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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 14, Issue 3 - Dec 2007
Volume 14, Issue 2 - Aug 2007
Volume 14, Issue 1 - Apr 2007
Selecting the target year
Forecasting Probability of Precipitation Using Morkov Logistic Regression Model
Park, Jeong-Soo ; Kim, Yun-Seon ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 1~9
DOI : 10.5351/CKSS.2007.14.1.001
A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow`s precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.
Reference-Intrinstic Analysis for the Difference between Two Normal Means
Jang, Eun-Jin ; Kim, Dal-Ho ; Lee, Kyeong-Eun ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 11~21
DOI : 10.5351/CKSS.2007.14.1.011
In this paper, we consider a decision-theoretic oriented, objective Bayesian inference for the difference between two normal means with unknown com-mon variance. We derive the Bayesian reference criterion as well as the intrinsic estimator and the credible region which correspond to the intrinsic discrepancy loss and the reference prior. We illustrate our results using real data analysis as well as simulation study.
Penalized Likelihood Regression with Negative Binomial Data with Unknown Shape Parameter
Kim, Young-Ju ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 23~32
DOI : 10.5351/CKSS.2007.14.1.023
We consider penalized likelihood regression with data from the negative binomial distribution with unknown shape parameter. Smoothing parameter selection and asymptotically efficient low dimensional approximations are employed for negative binomial data along with shape parameter estimation through several different algorithms.
Bayesian Estimation of Shape Parameter of Pareto Income Distribution Using LINEX Loss Function
Saxena, Sharad ; Singh, Housila P. ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 33~55
DOI : 10.5351/CKSS.2007.14.1.033
The economic world is full of patterns, many of which exert a profound influence over society and business. One of the most contentious is the distribution of wealth. Way back in 1897, an Italian engineer-turned-economist named Vilfredo Pareto discovered a pattern in the distribution of wealth that appears to be every bit as universal as the laws of thermodynamics or chemistry. The present paper proposes some Bayes estimators of shape parameter of Pareto income distribution in censored sampling. Asymmetric LINEX loss function has been considered to study the effects of overestimation and underestimation. For the prior distribution of the parameter involved a number of priors including one and two-parameter exponential, truncated Erlang and doubly truncated gamma have been contemplated to express the belief of the experimenter s/he has regarding the parameter. The estimators thus obtained have been compared theoretically and empirically with the corresponding estimators under squared error loss function, some of which were reported by Bhattacharya et al. (1999).
Modified Adaptive Cluster Sampling Designs
Park, Jeong-Soo ; Kim, Youn-Woo ; Son, Chang-Kyoon ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 57~69
DOI : 10.5351/CKSS.2007.14.1.057
Adaptive cluster sampling design is known as a sampling method for rare clustered population. Three modified adaptive cluster sampling designs are proposed. The adjusted Hansen-Hurwitz estimator and the Horvitz-Thompson estimator are considered. Efficiency issue of the proposed sampling designs is discussed in a Monte-Carlo simulation study.
Bayes and Empirical Bayes Estimation of the Scale Parameter of the Gamma Distribution under Balanced Loss Functions
Rezaeian, R. ; Asgharzadeh, A. ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 71~80
DOI : 10.5351/CKSS.2007.14.1.071
The present paper investigates estimation of a scale parameter of a gamma distribution using a loss function that reflects both goodness of fit and precision of estimation. The Bayes and empirical Bayes estimators rotative to balanced loss functions (BLFs) are derived and optimality of some estimators are studied.
Bayesian Inference for Multinomial Group Testing
Heo, Tae-Young ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 81~92
DOI : 10.5351/CKSS.2007.14.1.081
This paper consider trinomial group testing concerned with classification of N given units into one of k disjoint categories. In this paper, we propose Bayesian inference for estimating individual category proportions using the trinomial group testing model proposed by Bar-Lev et al. (2005). We compared a relative efficience (RE) based on the mean squared error (MSE) of MLE and Bayes estimators with various prior information. The impact of different prior specifications on the estimates is also investigated using selected prior distribution. The impact of different priors on the Bayes estimates is modest when the sample size and group size we large.
Some Exponentiated Distributions
Ali, M. Masoom ; Pal, Manisha ; Woo, Jung-Soo ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 93~109
DOI : 10.5351/CKSS.2007.14.1.093
In this paper we study a number of new exponentiated distributions. The survival function, failure rate and moments of the distributions have been derived using certain special functions. The behavior of the failure rate has also been studied.
Comparison of Nonparametric Maximum Likelihood and Bayes Estimators of the Survival Function Based on Current Status Data
Kim, Hee-Jeong ; Kim, Yong-Dai ; Son, Young-Sook ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 111~119
DOI : 10.5351/CKSS.2007.14.1.111
In this paper, we develop a nonparametric Bayesian methodology of estimating an unknown distribution function F at the given survival time with current status data under the assumption of Dirichlet process prior on F. We compare our algorithm with the nonparametric maximum likelihood estimator through application to simulated data and real data.
A Closed-Form Bayesian Inferences for Multinomial Randomized Response Model
Heo, Tae-Young ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 121~131
DOI : 10.5351/CKSS.2007.14.1.121
In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare the maximum likelihood estimator and the Bayes estimator in terms of MSE.
A Note on Fuzzy Support Vector Classification
Lee, Sung-Ho ; Hong, Dug-Hun ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 133~140
DOI : 10.5351/CKSS.2007.14.1.133
The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy
set. It will show us the trend of classification functions as
A Study on Box-Cox Transformed Threshold GARCH(1,1) Process
Lee, O. ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 141~146
DOI : 10.5351/CKSS.2007.14.1.141
In this paper, we consider a Box-Cox transformed threshold GARCH(1,1) process and find a sufficient condition under which the process is geometrically ergodic and has the
-mixing property with an exponential decay rate.
A Note Based on Multiparameter Discrete Exponential Families in View of Cacoullos-type Inequalities
Borzadaran, G. R. Mohtashami ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 147~153
DOI : 10.5351/CKSS.2007.14.1.147
In this note, we obtained results related to multiparameter discrete exponential families on considering lattice or semi-lattice in place of N (Natural numbers) in view of Cacoullos-type inequalities via the same arguments in Papathanasiou (1990, 1993).
Estimation of the Change Point in Monitoring the Mean of Autocorrelated Processes
Lee, Jae-Heon ; Han, Jung-Hee ; Jung, Sang-Hyun ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 155~167
DOI : 10.5351/CKSS.2007.14.1.155
Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the mean of a process in which the observations can be modeled as an AR(1) process plus an additional random error. The performance of the proposed MLE is compared to the performance of the built-in estimator when they are used in EWMA charts based on the residuals. The results show that the proposed MLE provides good performance in terms of both accuracy and precision of the estimator.
Statistical Method of Ranking Candidate Genes for the Biomarker
Kim, Byung-Soo ; Kim, In-Young ; Lee, Sun-Ho ; Rha, Sun-Young ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 169~182
DOI : 10.5351/CKSS.2007.14.1.169
Receive operating characteristic (ROC) approach can be employed to rank candidate genes from a microarray experiment, in particular, for the biomarker development with the purpose of population screening of a cancer. In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. Ideally, this experiment produces n pairs of microarray data. However, it is often the case that there are missing values either in the normal or tumor tissue data. Practically, we have
pairs of complete observations,
"normal only" and
"tumor only" data for the microarray. We refer to this data set as a mixed data set. We develop a ROC approach on the mixed data set to rank candidate genes for the biomarker development for the colorectal cancer screening. It turns out that the correlation between two ranks in terms of ROC and t statistics based on the top 50 genes of ROC rank is less than 0.6. This result indicates that employing a right approach of ranking candidate genes for the biomarker development is important for the allocation of resources.
Separate Fuzzy Regression with Fuzzy Input and Output
Choi, Seung-Hoe ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 183~193
DOI : 10.5351/CKSS.2007.14.1.183
This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).
Iterative Support Vector Quantile Regression for Censored Data
Shim, Joo-Yong ; Hong, Dug-Hun ; Kim, Dal-Ho ; Hwang, Chang-Ha ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 195~203
DOI : 10.5351/CKSS.2007.14.1.195
In this paper we propose support vector quantile regression (SVQR) for randomly right censored data. The proposed procedure basically utilizes iterative method based on the empirical distribution functions of the censored times and the sample quantiles of the observed variables, and applies support vector regression for the estimation of the quantile function. Experimental results we then presented to indicate the performance of the proposed procedure.
Normal Mixture Model with General Linear Regressive Restriction: Applied to Microarray Gene Clustering
Kim, Seung-Gu ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 205~213
DOI : 10.5351/CKSS.2007.14.1.205
In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.
Variable Selection in Sliced Inverse Regression Using Generalized Eigenvalue Problem with Penalties
Park, Chong-Sun ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 215~227
DOI : 10.5351/CKSS.2007.14.1.215
Variable selection algorithm for Sliced Inverse Regression using penalty function is proposed. We noted SIR models can be expressed as generalized eigenvalue decompositions and incorporated penalty functions on them. We found from small simulation that the HARD penalty function seems to be the best in preserving original directions compared with other well-known penalty functions. Also it turned out to be effective in forcing coefficient estimates zero for irrelevant predictors in regression analysis. Results from illustrative examples of simulated and real data sets will be provided.
Local Influence of the Quasi-likelihood Estimators in Generalized Linear Models
Jung, Kang-Mo ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 229~239
DOI : 10.5351/CKSS.2007.14.1.229
We present a diagnostic method for the quasi-likelihood estimators in generalized linear models. Since these estimators can be usually obtained by iteratively reweighted least squares which are well known to be very sensitive to unusual data, a diagnostic step is indispensable to analysis of data. We extend the local influence approach based on the maximum likelihood function to that on the quasi-likelihood function. Under several perturbation schemes local influence diagnostics are derived. An illustrative example is given and we compare the results provided by local influence and deletion.
Short Term Interest Rate Model Using Box-Cox Transformation
Choi, Young-Soo ; Lee, Yoon-Dong ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 241~254
DOI : 10.5351/CKSS.2007.14.1.241
This paper propose a new short-term interest rate model having a different nonlinear drift function and the same diffusion coefficient with Chan et al. (1992) model. The fractional polynomial power of the drift function in our model is linked to the local volatility elasticity of the diffusion coefficient. While the nonlinear drift function estimated by
-Sahalia (1996a) and others has a feature that higher interest rates tend to revert downward and low rates upward, the drift function estimated by our nonlinear model shows that higher interest rate mean-reverts strongly, but, medium rates has almost zero drift and low rates has a very small drift. This characteristic coincides the empirical result based on the nonparametric methodology by Stanton (1997) and the implication by the scatter plot of the short rate data.
Bayesian Parameter Estimation of the Four-Parameter Gamma Distribution
Oh, Mi-Ra ; Kim, Kyung-Sook ; Cho, Wan-Hyun ; Son, Young-Sook ;
Communications for Statistical Applications and Methods, volume 14, issue 1, 2007, Pages 255~266
DOI : 10.5351/CKSS.2007.14.1.255
A Bayesian estimation of the four-parameter gamma distribution is considered under the noninformative prior. The Bayesian estimators are obtained by the Gibbs sampling. The generation of the shape/power parameter and the power parameter in the Gibbs sampler is implemented using the adaptive rejection sampling algorithm of Gilks and Wild (1992). Also, the location parameter is generated using the adaptive rejection Metropolis sampling algorithm of Gilks, Best and Tan (1995). Finally, the simulation result is presented.