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Biometrics, 1995
Truncated binary data occurs when a group of individuals, who each have a binary response, are observed only if one or more of the individuals has a positive response. In this paper the group will be taken to be a motor vehicle accident and the binary response taken to be survival or death.
T J, O'Neill, S C, Barry
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Truncated binary data occurs when a group of individuals, who each have a binary response, are observed only if one or more of the individuals has a positive response. In this paper the group will be taken to be a motor vehicle accident and the binary response taken to be survival or death.
T J, O'Neill, S C, Barry
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American Journal of Physical Medicine & Rehabilitation, 2000
Medical rehabilitation researchers are increasingly interested in investigating complex, multivariate problems. Logistic regression analysis is a statistical tool that may be useful in exploring the relationship between multiple explanatory factors and a categorical outcome.
G V, Ostir, T, Uchida
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Medical rehabilitation researchers are increasingly interested in investigating complex, multivariate problems. Logistic regression analysis is a statistical tool that may be useful in exploring the relationship between multiple explanatory factors and a categorical outcome.
G V, Ostir, T, Uchida
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Statistica Neerlandica, 1988
A review is given of the development of logistic regression as a multi‐purpose statistical tool.A historical introduction shows several lines culminating in the unifying paper of Cox (1966), in which theory as developed in the field of bio‐assay is shown to be applicable to designs as discriminant‐analysis and case‐control study.
van Houwelingen, J. C., le Cessie, S.
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A review is given of the development of logistic regression as a multi‐purpose statistical tool.A historical introduction shows several lines culminating in the unifying paper of Cox (1966), in which theory as developed in the field of bio‐assay is shown to be applicable to designs as discriminant‐analysis and case‐control study.
van Houwelingen, J. C., le Cessie, S.
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IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
We propose an online learning algorithm for training a logistic regression model on nonstationary classification problems. The nonstationarity is captured by modelling the weights in a logistic regression classifier as evolving according to a first order Markov process.
William D. Penny, Stephen J. Roberts
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We propose an online learning algorithm for training a logistic regression model on nonstationary classification problems. The nonstationarity is captured by modelling the weights in a logistic regression classifier as evolving according to a first order Markov process.
William D. Penny, Stephen J. Roberts
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Advances in Data Analysis and Classification, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Che Ngufor, Janusz Wojtusiak
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Che Ngufor, Janusz Wojtusiak
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Ecology, 1999
We show how to use logistic regression in situations where the phenomenon under consideration is periodic, i.e., follows a cyclic pattern. The method is illustrated by using data for the snail Potamopyrgus antipodarum, which exhibits a 24-hr periodic pattern of foraging. Snails are found on the top or bottom of rocks, depending on time of day.
Bernard D. Flury, Edward P. Levri
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We show how to use logistic regression in situations where the phenomenon under consideration is periodic, i.e., follows a cyclic pattern. The method is illustrated by using data for the snail Potamopyrgus antipodarum, which exhibits a 24-hr periodic pattern of foraging. Snails are found on the top or bottom of rocks, depending on time of day.
Bernard D. Flury, Edward P. Levri
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International Journal of Injury Control and Safety Promotion, 2018
Simple and multiple linear regression models study the relationship between a single continuous dependent variable Y and one or multiple independent variables X, respectively (Bangdiwala, 2018a, 20...
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Simple and multiple linear regression models study the relationship between a single continuous dependent variable Y and one or multiple independent variables X, respectively (Bangdiwala, 2018a, 20...
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Empirical Bayes Logistic Regression
Statistical Applications in Genetics and Molecular Biology, 2008We construct a diagnostic predictor for patient disease status based on a single data set of mass spectra of serum samples together with the binary case-control response. The model is logistic regression with Bernoulli log-likelihood augmented either by quadratic ridge or absolute L1 penalties.
Strimenopoulou, Foteini +1 more
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Bayesian Multivariate Logistic Regression
Biometrics, 2004SummaryBayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters.
O'Brien, Sean M., Dunson, David B.
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