Browsing by Author "Wang, Weijing"

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A goodness-of-fit test for Archimedean copula models in the presence of right censoring
(2010-12-01)
Emura, Takeshi
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Lin, Chien-Wei
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Wang, Weijing
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統計學研究所
;
Institute of Statistics
A goodness-of-fit testing procedure for Archimedean copula (AC) models is developed based on right-censored data. The proposed approach extends an existing method, which is suitable for the Clayton model, to general AC models. Asymptotic properties of the proposed test statistics under the true model assumption are derived. Simulation analysis shows that the proposed test has reasonable performance. Finally, two real data examples are analyzed for illustrative purposes. (C) 2010 Elsevier B.V. All rights reserved.
Comments on: Inference in multivariate Archimedean copula models
(2011-08-01)
Wang, Weijing
;
Emura, Takeshi
;
統計學研究所
;
Institute of Statistics
Inference for bivariate survival data by copula models adjusted for the boundary effect
(2007-01-01)
Ding, Aidong Adam
;
Wang, Weijing
;
統計學研究所
;
Institute of Statistics
Copula models describe the dependence structure of two random variables separately from their marginal distributions and hence are particularly useful in studying the association for bivariate survival data. Semiparametric inference for bivariate survival data based on copula models has been studied for various types of data, including complete data, right-censored data, and current status data. This article discusses the boundary effect on these inference procedures, a problem that has been neglected in the previous literature. Specifically, asymptotic distribution of the association estimator on the boundary of parameter space is derived for one-dimensional copula models. The boundary properties are applied to test independence and to study the estimation efficiency. Simulation study is conducted for the bivariate right-censored data and current status data.
Inference for bivariate survival data by copula models adjusted for the boundary effect
(2007-01-01)
Ding, Aidong Adam
;
Wang, Weijing
;
統計學研究所
;
Institute of Statistics
Copula models describe the dependence structure of two random variables separately from their marginal distributions and hence are particularly useful in studying the association for bivariate survival data. Semiparametric inference for bivariate survival data based on copula models has been studied for various types of data, including complete data, right-censored data, and current status data. This article discusses the boundary effect on these inference procedures, a problem that has been neglected in the previous literature. Specifically, asymptotic distribution of the association estimator on the boundary of parameter space is derived for one-dimensional copula models. The boundary properties are applied to test independence and to study the estimation efficiency. Simulation study is conducted for the bivariate right-censored data and current status data.
Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data
(2015-01-01)
Ding, Aidong Adam
;
Hsieh, Jin-Jian
;
Wang, Weijing
;
統計學研究所
;
Institute of Statistics
Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.
Marginal Regression Analysis for Semi-Competing Risks Data Under Dependent Censoring
(2009-09-01)
Ding, A. Adam
;
Shi, Guangkai
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Wang, Weijing
;
Hsieh, Jin-Jian
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統計學研究所
Multiple events data are commonly seen in medical applications. There are two types of events, namely terminal and non-terminal. Statistical analysis for non-terminal events is complicated due to dependent censoring. Consequently, joint modelling and inference are often needed to avoid the problem of non-identifiability. This article considers regression analysis for multiple events data with major interest in a non-terminal event such as disease progression. We generalize the technique of artificial censoring, which is a popular way to handle dependent censoring, under flexible model assumptions on the two types of events. The proposed method is applied to analyse a data set of bone marrow transplantation.
Modelling hierarchical clustered censored data with the hierarchical Kendall copula
(2019-06-01)
Su, Chien-Lin
;
Neslehova, Johanna G.
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Wang, Weijing
;
統計學研究所
;
Institute of Statistics
This article proposes a new model for right-censored survival data with multi-level clustering based on the hierarchical Kendall copula model of Brechmann (2014) with Archimedean clusters. This model accommodates clusters of unequal size and multiple clustering levels, without imposing any structural conditions on the parameters or on the copulas used at various levels of the hierarchy. A step-wise estimation procedure is proposed and shown to yield consistent and asymptotically Gaussian estimates under mild regularity conditions. The model fitting is based on multiple imputation, given that the censoring rate increases with the level of the hierarchy. To check the model assumption of Archimedean dependence, a goodness-of test is developed. The finite-sample performance of the proposed estimators and of the goodness-of-fit test is investigated through simulations. The new model is applied to data from the study of chronic granulomatous disease. The Canadian Journal of Statistics 47: 182-203; 2019 (c) 2019 Statistical Society of Canada
Nonparametric maximum likelihood estimation for dependent truncation data based on copulas
(2012-09-01)
Emura, Takeshi
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Wang, Weijing
;
統計學研究所
;
Institute of Statistics
Truncation occurs when the variable of interest can be observed only if its value satisfies certain selection criteria. Most existing methods for analyzing such data critically rely on the assumption that the truncation variable is quasi-independent of the variable of interest. In this article, the authors propose a likelihood-based inference approach under the assumption that the dependence structure of the two variables follows a general form of copula model. They develop a model selection method for choosing the best-fitted copula among a broad class of model alternatives, and they derive large-sample properties of the proposed estimators, including the inverse Fisher information matrix. The treatment of ties is also discussed. They apply their methods to the analysis of a transfusion-related AIDS data set and compare the results with existing methods. Simulation results are also provided to evaluate the finite-sample performances of all the competing methods. (C) 2012 Elsevier Inc. All rights reserved.
Regression analysis based on semicompeting risks data
(2008)
Hsieh, Jin-Jian
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Wang, Weijing
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Ding, A. Adam
;
統計學研究所
;
Institute of Statistics
Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death. The methodology proposed is developed for discrete covariates under two types of assumption. First, separate copula models are assumed for each covariate group and then a flexible regression model is imposed on the progression time which is of major interest. Model checking procedures are also proposed to help to choose a best-fitted model. Under a two-sample setting, Lin and co-workers proposed a competing method which requires an additional marginal assumption on the terminal event and implicitly assumes that the dependence structures in the two groups are the same. Using simulations, we compare the two approaches on the basis of their finite sample performances and robustness properties under model misspecification. The method proposed is applied to a bone marrow transplant data set.
REGRESSION ANALYSIS FOR CUMULATIVE INCIDENCE PROBABILITY UNDER COMPETING RISKS
(2009-04-01)
Chang, Wei-Hwa
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Wang, Weijing
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統計學研究所
;
Institute of Statistics
The cumulative incidence function provides intuitive summary information about competing risks data. Via a mixture decomposition of this function, we study how covariates affect the cumulative incidence probability of a particular failure type at a chosen time point. Without specifying the corresponding failure time distribution, several inference methods are constructed based on imputation and weighting approaches. Large sample properties of the proposed estimators are derived, and their finite sample performances are examined via simulations. For illustrative purposes, the proposed methods are applied to well-known heart transplant data and compared with the analysis of Larson and Dinse (1985). In the on-line Supplement, we also apply our methods to analyze the Taiwan nationwide laboratory-confirmed severe acute respiratory syndrome (SARS) database.
Regression Analysis for Recurrent Events Data under Dependent Censoring
(2011-09-01)
Hsieh, Jin-Jian
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Ding, A. Adam
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Wang, Weijing
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統計學研究所
;
Institute of Statistics
Recurrent events data are commonly seen in longitudinal follow-up studies. Dependent censoring often occurs due to death or exclusion from the study related to the disease process. In this article, we assume flexible marginal regression models on the recurrence process and the dependent censoring time without specifying their dependence structure. The proposed model generalizes the approach by Ghosh and Lin (2003, Biometrics 59, 877-885). The technique of artificial censoring provides a way to maintain the homogeneity of the hypothetical error variables under dependent censoring. Here we propose to apply this technique to two Gehan-type statistics. One considers only order information for pairs whereas the other utilizes additional information of observed censoring times available for recurrence data. A model-checking procedure is also proposed to assess the adequacy of the fitted model. The proposed estimators have good asymptotic properties. Their finite-sample performances are examined via simulations. Finally, the proposed methods are applied to analyze the AIDS linked to the intravenous experiences cohort data.
SEMI-PARAMETRIC INFERENCE FOR COPULA MODELS FOR TRUNCATED DATA
(2011-01-01)
Emura, Takeshi
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Wang, Weijing
;
Hung, Hui-Nien
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統計學研究所
;
Institute of Statistics
We investigate the dependent relationship between two failure time variables that truncate each other. Chaieb, Rivest, and Abdous (2006) proposed a semi-parametric model under the so-called "semi-survival" Archimedean-copula assumption and discussed estimation of the association parameter, the truncation probability, and the marginal functions. Here the same model assumption is adopted but different inference approaches are proposed. For estimating the association parameter, we extend the conditional likelihood approach (Clayton (1978)) and the two-by-two table approach (Wang (2003)) to dependent truncation data. We further show that the three estimators, including that proposed by Chaieb, Rivest, and Abdous (2006), differ in weights. The likelihood approach provides the formula for a good weight. Large sample properties of the proposed methods are established by applying the functional delta method, which can handle estimating functions that are not in the form of U-statistics. Analytic formulae for the asymptotic variance estimators are provided. Two competing methods are compared via simulations, and applied to the transfusion-related AIDS data.
Semiparametric inference for an accelerated failure time model with dependent truncation
(2016-10)
Emura, Takeshi
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Wang, Weijing
;
統計學研究所
;
Institute of Statistics
Truncated data are commonly seen in studies of biomedicine, epidemiology, astronomy and econometrics. Existing regression methods for analyzing left-truncated and right-censored data have been developed under the assumption that the lifetime variable of interest is independent of both truncation and censoring variables. In this article, we propose a semiparametric accelerated failure time model that incorporates both covariates and the truncation variable as regressors. The proposed model utilizes the truncation information in statistical modeling and hence allows for dependent truncation. For estimation, we develop a set of estimating equations constructed from the log-rank and quasi-independence test statistics. We show that the resulting estimators are consistent and asymptotically normal. We also propose an explicit formula for variance estimation based on a kernel method. Finite-sample performances of the estimators are studied by simulations. The proposed methodology is applied to analyze a real data for illustration.
Testing quasi-independence for truncation data
(2010-01-01)
Emura, Takeshi
;
Wang, Weijing
;
交大名義發表
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National Chiao Tung University
Quasi-independence is a common assumption for analyzing truncated data. To verify this condition, we propose a class of weighted log-rank type statistics that include existing tests proposed by Tsai (1990) and Martin and Betensky (2005) as special cases. To choose an appropriate weight function that may lead to a more power test, we derive a score test when the dependence Structure under the alternative hypothesis is modeled via the odds ratio function proposed by Chaieb, Rivest and Abdous (2006). Asymptotic properties of the proposed tests are established based oil the functional delta method which can handle more general Situations than results based oil rank-statistics or U-statistics. Extension of the proposed methodology under two different censoring settings is also discussed. Simulations are performed to examine finite-sample performances of the proposed method and its competitors. Two datasets are analyzed for illustrative purposes. (C) 2009 Elsevier Inc. All rights reserved.