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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 22, Issue 6 - Nov 2015
Volume 22, Issue 5 - Sep 2015
Volume 22, Issue 4 - Jul 2015
Volume 22, Issue 3 - May 2015
Volume 22, Issue 2 - Mar 2015
Volume 22, Issue 1 - Jan 2015
Selecting the target year
Families of Distributions Arising from Distributions of Ordered Data
Ahmadi, Mosayeb ; Razmkhah, M. ; Mohtashami Borzadaran, G.R. ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 105~120
DOI : 10.5351/CSAM.2015.22.2.105
A large family of distributions arising from distributions of ordered data is proposed which contains other models studied in the literature. This extension subsume many cases of weighted random variables such as order statistics, records, k-records and many others in variety. Such a distribution can be used for modeling data which are not identical in distribution. Some properties of the theoretical model such as moment, mean deviation, entropy criteria, symmetry and unimodality are derived. The proposed model also studies the problem of parameter estimation and derives maximum likelihood estimators in a weighted gamma distribution. Finally, it will be shown that the proposed model is the best among the previously introduced distributions for modeling a real data set.
A Two Sample Test for Functional Data
Lee, Jong Soo ; Cox, Dennis D. ; Follen, Michele ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 121~135
DOI : 10.5351/CSAM.2015.22.2.121
We consider testing equality of mean functions from two samples of functional data. A novel test based on the adaptive Neyman methodology applied to the Hotelling's T-squared statistic is proposed. Under the enlarged null hypothesis that the distributions of the two populations are the same, randomization methods are proposed to find a null distribution which gives accurate significance levels. An extensive simulation study is presented which shows that the proposed test works very well in comparison with several other methods under a variety of alternatives and is one of the best methods for all alternatives, whereas the other methods all show weak power at some alternatives. An application to a real-world data set demonstrates the applicability of the method.
A Dual Problem of Calibration of Design Weights Based on Multi-Auxiliary Variables
Al-Jararha, J. ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 137~146
DOI : 10.5351/CSAM.2015.22.2.137
Singh (2013) considered the dual problem to the calibration of design weights to obtain a new generalized linear regression estimator (GREG) for the finite population total. In this work, we have made an attempt to suggest a way to use the dual calibration of the design weights in case of multi-auxiliary variables; in other words, we have made an attempt to give an answer to the concern in Remark 2 of Singh (2013) work. The same idea is also used to generalize the GREG estimator proposed by Deville and S
rndal (1992). It is not an easy task to find the optimum values of the parameters appear in our approach; therefore, few suggestions are mentioned to select values for such parameters based on a random sample. Based on real data set and under simple random sampling without replacement design, our approach is compared with other approaches mentioned in this paper and for different sample sizes. Simulation results show that all estimators have negligible relative bias, and the multivariate case of Singh (2013) estimator is more efficient than other estimators.
An Additive Sparse Penalty for Variable Selection in High-Dimensional Linear Regression Model
Lee, Sangin ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 147~157
DOI : 10.5351/CSAM.2015.22.2.147
We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.
Signal Reconstruction by Synchrosqueezed Wavelet Transform
Park, Minsu ; Oh, Hee-Seok ; Kim, Donghoh ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 159~172
DOI : 10.5351/CSAM.2015.22.2.159
This paper considers the problem of reconstructing an underlying signal from noisy data. This paper presents a reconstruction method based on synchrosqueezed wavelet transform recently developed for multiscale representation. Synchrosqueezed wavelet transform based on continuous wavelet transform is efficient to estimate the instantaneous frequency of each component that consist of a signal and to reconstruct components. However, an objective selection method for the optimal number of intrinsic mode type functions is required. The proposed method is obtained by coupling the synchrosqueezed wavelet transform with cross-validation scheme. Simulation studies and musical instrument sounds are used to compare the empirical performance of the proposed method with existing methods.
Principal Component Regression by Principal Component Selection
Lee, Hosung ; Park, Yun Mi ; Lee, Seokho ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 173~180
DOI : 10.5351/CSAM.2015.22.2.173
We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal components among the candidate set using sparse regression techniques. The performance of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression and partial least square regression) using synthetic and real datasets.
How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review
Oh, Sunghee ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 181~199
DOI : 10.5351/CSAM.2015.22.2.181
In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at gene, transcript, isoform, and exon specific quantification, genetic and genomic mutations, and etc. RNA-seq technique has been rapidly replacing arrays with seq-based platform experimental settings by revealing a couple of advantages such as identification of alternative splicing and allelic specific expression. The remarkable characteristics of high-throughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes compared to sample size, different sampling rates, inevitable systematic RNA-seq biases, and etc. In this study, we will comprehensively review how robust Bayesian and non-parametric methods have a better performance than classical statistical approaches by explicitly incorporating such intrinsic RNA-seq specific features with flexible and more appropriate assumptions and distributions in practice.
Kernel-Trick Regression and Classification
Huh, Myung-Hoe ;
Communications for Statistical Applications and Methods, volume 22, issue 2, 2015, Pages 201~207
DOI : 10.5351/CSAM.2015.22.2.201
Support vector machine (SVM) is a well known kernel-trick supervised learning tool. This study proposes a working scheme for kernel-trick regression and classification (KtRC) as a SVM alternative. KtRC fits the model on a number of random subsamples and selects the best model. Empirical examples and a simulation study indicate that KtRC's performance is comparable to SVM.