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Two-sample test of stochastic block models via the maximum sampling entry-wise deviation J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-03-03 Qianyong Wu, Jiang Hu
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Jackknife model averaging for linear regression models with missing responses J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-02-19 Jie Zeng, Weihu Cheng, Guozhi Hu
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A self-normalization test for structural breaks in a regression model for panel data sets J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-02-15 Ji-Eun Choi, Dong Wan Shin
We construct a new structural break test in a panel regression model using the self-normalization method. The self-normalization test is shown to be superior to an existing test in that the former is theoretically and experimentally valid for regression models with serially and/or cross-sectionally correlated errors while the latter is not. We derive the asymptotic null distribution of the self-normalization
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Gradient-based kernel variable selection for support vector hazards machine J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-02-15 Sanghun Jeong, Kyungjun Kang, Hojin Yang
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Testing for conditional independence of survival time from covariate J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-02-14 Minjung Kwak
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Asymptotic results of error density estimator in nonlinear autoregressive models J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-02-13 Shipeng Wu, Wenzhi Yang, Min Gao, Hongyan Fang
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Disseminating massive frequency tables by masking aggregated cell frequencies J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-30 Min-Jeong Park, Hang J. Kim, Sunghoon Kwon
We propose a confidential approach for disseminating frequency tables constructed for any combination of key variables in the given microdata, including those of hierarchical key variables. The system generates all possible frequency tables by either marginalizing or aggregating fully joint frequency tables of key variables while protecting the original cells with low frequencies through two masking
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Use of ridge calibration method in predicting election results J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-23 Yohan Lim, Mingue Park
Ridge calibration is a penalized method used in survey sampling to reduce the variability of the final set of weights by relaxing the linear restrictions. We proposed a method for selecting the penalty parameter that minimizes the estimated mean squared error of the mean estimator when estimated auxiliary information is used. We showed that the proposed estimator is asymptotically equivalent to the
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Asymptotic of the number of false change points of the fused lasso signal approximator J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-18 Donghyeon Yu, Johan Lim, Won Son
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Large sample properties of maximum likelihood estimator using moving extremes ranked set sampling J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-13 Han Wang, Wangxue Chen, Bingjie Li
In this paper, we investigate the maximum likelihood estimator (MLE) for the parameter \(\theta\) in the probability density function \(f(x;\theta )\). We specifically focus on the application of moving extremes ranked set sampling (MERSS) and analyze its properties in large samples. We establish the existence and uniqueness of the MLE for two common distributions when utilizing MERSS. Our theoretical
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Logistic regression models for elastic shape of curves based on tangent representations J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-12 Tae-Young Heo, Joon Myoung Lee, Myung Hun Woo, Hyeongseok Lee, Min Ho Cho
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Byzantine-resilient decentralized network learning J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-10 Yaohong Yang, Lei Wang
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Sequential online monitoring for autoregressive time series of counts J. Korean Stat. Soc. (IF 0.6) Pub Date : 2024-01-02
Abstract This study considers the online monitoring problem for detecting the parameter change in time series of counts. For this task, we construct a monitoring process based on the residuals obtained from integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models. We consider this problem within a more general framework using martingale difference sequences as the monitoring
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Return prediction by machine learning for the Korean stock market J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-12-20 Wonwoo Choi, Seongho Jang, Sanghee Kim, Chayoung Park, Sunyoung Park, Seongjoo Song
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Spatially integrated estimator of finite population total by integrating data from two independent surveys using spatial information J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-12-19 Nobin Chandra Paul, Anil Rai, Tauqueer Ahmad, Ankur Biswas, Prachi Misra Sahoo
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Statistical integration of allele frequencies from several organizations J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-12-18 Su Jin Jeong, Hyo-jung Lee, Soong Deok Lee, Su Jeong Park, Seung Hwan Lee, Jae Won Lee
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Classification of repeated measurements using bias corrected Euclidean distance discriminant function J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-12-12 Edward Kanuti Ngailo, Saralees Nadarajah
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Nonparametric longitudinal regression model to analyze shape data using the Procrustes rotation J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-12-03 Meisam Moghimbeygi, Mousa Golalizadeh
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Sparse functional linear models via calibrated concave-convex procedure J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-12-03 Young Joo Lee, Yongho Jeon
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Variable selection for semiparametric accelerated failure time models with nonignorable missing data J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-11-19 Tianqing Liu, Xiaohui Yuan, Liuquan Sun
The regularization approach for variable selection was well developed for semiparametric accelerated failure time (AFT) models, where the response variable is right censored. In the presence of missing data, this approach needs to be tailored to different missing data mechanisms. In this paper, we propose a flexible and generally applicable missing data mechanism for AFT models, which contains both
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Robust and Efficient derivative estimation under correlated errors J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-11-18 Deru Kong, Wei Shen, Shengli Zhao, WenWu Wang
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A review on concomitants of order statistics and its application in parameter estimation under ranked set sampling J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-11-13 Rohan D. Koshti, Kirtee K. Kamalja
The concomitants of order statistics (COS) is a variable associated with a bivariate sample when ordered with respect to the other variable. The distribution theory of COS is widely developed for various families of bivariate distributions. The COS have a wide variety of applications in different fields such as selection procedures, parameter estimation, ranked set sampling (RSS), etc. The notion of
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Asymptotic bias of the $$\ell _2$$ -regularized error variance estimator J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-11-14 Semin Choi, Gunwoong Park
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A novel doubling-tripling-threshold accepting hybrid algorithm for constructing asymmetric space-filling designs J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-11-03 A. M. Elsawah
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Variable selection for single-index models based on martingale difference divergence J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-10-25 Xiaohui Yuan, Yue Wang, Yiming Wang, Tianqing Liu
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Distributed smoothed rank regression with heterogeneous errors for massive data J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-10-23 Xiaohui Yuan, Xinran Zhang, Yue Wang, Chunjie Wang
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Estimation of Bergsma’s covariance J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-10-18 Arup Bose, Divya Kappara, Madhuchhanda Bhattacharjee
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A nonparametric binomial likelihood approach for causal inference in instrumental variable models J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-10-19 Kwonsang Lee, Bhaswar B. Bhattacharya, Jing Qin, Dylan S. Small
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Genetic algorithm with a Bayesian approach for multiple change-point detection in time series of counting exceedances for specific thresholds J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-10-09 Biviana Marcela Suárez-Sierra, Arrigo Coen, Carlos Alberto Taimal
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Normality test in random coefficient autoregressive models J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-09-22 Zixuan Liu, Junmo Song
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Construction of optimal designs for quantile regression model via particle swarm optimization J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-09-21 Yi Zhai, Chen Xing, Zhide Fang
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Improved control chart for statistical process control using combined X and delayed EWMA statistics J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-09-21 Johan Lim, Sungim Lee
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Nonparametric maximum likelihood estimation of the distribution function using ranked-set sampling J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-09-20 Jesse Frey, Yimin Zhang
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Mixture copulas with discrete margins and their application to imbalanced data J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-09-09 Yujian Liu, Dejun Xie, David A. Edwards, Siyi Yu
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Higher-order expansions of sample range from skew-normal distribution J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-09-02 Wenjing Zhang, Yingyin Lu
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Objective Bayesian inference for the reliability in a bivariate Lomax distribution J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-08-30 Sang Gil Kang, Woo Dong Lee, Yongku Kim
We consider the objective Bayesian analysis for the reliability in the bivariate Lomax distribution. In this paper, we derive the first- and second-order matching priors and the reference priors for the reliability in the bivariate Lomax population. However, it turns out that the reference priors do not satisfy the first-order matching criterion and also, the matching priors and the reference priors
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A multiple imputation approach for the Cox–Aalen cure model with interval-censored data J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-08-31 Pao-sheng Shen
Interval censored survival data, where the exact event time is only known to lie in an interval, is commonly encountered in practice. Furthermore, medical advancements have made it possible for a fraction of patients to be cured. In this article, we analyze interval-censored data using the Cox–Aalen model with a cure fraction, where the probability of being uncured is determined by a logistic regression
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Goodness of fit test for Rayleigh distribution with censored observations J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-08-08 K. M. Vaisakh, Thomas Xavier, E. P. Sreedevi
We develop new goodness of fit tests for Rayleigh distribution based on fixed point characterization. We use U-Statistic theory to derive the test statistics. First we develop a test for complete data and then discuss, how the right censored observations can be incorporated in the testing procedure. The asymptotic properties of the test statistic in both uncensored and censored cases are studied in
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Robust variable selection for the varying index coefficient models J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-08-07 Hang Zou, Yunlu Jiang
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Correction: Extreme eigenvalues of principal minors of random matrices with moment conditions J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-07-18 Jianwei Hu, Seydou Keita, Kang Fu
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Functional regression with dependent error and missing observation in reproducing kernel Hilbert spaces J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-06-30 Yan-Ping Hu, Han-Ying Liang
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A new non-iterative deterministic algorithm for constructing asymptotically orthogonal maximin distance Latin hypercube designs J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-07-03 A. M. Elsawah, Yingyao Gong
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Extreme eigenvalues of principal minors of random matrix with moment conditions J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-06-21 Jianwei Hu, Seydou Keita, Kang Fu
Let \(\varvec{x}_1,\ldots ,\varvec{x}_n\) be a random sample of size n from a p-dimensional population distribution, where \(p=p(n)\rightarrow \infty\). Consider a symmetric matrix \(W=X^\top X\) with parameters n and p, where \(X=(\varvec{x}_1,\ldots ,\varvec{x}_n)^\top\). In this paper, motivated by model selection theory in high-dimensional statistics, we mainly investigate the asymptotic behavior
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Zero-inflated Poisson-Akash distribution for count data with excessive zeros J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-06-16 Mohammad Kafeel Wani, Peer Bilal Ahmad
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Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+ J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-06-15 Dongu Han, Daeyoung Lim, Taeryon Choi
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A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-05-29 A. M. Elshehawey, Zhengming Qian
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The relationship between shape parameters and kurtosis in some relevant models J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-05-17 Claudio Giovanni Borroni, Lucio De Capitani
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Structure learning of exponential family graphical model with false discovery rate control J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-05-09 Yanhong Liu, Yuhao Zhang, Zhonghua Li
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Monitoring parameter change for bivariate time series models of counts J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-05-07 Sangyeol Lee, Dongwon Kim
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Matrix variate density estimation with additional information J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-04-18 Abdolnasser Sadeghkhani, Mohammad Arashi
Quite often, some additional information is available from different sources other than the parent population. In such cases, the density estimation problem becomes substantial. Suppose a random matrix loading information is independent of the random matrix we want to estimate its density. This paper proposes estimating the future density of the random–variate matrix from a normal distribution using
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Parameter estimation for nth-order mixed fractional Brownian motion with polynomial drift J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-03-14 Mohamed El Omari
The present work deals with the parameter estimation problem for an nth-order mixed fractional Brownian motion (fBm) of the form \(X(t)=\theta \mathcal {P}(t)+\alpha W(t)+\sigma B_H^n(t)\), where W(t) is a Wiener process and \(B_H^n(t)\) is the nth-order fBm (\(n\ge 2\)) with Hurst index \(H\in (n-1,n)\). By using power-variations method we estimate \(\alpha\), then we build maximum likelihood estimators
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Testing independence of bivariate censored data using random walk on restricted permutation graph J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-02-24 Seonghun Cho, Donghyeon Yu, Johan Lim
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Concentration inequalities for Kernel density estimators under uniform mixing J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-02-24 Stelios Arvanitis
We derive non-asymptotic concentration inequalities for the uniform deviation between a multivariate density function and its non-parametric kernel density estimator in stationary and uniform mixing time series framework. We derive analogous inequalities for their (first) Wasserstein distance, as well as for the deviations between integrals of bounded functions w.r.t. them. They can be used for the
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$$L_1$$ -penalized fraud detection support vector machines J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-02-21 Minhyoung Park, Hyungwoo Kim, Seung Jun Shin
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Bayesian pathway selection J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-01-24 Pacifique Nizeyimana, Kyeong Eun Lee, Inyoung Kim
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The expectation–maximization approach for Bayesian additive Cox regression with current status data J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-01-22 Di Cui, Clarence Tee
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Goodness of fit test for uniform distribution with censored observation J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-01-23 E. P. Sreedevi, Sudheesh K. Kattumannil
We develop new goodness of fit test for uniform distribution based on a conditional moment characterization. We study the asymptotic properties of the proposed test statistic. We also present a goodness of fit test for uniform distribution to incorporate the right censored observations and studied its properties. A Monte Carlo simulation study is carried out to evaluate the finite sample performance
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Deconvolution problem of cumulative distribution function with heteroscedastic errors J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-01-21 Le Thi Hong Thuy, Cao Xuan Phuong
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Nonresponse adjusted estimation based on a composite weighting method in a panel survey J. Korean Stat. Soc. (IF 0.6) Pub Date : 2023-01-11 Hyung-A Choi, Young-Won Kim
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A Bayesian method for multinomial probit model J. Korean Stat. Soc. (IF 0.6) Pub Date : 2022-12-03 Donghyun Koo, Chanmin Kim, Keunbaik Lee
The independence of irrelevant alternatives (IIA) property states that the ratio of any two choice probabilities in a set of alternatives is independent of the presence or absence of other alternatives. In the modeling of multinomial data, the IIA is not feasible. In this situation, the multinomial probit (MNP) model is a type of discrete choice model that is commonly used. Due to the identifiability