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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 21, Issue 6 - Nov 2014
Volume 21, Issue 5 - Sep 2014
Volume 21, Issue 4 - Jul 2014
Volume 21, Issue 3 - May 2014
Volume 21, Issue 2 - Mar 2014
Volume 21, Issue 1 - Jan 2014
Selecting the target year
A Berry-Esseen Type Bound in Kernel Density Estimation for a Random Left-Truncation Model
Asghari, P. ; Fakoor, V. ; Sarmad, M. ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 115~124
DOI : 10.5351/CSAM.2014.21.2.115
In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum (Y) is randomly left truncated and is sampled if
, where T is the truncation random variable with an unknown distribution. This unknown distribution is estimated with the Lynden-Bell estimator. In particular the normal approximation rate, by choice of the bandwidth, is shown to be close to
modulo logarithmic term. We have also investigated this normal approximation rate via a simulation study.
Kullback-Leibler Information of the Equilibrium Distribution Function and its Application to Goodness of Fit Test
Park, Sangun ; Choi, Dongseok ; Jung, Sangah ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 125~134
DOI : 10.5351/CSAM.2014.21.2.125
Kullback-Leibler (KL) information is a measure of discrepancy between two probability density functions. However, several nonparametric density function estimators have been considered in estimating KL information because KL information is not well-defined on the empirical distribution function. In this paper, we consider the KL information of the equilibrium distribution function, which is well defined on the empirical distribution function (EDF), and propose an EDF-based goodness of fit test statistic. We evaluate the performance of the proposed test statistic for an exponential distribution with Monte Carlo simulation. We also extend the discussion to the censored case.
Bayesian Conjugate Analysis for Transition Probabilities of Non-Homogeneous Markov Chain: A Survey
Sung, Minje ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 135~145
DOI : 10.5351/CSAM.2014.21.2.135
The present study surveys Bayesian modeling structure for inferences about transition probabilities of Markov chain. The motivation of the study came from the data that shows transitional behaviors of emotionally disturbed children undergoing residential treatment program. Dirichlet distribution was used as prior for the multinomial distribution. The analysis with real data was implemented in WinBUGS programming environment. The performance of the model was compared to that of alternative approaches.
The Exponentiated Weibull-Geometric Distribution: Properties and Estimations
Chung, Younshik ; Kang, Yongbeen ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 147~160
DOI : 10.5351/CSAM.2014.21.2.147
In this paper, we introduce the exponentiated Weibull-geometric (EWG) distribution which generalizes two-parameter exponentiated Weibull (EW) distribution introduced by Mudholkar et al. (1995). This proposed distribution is obtained by compounding the exponentiated Weibull with geometric distribution. We derive its cumulative distribution function (CDF), hazard function and the density of the order statistics and calculate expressions for its moments and the moments of the order statistics. The hazard function of the EWG distribution can be decreasing, increasing or bathtub-shaped among others. Also, we give expressions for the Renyi and Shannon entropies. The maximum likelihood estimation is obtained by using EM-algorithm (Dempster et al., 1977; McLachlan and Krishnan, 1997). We can obtain the Bayesian estimation by using Gibbs sampler with Metropolis-Hastings algorithm. Also, we give application with real data set to show the flexibility of the EWG distribution. Finally, summary and discussion are mentioned.
Finding Cost-Effective Mixtures Robust to Noise Variables in Mixture-Process Experiments
Lim, Yong B. ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 161~168
DOI : 10.5351/CSAM.2014.21.2.161
In mixture experiments with process variables, we consider the case that some of process variables are either uncontrollable or hard to control, which are called noise variables. Given the such mixture experimental data with process variables, first we study how to search for candidate models. Good candidate models are screened by the sequential variables selection method and checking the residual plots for the validity of the model assumption. Two methods, which use numerical optimization methods proposed by Derringer and Suich (1980) and minimization of the weighted expected loss, are proposed to find a cost-effective robust optimal condition in which the performance of the mean as well as the variance of the response for each of the candidate models is well-behaved under the cost restriction of the mixture. The proposed methods are illustrated with the well known fish patties texture example described by Cornell (2002).
Autoregressive Cholesky Factor Modeling for Marginalized Random Effects Models
Lee, Keunbaik ; Sung, Sunah ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 169~181
DOI : 10.5351/CSAM.2014.21.2.169
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.
A Clarification of the Cauchy Distribution
Lee, Hwi-Young ; Park, Hyoung-Jin ; Kim, Hyoung-Moon ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 183~191
DOI : 10.5351/CSAM.2014.21.2.183
We define a multivariate Cauchy distribution using a probability density function; subsequently, a Ferguson's definition of a multivariate Cauchy distribution can be viewed as a characterization theorem using the characteristic function approach. To clarify this characterization theorem, we construct two dependent Cauchy random variables, but their sum is not Cauchy distributed. In doing so the proofs depend on the characteristic function, but we use the cumulative distribution function to obtain the exact density of their sum. The derivation methods are relatively straightforward and appropriate for graduate level statistics theory courses.
Bayesian Inference for Censored Panel Regression Model
Lee, Seung-Chun ; Choi, Byongsu ;
Communications for Statistical Applications and Methods, volume 21, issue 2, 2014, Pages 193~200
DOI : 10.5351/CSAM.2014.21.2.193
It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.