The Experts below are selected from a list of 247692 Experts worldwide ranked by ideXlab platform
Weibing Zuo - One of the best experts on this subject based on the ideXlab platform.
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A New Stochastic Restricted Liu Estimator for the Logistic Regression Model
Open Journal of Statistics, 2018Co-Authors: Weibing ZuoAbstract:In order to overcome the well-known multicollinearity problem, we propose a new Stochastic Restricted Liu Estimator in Logistic Regression Model. In the mean square error matrix sense, the new estimation is compared with the Maximum Likelihood Estimation, Liu Estimator Stochastic Restricted Maximum Likelihood Estimator etc. Finally, a numerical example and a Monte Carlo simulation are given to explain some of the theoretical results.
Chenchien Wang - One of the best experts on this subject based on the ideXlab platform.
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matrix variate Logistic Regression Model with application to eeg data
Biostatistics, 2013Co-Authors: Hung Hung, Chenchien WangAbstract:SUMMARY Logistic Regression has been widely applied in the field of biomedical research for a long time. In some applications, the covariates of interest have a natural structure, such as that of a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they must contain useful information regarding the response. If we simply stack the covariate matrix as a vector and fit a conventional Logistic Regression Model, relevant information can be lost, and the problem of inefficiency will arise. Motivated from these reasons, we propose in this paper the matrix variate Logistic (MV-Logistic) Regression Model. The advantages of the MV-Logistic Regression Model include the preservation of the inherent matrix structure of covariates and the parsimony of parameters needed. In the EEG Database Data Set, we successfully extract the structural effects of covariate matrix, and a high classification accuracy is achieved.
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matrix variate Logistic Regression Model with application to eeg data
arXiv: Applications, 2011Co-Authors: Hung Hung, Chenchien WangAbstract:Logistic Regression has been widely applied in the field of biomedical research for a long time. In some applications, covariates of interest have a natural structure, such as being a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they must contain useful information regarding the response. If we simply stack the covariate matrix as a vector and fit the conventional Logistic Regression Model, relevant information can be lost, and the problem of inefficiency will arise. Motivated from these reasons, we propose in this paper the matrix variate Logistic (MV-Logistic) Regression Model. Advantages of MV-Logistic Regression Model include the preservation of the inherent matrix structure of covariates and the parsimony of parameters needed. In the EEG Database Data Set, we successfully extract the structural effects of covariate matrix, and a high classification accuracy is achieved.
Wujibo - One of the best experts on this subject based on the ideXlab platform.
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Modified restricted Liu estimator in Logistic Regression Model
Computational Statistics, 2016Co-Authors: WujiboAbstract:źiray et al. (Commun Stat Simul Comput, 2014) proposed a restricted Liu estimator in Logistic Regression Model with linear restrictions. However, this estimator did not satisfy the linear restricti...
Peter C Austin - One of the best experts on this subject based on the ideXlab platform.
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interpreting the concordance statistic of a Logistic Regression Model relation to the variance and odds ratio of a continuous explanatory variable
BMC Medical Research Methodology, 2012Co-Authors: Peter C Austin, Ewout W SteyerbergAbstract:Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a Logistic Regression Model.
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absolute risk reductions relative risks relative risk reductions and numbers needed to treat can be obtained from a Logistic Regression Model
Journal of Clinical Epidemiology, 2010Co-Authors: Peter C AustinAbstract:Abstract Objective Logistic Regression Models are frequently used in cohort studies to determine the association between treatment and dichotomous outcomes in the presence of confounding variables. In a Logistic Regression Model, the association between exposure and outcome is measured using the odds ratio (OR). The OR can be difficult to interpret and only approximates the relative risk (RR) in certain restrictive settings. Several authors have suggested that for dichotomous outcomes, RRs, RR reductions, absolute risk reductions, and the number needed to treat (NNT) are more clinically meaningful measures of treatment effect. Study Design and Setting We describe a method for deriving clinically meaningful measures of treatment effect from a Logistic Regression Model. This method involves determining the probability of the outcome if each subject in the cohort was treated and if each subject was untreated. These probabilities are then averaged across the study cohort to determine the average probability of the outcome in the population if all subjects were treated and if they were untreated. Results Risk differences, RRs, and NNTs were derived using a Logistic Regression Model. Conclusions Clinically meaningful measures of effect can be derived from a Logistic Regression Model in a cohort study. These methods can also be used in randomized controlled trials when Logistic Regression is used to adjust for possible imbalance in prognostically important baseline covariates.
Pushpakanthie Wijekoon - One of the best experts on this subject based on the ideXlab platform.
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stochastic restricted maximum likelihood estimator in Logistic Regression Model
Open Journal of Statistics, 2015Co-Authors: Varathan Nagarajah, Pushpakanthie WijekoonAbstract:In the presence of multicollinearity in Logistic Regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in Logistic Regression Model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the Logistic Regression Model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the Logistic Regression Model by using Scalar Mean Squared Error (SMSE).
