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Showing 1 to 20 of 2758 for “"Logistic Regression"”.
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Logistic Regression for Prospectivity Modeling
… a method for automated model selection using a logistic regression model in the context of prospectivity modeling, i.e. the exploration of minearlisations. This kind of data is characterized by a rare positive event and a large dataset. We adapted and combined the two statistical measures Wald …
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Bayesian Inference in Nonparametric Logistic Regression
We consider the problem of regressing a dichotomous response variable on a predictor variable. Our interest is in modelling the probability of occurrence of the response as a function of the predictor variable, and in inferences about the estimated function. The log-odds (logit) of the probability …
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Monitoring Parameter Change in Autocorrelated Logistic Regression
… for monitoring changes in the coefficients of logistic regression model with AR( p)-type structure. The objective of this thesis is (a) to use Monte Carlo experiments to evaluate the average stopping times, probability of false alarm, and power of the proposed procedure; (b) to illustrate the …
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Properties Of Logistic Regression Models With Correlated Observations
The correlated logistic regression model, a new model for correlated binary observations in the presence of covariates, is introduced and its relationship with other logistic models established. Comparisons are made with other models for correlated binary outcomes, particularly those that allow …
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Modeling the NCAA Tournament Through Bayesian Logistic Regression
… variables. We use an MCMC approach and logistic regression along with several model selection techniques to arrive at models for predicting the winner of each game. When given the 63 actual games in the 2012 tournament, eight of our models performed as well as Pomeroy's rating system and …
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Some sequential estimation problems in logistic regression models
… i = 1,2,\cdots,$ be a random sample satisfying a logistic regression model; that is, for each i, log($P(Y\sb{i}$ = $1\vert{\bf X}\sb{i})/P(Y\sb{i}$ = 0$\vert{\bf X}\sb{i})\rbrack$ = ${\bf X}\sbsp{i}{T}\beta\sb0,$ where $Y\sb{i}\in\{$0,1$\},$ ${\bf X}\sb{i}\in{\bf R}\sp{p}$ and $\beta\sb0\in{\bf …
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Optimal designs for a bivariate logistic regression model
… response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a …
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Power analysis for a mixed effects logistic regression model
In herd health studies, the mixed effects logistic regression model with random herd effects are commonly used for modeling clustered binary data. These models are well developed and widely used in the literature, among which is the logistic-normal regression model. In contrast to the rich …
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Optimal experimental designs for two-variable logistic regression models
Binary response data is often modeled using the logistic regression model. Experimental design theory for the logistic model appears to be increasingly important as experimentation becomes more complex and expensive. The optimal design work is extremely valuable in areas such as biomedical and …
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Berkson's method vs. maximum likelihood in the logistic regression model
Call number: LD2668 .R4 STAT 1988 R33
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Berkson's method vs. maximum likelihood in the logistic regression model
Call number: LD2668 .R4 STAT 1988 R33
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Modifications and extensions of the logistic regression and Cox model
… such as linear models for continuous outcomes, logistic models for binary outcomes and the Cox model for time-to-event data. In epidemiological, medical, biological, societal and economic studies, the logistic regression is widely used to describe the relationship between a response variable as …
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Varying Coefficients in Logistic Regression with Applications to Marketing Research
… data, we tackle these questions and propose a logistic regression model in which coefficients can vary based on a consumer's purchase history. We also introduce a two-step procedure for model selection that uses a group LASSO penalty to decide which are informative and which variables need …
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Septic shock : providing early warnings through multivariate logistic regression models
(cont.) The EWS models were then tested in a forward, casual manner on a random cohort of 500 ICU patients to mimic the patients' stay in the unit. The model with the highest performance achieved a sensitivity of 0.85 and a positive predictive value (PPV) of 0.70. Of the 35 episodes of hypotension …
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Investigating Factors Associated with Burglary Crime Analysis using Logistic Regression Modeling
<p>This study conducted a logistic regression to determine the relationship of factors associated with burglary to determine the variables necessary to predict criminal activity. Predictors utilized in the study; included time of day, day of week, connectors, barriers, and repeat victimization. …
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Variable selection in logistic regression, with special application to medical data
… for prediction and estimation problems in logistic regression will be described. The procedures will be applied to medical data sets. On the basis of the literature review as well as the applications to examples, strengths and weaknesses of the approaches will be identified. The procedures …
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Optimal one and two-stage designs for the logistic regression model
Binary response data is often modeled using the logistic regression model, a well known nonlinear model. Designing an optimal experiment for this nonlinear situation poses some problems not encountered with a linear model. The application of several optimality design criteria to the logistic …
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Large-Sample Logistic Regression with Latent Covariates in a Bayesian Networking Context
… problem of predicting student retention using logistic regression when the most important covariates such as the college variables are latent, but the network structure is known. This network structure specifies the relationship between pre-college to college variables and then from college to …
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The utility of hierarchical logistic regression for predicting repeated measures binary responses
This report will employ a hierarchical logistic regression model with mixed effects as an alternative to the traditional analysis of variance (ANOVA) approach that is often used when repeated observations are taken for each treatment. In the case of a binary response variable, ANOVA approaches …
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Fixed versus Mixed Parameterization in Logistic Regression Models: Application to Meta-Analysis
… generalized mixed model (GLMM), and conditional logistic regression (clogit) are compared in a meta-analysis of 43 studies assessing the effect of diet on cancer incidence in rats. We also perform simulation studies to assess distributional behavior of regression estimates and tests of fit. Other …
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