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Volume 7, Issue 2 2008 Article 9 Statistical Applications in Genetics and Molecular Biology COMPETITION ON CLINICAL MASS SPECTROMETRY BASED PROTEOMIC DIAGNOSIS Empirical Bayes Logistic Regression Foteini Strimenopoulou, University of Kent Philip J. Brown, University of Kent Recommended Citation: Strimenopoulou, Foteini and Brown, Philip J. (2008) "Empirical Bayes Logistic Regression," Statistical Applications in Genetics and Molecular Biology: Vol. 7: Iss. 2, Article 9. DOI: 10.2202/1544-6115.1359 Empirical Bayes Logistic Regression Foteini Strimenopoulou and

Volume 6, Issue 1 2007 Article 6 Statistical Applications in Genetics and Molecular Biology Sparse Logistic Regression with Lp Penalty for Biomarker Identification Zhenqiu Liu, University of Maryland Feng Jiang, University of Maryland Guoliang Tian, University of Maryland Suna Wang, University of Maryland School of Medicine Fumiaki Sato, Johns Hopkins University School of Medicine Stephen J. Meltzer, Johns Hopkins University School of Medicine Ming Tan, University of Maryland Greenebaum Cancer Center Recommended Citation: Liu, Zhenqiu; Jiang, Feng; Tian, Guoliang

Volume 5, Issue 1 2009 Article 3 The International Journal of Biostatistics Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression James A. Hanley, McGill University Olli S. Miettinen, McGill University Recommended Citation: Hanley, James A. and Miettinen, Olli S. (2009) "Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression," The International Journal of Biostatistics: Vol. 5: Iss. 1, Article 3. DOI: 10.2202/1557-4679.1125 Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression James A. Hanley and Olli S

that soar or sink depending on a few chance plays. A better measure of performance would be the partial effect of each player, having controlled for the contributions of teammates, opponents and possibly other variables. To this end, we propose a logistic regression model to estimate the credit or blame that should be apportioned to each player when a goal is scored. In keeping with the spirit of plus-minus (and using the same publicly available data), we focus on the list of players on the ice for each goal as our basic unit of analysis. Briefly, denote by q i

generalized linear models with assuming independence. Höhle [ 21 ] proposed a CUSUM control chart method based on the generalized likelihood ratio statistic for sequential change-point detection in regression models for categorical time series. The goal of this article is to propose a sequential change-point detection procedure based on the partial likelihood score process for the coefficients of multinomial logistic regression model for categorical time series. Score test for the sequential detection of changes in time series models has been studied by many

on learning. In the current study, within the framework of an observational methodology design ( Anguera, 1979 ), we applied a series of logistic regression models to investigate whether F-7 or F-8 was better suited to the learning needs of children aged 8-10 years moving up from futsal (F-5). The matches analyzed took place at the end of the 2011-2012 season between teams about to move up from F-5 to the newly introduced F-8 format. Considering the multiple dichotomous variables of interest in studies of sport, together with the potential offered by logistic

Clin Chem Lab Med 2012;50(1):73–76 © 2011 by Walter de Gruyter • Berlin • Boston. DOI 10.1515/CCLM.2011.726 C-statistics versus logistic regression for assessing the performance of qualitative diagnostic tests Cesar Romero 1 , Leonard te Velde 2 , Huibert Ponsen 2 and Ton J. Cleophas 2,3, * 1 Hospital San Carlos , Playa del Carmen , Mexico 2 Department of Medicine , Albert Scheitzer Hospital, Dordrecht , The Netherlands 3 European College of Pharmaceutical Medicine , Lyon , France Abstract Background: Qualitative diagnostic tests

). Applied logistic regression John Wiley & Sons. 4. Dobek A., Moliński K., Skotarczak E. Porównanie mocy testów Rao’s score, Walda i ilorazu największej wiarogodności dla tablicy kontyngencji wymiaru (2xc). Biometrical Letters, 2015. 5. Domencich, Thomas A., and Daniel McFadden. Urban travel demand-a behavioral analysis. No. Monograph. 1975. 6. Davis, B. C. “Factors affecting choice of travel mode.” Australian Road Research Board Conference Proc. Vol. 7. No. 2. 1975.

Volume 6, Issue 1 2007 Article 17 Statistical Applications in Genetics and Molecular Biology A Method for Meta-Analysis of Case-Control Genetic Association Studies Using Logistic Regression Pantelis G. Bagos, Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece and Department of Biomedical Informatics, University of Central Greece, Lamia, Greece Georgios K. Nikolopoulos, Hellenic Centre for Disease Control and Prevention Recommended Citation: Bagos, Pantelis G. and Nikolopoulos, Georgios K. (2007) "A Method for Meta

the model are correlated to an extent that individual regression coefficient estimates become unreliable. When the regressors have an exact linear relationship, they are said to be perfectly collinear. When the relationship between the predictor variables is almost linear (but not exact), this results in the phenomenon known as near - multicollinearity , the problem specifically addressed in this paper and frequently encountered in the applied literature. The problems traditionally associated with near-multicollineairty in the logistic regression model are similar