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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)~25권5호(2018) |수록논문 수 : 1,819
CSAM(Communications for Statistical Applications and Methods)
25권5호(2018년 09월) 수록논문
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KCI등재

1Non-convex penalized estimation for the AR process

저자 : Okyoung Na , Sunghoon Kwon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 453-470 (18 pages)

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We study how to distinguish the parameters of the sparse autoregressive (AR) process from zero using a nonconvex penalized estimation. A class of non-convex penalties are considered that include the smoothly clipped absolute deviation and minimax concave penalties as special examples. We prove that the penalized estimators achieve some standard theoretical properties such as weak and strong oracle properties which have been proved in sparse linear regression framework. The results hold when the maximal order of the AR process increases to infinity and the minimal size of true non-zero parameters decreases toward zero as the sample size increases. Further, we construct a practical method to select tuning parameters using generalized information criterion, of which the minimizer asymptotically recovers the best theoretical non-penalized estimator of the sparse AR process. Simulation studies are given to confirm the theoretical results.

KCI등재

2A Bayesian cure rate model with dispersion induced by discrete frailty

저자 : Vicente G. Cancho , Katherine E. C. Zavaleta , M´ Arcia A. C. Macera , Adriano K. Suzuki , Francisco Louzada

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 471-488 (18 pages)

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In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ψ-divergence measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

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3Bayesian quantile regression analysis of Korean Jeonse deposit

저자 : Eun Jung Nam , Eun Kyung Lee , Man-suk Oh

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 489-499 (11 pages)

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Jeonse is a unique property rental system in Korea in which a tenant pays a part of the price of a leased property as a fixed amount security deposit and gets back the entire deposit when the tenant moves out at the end of the tenancy. Jeonse deposit is very important in the Korean real estate market since it is directly related to the residential property sales price and it is a key indicator to predict future real estate market trend. Jeonse deposit data shows a skewed and heteroscedastic distribution and the commonly used mean regression model may be inappropriate for the analysis of Jeonse deposit data. In this paper, we apply a Bayesian quantile regression model to analyze Jeonse deposit data, which is non-parametric and does not require any distributional assumptions. Analysis results show that the quantile regression coefficients of most explanatory variables change dramatically for different quantiles. The regression coefficients of some variables have different signs for different quantiles, implying that even the same variable may affect the Jeonse deposit in the opposite direction depending on the amount of deposit.

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4Sequential patient recruitment monitoring in multi-center clinical trials

저자 : Dong-yun Kim , Sung-min Han , Marston Youngblood Jr

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 501-512 (12 pages)

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We propose Sequential Patient Recruitment Monitoring (SPRM), a new monitoring procedure for patient recruitment in a clinical trial. Based on the sequential probability ratio test using improved stopping boundaries by Woodroofe, the method allows for continuous monitoring of the rate of enrollment. It gives an early warning when the recruitment is unlikely to achieve the target enrollment. The packet data approach combined with the Central Limit Theorem makes the method robust to the distribution of the recruitment entry pattern. A straightforward application of the counting process framework can be used to estimate the probability to achieve the target enrollment under the assumption that the current trend continues. The required extension of the recruitment period can also be derived for a given confidence level. SPRM is a new, continuous patient recruitment monitoring tool that provides an opportunity for corrective action in a timely manner. It is suitable for the modern, centralized data management environment and requires minimal effort to maintain. We illustrate this method using real data from two well-known, multicenter, phase III clinical trials.

KCI등재

5On robustness in dimension determination in fused sliced inverse regression

저자 : Jae Keun Yoo , Yoo Na Cho

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 513-521 (9 pages)

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The goal of sufficient dimension reduction (SDR) is to replace original p-dimensional predictors with a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) (Li, Journal of the American Statistical Association, 86, 316-342, 1991) is one of the most popular SDR methods because of its applicability and simple implementation in practice. However, SIR may yield different dimension reduction results for different numbers of slices and despite its popularity, is a clear deficit for SIR. To overcome this, a fused sliced inverse regression was recently proposed. The study shows that the dimension-reduced predictors is robust to the numbers of the slices, but it does not investigate how robust its dimension determination is. This paper suggests a permutation dimension determination for the fused sliced inverse regression that is compared with SIR to investigate the robustness to the numbers of slices in the dimension determination. Numerical studies confirm this and a real data example is presented.

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6The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

저자 : Juliana B. Fachini-gomes , Edwin M. M. Ortega , Gauss M. Cordeiro , Adriano K. Suzuki

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 523-544 (22 pages)

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Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate KumaraswamyWeibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

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7A sample size calibration approach for the p-value problem in huge samples

저자 : Yousung Park , Saebom Jeon , Tae Yeon Kwon

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 545-557 (13 pages)

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The inclusion of covariates in the model often affects not only the estimates of meaningful variables of interest but also its statistical significance. Such gap between statistical and subject-matter significance is a critical issue in huge sample studies. A popular huge sample study, the sample cohort data from Korean National Health Insurance Service, showed such gap of significance in the inference for the effect of obesity on cause of mortality, requiring careful consideration. In this regard, this paper proposes a sample size calibration method based on a Monte Carlo t (or z)-test approach without Monte Carlo simulation, and also proposes a test procedure for subject-matter significance using this calibration method in order to complement the deflated p-value in the huge sample size. Our calibration method shows no subject-matter significance of the obesity paradox regardless of race, sex, and age groups, unlike traditional statistical suggestions based on p-values.

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8Robust inference with order constraint in microarray study

저자 : Joonsung Kang

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 559-568 (10 pages)

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Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The Mestimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of α and π0 (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.

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9New approach for analysis of progressive Type-II censored data from the Pareto distribution

저자 : Jung-in Seo , Suk-bok Kang , Ho-yong Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 25권 5호 발행 연도 : 2018 페이지 : pp. 569-575 (7 pages)

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Pareto distribution is important to analyze data in actuarial sciences, reliability, finance, and climatology. In general, unknown parameters of the Pareto distribution are estimated based on the maximum likelihood method that may yield inadequate inference results for small sample sizes and high percent censored data. In this paper, a new approach based on the regression framework is proposed to estimate unknown parameters of the Pareto distribution under the progressive Type-II censoring scheme. The proposed method provides a new regression type estimator that employs the spacings of exponential progressive Type-II censored samples. In addition, the provided estimator is a consistent estimator with superior performance compared to maximum likelihood estimators in terms of the mean squared error and bias. The validity of the proposed method is assessed through Monte Carlo simulations and real data analysis.

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