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Search Results: 1 - 10 of 66537 matches for " Jin-Guan Lin "
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Tail Dependence for Regularly Varying Time Series
Ai-Ju Shi,Jin-Guan Lin
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/280869
Abstract: We use tail dependence functions to study tail dependence for regularly varying (RV) time series. First, tail dependence functions about RV time series are deduced through the intensity measure. Then, the relation between the tail dependence function and the intensity measure is established: they are biuniquely determined. Finally, we obtain the expressions of the tail dependence parameters based on the expectation of the RV components of the time series. These expressions are coincided with those obtained by the conditional probability. Some simulation examples are demonstrated to verify the results we established in this paper.
Detection of Outliers and Patches in Bilinear Time Series Models
Ping Chen,Ling Li,Ye Liu,Jin-Guan Lin
Mathematical Problems in Engineering , 2010, DOI: 10.1155/2010/580583
Abstract: We propose a Gibbs sampling algorithm to detect additive outliers and patches of outliers in bilinear time series models based on Bayesian view. We first derive the conditional posterior distributions, and then use the results of first Gibbs run to start the second adaptive Gibbs sampling. It is shown that our procedure could reduce possible effects on masking and swamping. At last, some simulations are performed to demonstrate the efficacy of detection and estimation by Monte Carlo methods.
Approximation for the Finite-Time Ruin Probability of a General Risk Model with Constant Interest Rate and Extended Negatively Dependent Heavy-Tailed Claims
Yang Yang,Xin Ma,Jin-guan Lin
Mathematical Problems in Engineering , 2011, DOI: 10.1155/2011/852852
Abstract: We propose a general continuous-time risk model with a constant interest rate. In this model, claims arrive according to an arbitrary counting process, while their sizes have dominantly varying tails and fulfill an extended negative dependence structure. We obtain an asymptotic formula for the finite-time ruin probability, which extends a corresponding result of Wang (2008).
Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood
Fang-Rong Yan,Jin-Guan Lin,Yu Liu
Journal of Biomedicine and Biotechnology , 2011, DOI: 10.1155/2011/875309
Abstract: The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis.
On Complete Convergence for Arrays of Rowwise ρ-Mixing Random Variables and Its Applications
Xing-cai Zhou,Jin-guan Lin
Journal of Inequalities and Applications , 2010, DOI: 10.1155/2010/769201
On Complete Convergence for Arrays of Rowwise -Mixing Random Variables and Its Applications
Zhou Xing-cai,Lin Jin-guan
Journal of Inequalities and Applications , 2010,
Abstract: We give out a general method to prove the complete convergence for arrays of rowwise -mixing random variables and to present some results on complete convergence under some suitable conditions. Some results generalize previous known results for rowwise independent random variables.
Bayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study
Fang-Rong Yan, Yuan Huang, Jun-Lin Liu, Tao Lu, Jin-Guan Lin
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0058369
Abstract: This article provides a fully Bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK) model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data.
Empirical Likelihood Estimation for Population Pharmacokinetic Study Based on Generalized Linear Model
Fang-rong Yan,Jin-guan Lin,Yuan Huang,Jun-lin Liu,Tao Lu
Journal of Applied Mathematics , 2012, DOI: 10.1155/2012/250909
Abstract: To obtain efficient estimation of parameters is a major objective in population pharmacokinetic study. In this paper, we propose an empirical likelihood-based method to analyze the population pharmacokinetic data based on the generalized linear model. A nonparametric version of the Wilk's theorem for the limiting distributions of the empirical likelihood ratio is derived. Simulations are conducted to demonstrate the accuracy and efficiency of empirical likelihood method. An application illustrating our methods and supporting the simulation study results is presented. The results suggest that the proposed method is feasible for population pharmacokinetic data. 1. Introduction The parameter estimation in population pharmacokinetics (PPK) is a significant problem in clinical research. It is a novel problem in pharmacokinetic (PK) study that combines classic PK models with group statistical models. The PPK parameters, including group typical values, fixed effect parameter, interindividual variation, and intraindividual variation, which are the determinant factors of drug concentration in patients, are taken into consideration. PPK is capable of quantitatively describing the effects of different factors in drug metabolism, such as pathology, physiology, and combined medication, then providing guidance on the adjustment of therapeutic regimen, thus increasing the efficacy and safety in a new drug evaluation. Many statistical models have been proposed to fit PPK parameters. The most popular analytic statistical model for PPK data is the random effects model proposed by Larid and Ware [1]. Besides, the classic compartmental model and nonlinear mixed effect model proposed by Sheiner et al. in 1977 [2] are the most commonly used statistical models for PPK. And recently, generalized linear model proposed by Salway and Wakefield in 2008 [3] has received wide attention. In fact nonlinear models are used to estimate parameters of a chosen compartmental model. This model can provide generally good results. However, a disadvantage of the nonlinear models is that it is generally more difficult to fit. From a computational point of view, one also faces the usual challenges associated with nonlinear regression such as choosing starting values, problem with convergence, nonlinear regression diagnostics, and so forth. Other nonlinear models for the analysis of PPK data see [4–6]. Salway and Wakefield [3] proposed a generalized linear model with gamma distribution to deal with PPK data. The GLM is one of the most widely used regression models for statistical analysis. The
Testing for Varying Dispersion in Exponential Family Nonlinear Models with Random Coefficients

LIN Jin-Guan,WUIBo-Cheng,

数学物理学报(A辑) , 2003,
Abstract: 指数族广义非线性随机系数模型是Smith&Heitjan和weietal所研究模型的推广,该文分别在模型离差(dispersion)的权不变和变异时,讨论了指数族广义非线性随机系数模型的变离差的检验问题,得到了score检验统计量,并利用欧洲野兔数据,分别对正态分布模型、Г分布模型和逆高斯分布模型说明检验方法的有效性。
On the Strong Laws for Weighted Sums of ρ -Mixing Random Variables
Xing-Cai Zhou,Chang-Chun Tan,Jin-Guan Lin
Journal of Inequalities and Applications , 2011, DOI: 10.1155/2011/157816
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