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한국통계학회> CSAM(Communications for Statistical Applications and Methods)

CSAM(Communications for Statistical Applications and Methods) update

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  • : 한국통계학회논문집(~2011)→Communications for statistical applications and methods(2012~)

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수록범위 : 1권1호(1994)~26권4호(2019) |수록논문 수 : 1,857
CSAM(Communications for Statistical Applications and Methods)
26권4호(2019년 07월) 수록논문
최근 권호 논문
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KCI등재

1Nonnegative estimates of variance components in a two-way random model

저자 : Jaesung Choi

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 4호 발행 연도 : 2019 페이지 : pp. 337-346 (10 pages)

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This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.

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2An improved estimation procedure of population mean using bivariate auxiliary information under non-response

저자 : Shashi Bhushan , Abhay Pratap Pandey

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 4호 발행 연도 : 2019 페이지 : pp. 347-357 (11 pages)

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We propose new classes of estimators of population mean under non-response using bivariate auxiliary information. Some improved regression (or difference) type estimators have been proposed in four different situations of non response along with their properties and the expressions for the bias and mean square errors of the proposed estimators are derived under double (two-stage) sampling scheme. The properties of the suggested class of estimators are studied and it is observed that the proposed estimators performed better when compared to conventional estimators proposed by Singh and Kumar (Journal of Statistical Planning and Inference, 140, 2536-2550, 2010b), Shabbir and Khan (Communications in Statistics - Theory and Methods, 42, 4127-4145, 2013) and Bhushan and Naqvi (Journal of Statistics and Management Systems, 18, 573-602, 2015). A comparative study is also conducted both theoretically as well as empirically in order to support the results.

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3A numerical study on group quantile regression models

저자 : Doyoen Kim , Yoonsuh Jung

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 4호 발행 연도 : 2019 페이지 : pp. 359-370 (12 pages)

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Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.

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4Restricted maximum likelihood estimation of a censored random effects panel regression model

저자 : Minah Lee , Seung-chun Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 4호 발행 연도 : 2019 페이지 : pp. 371-383 (13 pages)

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Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

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5Classical and Bayesian studies for a new lifetime model in presence of type-II censoring

저자 : Teena Goyal , Piyush K Rai , Sandeep K Maurya

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 4호 발행 연도 : 2019 페이지 : pp. 385-410 (26 pages)

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This paper proposes a new class of distribution using the concept of exponentiated of distribution function that provides a more flexible model to the baseline model. It also proposes a new lifetime distribution with different types of hazard rates such as decreasing, increasing and bathtub. After studying some basic statistical properties and parameter estimation procedure in case of complete sample observation, we have studied point and interval estimation procedures in presence of type-II censored samples under a classical as well as Bayesian paradigm. In the Bayesian paradigm, we considered a Gibbs sampler under Metropolis-Hasting for estimation under two different loss functions. After simulation studies, three different real datasets having various nature are considered for showing the suitability of the proposed model.

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6Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes

저자 : Henry Linder , Yuping Zhang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 26권 4호 발행 연도 : 2019 페이지 : pp. 411-430 (20 pages)

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Tumor development is driven by complex combinations of biological elements. Recent advances suggest that molecularly distinct subtypes of breast cancers may respond differently to pathway-targeted therapies. Thus, it is important to dissect pathway disturbances by integrating multiple molecular profiles, such as genetic, genomic and epigenomic data. However, missing data are often present in the -omic profiles of interest. Motivated by genomic data integration and imputation, we present a new statistical framework for pathway significance analysis. Specifically, we develop a new strategy for imputation of missing data in large-scale genomic studies, which adapts low-rank, structured matrix completion. Our iterative strategy enables us to impute missing data in complex configurations across multiple data platforms. In turn, we perform large-scale pathway analysis integrating gene expression, copy number, and methylation data. The advantages of the proposed statistical framework are demonstrated through simulations and real applications to breast cancer subtypes. We demonstrate superior power to identify pathway disturbances, compared with other imputation strategies. We also identify differential pathway activity across different breast tumor subtypes.

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