest mean square error in the generalized exponential distribution under progressive type I interval censoring. However, this method is difficult to derive closed form of solution for the parameter estimation using by maximum likelihood estimation method. In this paper, we propose two type of approximate maximum likelihood estimate to solve that problem. The simulation results show the obtained estimators have good performance in the sense of the mean square error. And proposed method derive closed form of solution for the parameter estimation from the generalized exponential distribution under progressive type I interval censoring.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.421-431
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• 2023

Sewer deterioration models can offer important information on prediction of future condition of the asset to decision makers in their implementing sewer pipe networks management program. In this study, Markov chain model was used to estimate sewer deterioration trend based on the historical structural condition assessment data obtained by CCTV inspection. The data used in this study were limited to Hume pipe with diameter of 450 mm and 600 mm in three sub-catchment areas in city A, which were collected by CCTV inspection projects performed in 1998-1999 and 2010-2011. As a result, it was found that sewers in sub-catchment area EM have deteriorated faster than those in other two sub-catchments. Various main defects were to generate in 29% of 450 mm sewers and 38% of 600 mm in 35 years after the installation, while serious failure in 62% of 450 mm sewers and 74% of 600 mm in 100 years after the installation in sub-catchment area EM. In sub-catchment area SN, main defects were to generate in 26% of 450 mm sewers and 35% of 600 mm in 35 years after the installation, while in sub-catchment area HK main defects were to generate in 27% of 450 mm sewers and 37% of 600 mm in 35 years after the installation. Larger sewer pipes of 600 mm were found to deteriorate faster than smaller sewer pipes of 450 mm by about 12 years. Assuming that the percentage of main defects generation could be set as 40% to estimate the life expectancy of the sewers, it was estimated as 60 years in sub-catchment area SN, 42 years in sub-catchment area EM, 59 years in sub-catchment area HK for 450 mm sewer pipes, respectively. For 600 mm sewer pipes, on the other hand, it was estimated as 43 years, 34 years, 39 years in sub-catchment areas SN, EM, and HK, respectively.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.715-723
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• 2002

This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.178-183
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• 2011

The deterministic annealing-based clustering algorithm is an EM-based algorithm which behaves like simulated annealing method, yet less sensitive to the initialization of parameters. Pairwise clustering is a kind of clustering technique to perform clustering with inter-entity distance information but not enforcing to have detailed attribute information. The pairwise deterministic annealing-based clustering algorithm repeatedly alternates the steps of estimation of mean-fields and the update of membership degrees of data objects to clusters until termination condition holds. Lacking of attribute value information, pairwise clustering algorithms do not explicitly determine the centroids or medoids of clusters in the course of clustering process or at the end of the process. This paper proposes a method to identify the medoids as the centers of formed clusters for the pairwise deterministic annealing-based clustering algorithm. Experimental results show that the proposed method locate meaningful medoids.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2005.11a
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• pp.143-148
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• 2005

The objective of this study is to estimate RF signal strength over sea and land surfaces. For this work we calculated scattering by land with DEM(Digital Elevation Model) and sea surface with RMS surface height. and we selected two area inland and sea shore as RX point. And for each area, we get VV-pol and HH-pol characteristic of scattering at 2.2GHz.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.171-174
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• 1998

본 논문에서는 음성합성(speech synthesis) 및 부호화(coding) 시스템에 있어서 음원(voice source) 모델링에 관한 문제를 살펴보고자 한다. 기존의 음원 모델링 시스템이 가지고 있는 여러 문제들을 극복하고자 기저함수(basis function) 의 가중 합(weighted-sum)으로 음원을 모델링 하는 새로운 기법을 제안하고자 한다. 제안한 방법에서는 음원 파형(voice source waveform)을 적절히 표현하기 위해서 필터뱅크(filter bank)에 기초한 기저함수의 가중 합으로 나타낸다. 다양한 음원 특성을 효과적으로 나타내는 음원 파라미터를 구하기 위하여 EM(estimate maximize)에 기초한 구조에 관해 조사한다. 제안한 방법을 이용하여 다양한 유성음에 대해 실험을 수행하였다. 실험결과 제안한 추정(estimation) 방법 및 모델링 방법을 이용하면 기존의 방법에 비해 더 정확한 음원 파형을 추정할 수 있고, 다양한 음원 특성을 나타낼 수 있다. 또한 음성합성 및 부호화에서도 음성품질(voice quality)를 개선시킬 수 있으리라 기대된다.