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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
On Bounds for Moments of Unimodal Distributions
Sharma, R. ; Bhandaria, R. ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 201~212
DOI : 10.5351/CSAM.2014.21.3.201
We provide a simple basic method to find bounds for higher order moments of unimodal distributions in terms of lower order moments when the random variable takes value in a given finite real interval. The bounds for moments in terms of the geometric mean of the distribution are also derived. Both continuous and discrete cases are considered. The bounds for the ratio and difference of moments are obtained. The special cases provide refinements of several well-known inequalities, such as Kantorovich inequality and Krasnosel'skii and Krein inequality.
An Empirical Characteristic Function Approach to Selecting a Transformation to Normality
Yeo, In-Kwon ; Johnson, Richard A. ; Deng, XinWei ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 213~224
DOI : 10.5351/CSAM.2014.21.3.213
In this paper, we study the problem of transforming to normality. We propose to estimate the transformation parameter by minimizing a weighted squared distance between the empirical characteristic function of transformed data and the characteristic function of the normal distribution. Our approach also allows for other symmetric target characteristic functions. Asymptotics are established for a random sample selected from an unknown distribution. The proofs show that the weight function
needs to be modified to have thinner tails. We also propose the method to compute the influence function for M-equation taking the form of U-statistics. The influence function calculations and a small Monte Carlo simulation show that our estimates are less sensitive to a few outliers than the maximum likelihood estimates.
Symbolic Cluster Analysis for Distribution Valued Dissimilarity
Matsui, Yusuke ; Minami, Hiroyuki ; Misuta, Masahiro ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 225~234
DOI : 10.5351/CSAM.2014.21.3.225
We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.
Application of Constrained Bayes Estimation under Balanced Loss Function in Insurance Pricing
Kim, Myung Joon ; Kim, Yeong-Hwa ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 235~243
DOI : 10.5351/CSAM.2014.21.3.235
Constrained Bayesian estimates overcome the over shrinkness toward the mean which usual Bayes and empirical Bayes estimates produce by matching first and second empirical moments; subsequently, a constrained Bayes estimate is recommended to use in case the research objective is to produce a histogram of the estimates considering the location and dispersion. The well-known squared error loss function exclusively emphasizes the precision of estimation and may lead to biased estimators. Thus, the balanced loss function is suggested to reflect both goodness of fit and precision of estimation. In insurance pricing, the accurate location estimates of risk and also dispersion estimates of each risk group should be considered under proper loss function. In this paper, by applying these two ideas, the benefit of the constrained Bayes estimates and balanced loss function will be discussed; in addition, application effectiveness will be proved through an analysis of real insurance accident data.
A Note on Exponential Inequalities of ψ-Weakly Dependent Sequences
Hwang, Eunju ; Shin, Dong Wan ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 245~251
DOI : 10.5351/CSAM.2014.21.3.245
Two exponential inequalities are established for a wide class of general weakly dependent sequences of random variables, called
-weakly dependent process which unify weak dependence conditions such as mixing, association, Gaussian sequences and Bernoulli shifts. The
-weakly dependent process includes, for examples, stationary ARMA processes, bilinear processes, and threshold autoregressive processes, and includes essentially all classes of weakly dependent stationary processes of interest in statistics under natural conditions on the process parameters. The two exponential inequalities are established on more general conditions than some existing ones, and are proven in simpler ways.
Case Deletion Diagnostics for Intraclass Correlation Model
Kim, Myung Geun ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 253~260
DOI : 10.5351/CSAM.2014.21.3.253
The intraclass correlation model has a long history of applications in several fields of research. Case deletion diagnostic methods for the intraclass correlation model are proposed. Based on the likelihood equations, we derive a formula for a case deletion diagnostic method which enables us to investigate the influence of observations on the maximum likelihood estimates of the model parameters. Using the Taylor series expansion we develop an approximation to the likelihood distance. Numerical examples are provided for illustration.
Robust Bayesian Inference in Finite Population Sampling under Balanced Loss Function
Kim, Eunyoung ; Kim, Dal Ho ;
Communications for Statistical Applications and Methods, volume 21, issue 3, 2014, Pages 261~274
DOI : 10.5351/CSAM.2014.21.3.261
In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.