The Experts below are selected from a list of 97464 Experts worldwide ranked by ideXlab platform
Dianne M. Finkelstein - One of the best experts on this subject based on the ideXlab platform.
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a non parametric maximum likelihood estimator for Bivariate interval censored Data
Statistics in Medicine, 1999Co-Authors: Rebecca A Betensky, Dianne M. FinkelsteinAbstract:: We derive a non-parametric maximum likelihood estimator for Bivariate interval censored Data using standard techniques for constrained convex optimization. Our approach extends those taken for univariate interval censored Data. We illustrate the estimator with Bivariate Data from an AIDS study.
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an extension of kendall s coefficient of concordance to Bivariate interval censored Data
Statistics in Medicine, 1999Co-Authors: Rebecca A Betensky, Dianne M. FinkelsteinAbstract:Non-parametric tests of independence, as well as accompanying measures of association, are essential tools for the analysis of Bivariate Data. Such tests and measures have been developed for uncensored and right censored failure time Data, but have not been developed for interval censored failure time Data. Bivariate interval censored Data arise in AIDS studies in which screening tests for early signs of viral and bacterial infection are done at clinic visits. Because of missed clinic visits, the actual times of first positive screening tests are interval censored. To handle such Data, we propose an extension of Kendall's coefficient of concordance. We apply it to Data from an AIDS study that recorded times of shedding of cytomegalovirus (CMV) and times of colonization of mycobacterium avium complex (MAC). We examine the performance of our proposed measure through a simulation study.
Qingning Zhou - One of the best experts on this subject based on the ideXlab platform.
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a sieve semiparametric maximum likelihood approach for regression analysis of Bivariate interval censored failure time Data
Journal of the American Statistical Association, 2017Co-Authors: Qingning Zhou, Jianguo SunAbstract:ABSTRACTInterval-censored failure time Data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate Data and there exists only limited research on Bivariate Data, especially on regression analysis of Bivariate interval-censored Data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The model provides a great flexibility, in particular including the commonly used proportional hazards model as a special case, and in the approach, Bernstein polynomials are employed. The strong consistency and asymptotic normality of the resulting estimators of regression parameters are established and furthermore, the estimators are shown to be asymptotically efficient. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. Supplementary materials for this article are ...
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a sieve semiparametric maximum likelihood approach for regression analysis of Bivariate interval censored failure time Data
Journal of the American Statistical Association, 2017Co-Authors: Qingning Zhou, Tao HuAbstract:Interval-censored failure time Data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate Data and there exists only limited research on Bivariate Data, especially on regression analysis of Bivariate interval-censored Data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The model provides a great flexibility, in particular including the commonly used proportional hazards model as a special case, and in the approach, Bernstein polynomials are employed. The strong consistency and asymptotic normality of the resulting estimators of regression parameters are established and furthermore, the estimators are shown to be asymptotically efficient. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. Supplementary materials for this article are available online.
Rebecca A Betensky - One of the best experts on this subject based on the ideXlab platform.
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a non parametric maximum likelihood estimator for Bivariate interval censored Data
Statistics in Medicine, 1999Co-Authors: Rebecca A Betensky, Dianne M. FinkelsteinAbstract:: We derive a non-parametric maximum likelihood estimator for Bivariate interval censored Data using standard techniques for constrained convex optimization. Our approach extends those taken for univariate interval censored Data. We illustrate the estimator with Bivariate Data from an AIDS study.
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an extension of kendall s coefficient of concordance to Bivariate interval censored Data
Statistics in Medicine, 1999Co-Authors: Rebecca A Betensky, Dianne M. FinkelsteinAbstract:Non-parametric tests of independence, as well as accompanying measures of association, are essential tools for the analysis of Bivariate Data. Such tests and measures have been developed for uncensored and right censored failure time Data, but have not been developed for interval censored failure time Data. Bivariate interval censored Data arise in AIDS studies in which screening tests for early signs of viral and bacterial infection are done at clinic visits. Because of missed clinic visits, the actual times of first positive screening tests are interval censored. To handle such Data, we propose an extension of Kendall's coefficient of concordance. We apply it to Data from an AIDS study that recorded times of shedding of cytomegalovirus (CMV) and times of colonization of mycobacterium avium complex (MAC). We examine the performance of our proposed measure through a simulation study.
Tao Hu - One of the best experts on this subject based on the ideXlab platform.
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a sieve semiparametric maximum likelihood approach for regression analysis of Bivariate interval censored failure time Data
Journal of the American Statistical Association, 2017Co-Authors: Qingning Zhou, Tao HuAbstract:Interval-censored failure time Data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate Data and there exists only limited research on Bivariate Data, especially on regression analysis of Bivariate interval-censored Data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The model provides a great flexibility, in particular including the commonly used proportional hazards model as a special case, and in the approach, Bernstein polynomials are employed. The strong consistency and asymptotic normality of the resulting estimators of regression parameters are established and furthermore, the estimators are shown to be asymptotically efficient. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. Supplementary materials for this article are available online.
Jianguo Sun - One of the best experts on this subject based on the ideXlab platform.
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a sieve semiparametric maximum likelihood approach for regression analysis of Bivariate interval censored failure time Data
Journal of the American Statistical Association, 2017Co-Authors: Qingning Zhou, Jianguo SunAbstract:ABSTRACTInterval-censored failure time Data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate Data and there exists only limited research on Bivariate Data, especially on regression analysis of Bivariate interval-censored Data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The model provides a great flexibility, in particular including the commonly used proportional hazards model as a special case, and in the approach, Bernstein polynomials are employed. The strong consistency and asymptotic normality of the resulting estimators of regression parameters are established and furthermore, the estimators are shown to be asymptotically efficient. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. Supplementary materials for this article are ...