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Yang Yang,Jinguan Lin,Chao Huang,Xin Ma 한국통계학회 2012 Journal of the Korean Statistical Society Vol.41 No.2
This paper considers an ordinary renewal risk model and a compound renewal risk model with constant interest rate, subexponential claims and a general premium process. We derive some asymptotic results on the finite-time ruin probabilities.
Construction of main effects plans orthogonal through the block factor based on level permutation
Xue-Ping Chen,Jinguan Lin,Xing-Fang Huang 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.4
In this paper, We first demonstrate that the orthogonal property of main effects plans orthogonal through the block factor (Bagchi, 2010) remains unchanged under level permutation. However, level permutation of factors could alter their geometrical structures and statistical properties. Hence uniformity is used to further distinguish main effects plans orthogonal through the block factor (POTB). A modified optimization algorithm is proposed to search uniform or nearly uniform POTBs and many new optimal POTBs with lowerdiscrepancy are obtained.
Xing-cai Zhou,Jinguan Lin 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.2
In this paper, empirical likelihood inference in mixtures of semiparametric varying coefficient errors-in-variables (EV) models for longitudinal data with nonignorable dropout is investigated. The empirical log-likelihood ratio statistic for the fixed-effects parameters and the mean parameters of random effects are proposed. The proposed statistic at the true parameters is proven to be asymptotically χ2q+r , where q and r are the dimensions of the fixed and random effects respectively, and the corresponding confidence regions for the parameters of interest are then constructed. We also obtain the maximum empirical likelihood estimator of the parameters, and prove that it is asymptotically normal under some suitable conditions. Simulation studies are undertaken to assess the finite sample performance of the proposed method.
Bayesian case influence analysis for GARCH models based on Kullback–Leibler divergence
Hong-Xia Hao,Jinguan Lin,Hong-Xia Wang,Xing-Fang Huang 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.4
Influence analysis has become an important tool for statistical analysis. This paper is concerned with Bayesian case influence analysis for generalized autoregressive conditional heteroscedasticity (GARCH) model. Case influence analysis is developed for both the joint and marginal posterior distributions based on the Kullback–Leibler divergence (K–L divergence). A simplified expression is presented for computing the K–L divergence between the full data posterior distribution and the case-deleted posterior distributions. The related computations can be done numerically by Markov Chain Monte Carlo samples from posterior distribution with full data. Some simulation studies are carried out to examine the performance of the proposed methods and show the relations between case-deletion model (CDM) and mean-shift outlier model (MSOM) for the GARCH models. Meanwhile, the methods are also illustrated by a real data.
Xu Xuemeng,Peng Qiu,Jiang Xianjie,Tan Shiming,Yang Yiqing,Yang Wenjuan,Han Yaqian,Chen Yuyu,Oyang Linda,Lin Jinguan,Xia Longzheng,Peng Mingjing,Wu Nayiyuan,Tang Yanyan,Li Jinyun,Liao Qianjin,Zhou Yuju 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
Metabolic reprogramming and epigenetic modifications are hallmarks of cancer cells. In cancer cells, metabolic pathway activity varies during tumorigenesis and cancer progression, indicating regulated metabolic plasticity. Metabolic changes are often closely related to epigenetic changes, such as alterations in the expression or activity of epigenetically modified enzymes, which may exert a direct or an indirect influence on cellular metabolism. Therefore, exploring the mechanisms underlying epigenetic modifications regulating the reprogramming of tumor cell metabolism is important for further understanding tumor pathogenesis. Here, we mainly focus on the latest studies on epigenetic modifications related to cancer cell metabolism regulations, including changes in glucose, lipid and amino acid metabolism in the cancer context, and then emphasize the mechanisms related to tumor cell epigenetic modifications. Specifically, we discuss the role played by DNA methylation, chromatin remodeling, noncoding RNAs and histone lactylation in tumor growth and progression. Finally, we summarize the prospects of potential cancer therapeutic strategies based on metabolic reprogramming and epigenetic changes in tumor cells.