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Showing 1 to 20 of 332 for “"logistic regression model."”.
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Optimal designs for a bivariate logistic regression model
… interest are efficacy and toxicity. These can be modeled as a bivariate quantal 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 …
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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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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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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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Evaluating Sources of Arsenic in Groundwater in Virginia using a Logistic Regression Model
For this study, I have constructed a logistic regression model, using existing datasets of environmental parameters to predict the probability of As concentrations above 5 parts per billion (ppb) in Virginia groundwater and to evaluate if geologic or other characteristics are linked to elevated As …
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Dealing with measurement error in covariates with special reference to logistic regression model: a flexible parametric approach
… response or outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely we measure the relevant covariates. In many instances, we can not measure some of the covariates accurately, rather we can measure noisy versions of them. In statistical …
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Improving Lung Cancer Risk Prediction: Integration of Novel Predictors and Modelling Using Machine Learning Random Forest versus the Validated PLCOm2012 Logistic Regression Model
… was to develop a superior LC risk prediction model compared to the current established Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial 2012 model (PLCOm2012) for selection of high-risk individuals for LC screening. Development of the risk models was done using data from the …
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Genome-wide association study for non-normally distributed traits: A case study for stalk lodging in maize
… is that the assumptions underlying the linear model typically used to conduct the analysis are often violated in nature, and in such cases, the linear model is inappropriate to use. Alternatively, the mixed logistic regression model is well-suited for a genome-wide association study of …
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Predictors of Re-Hospitalization for Home Healthcare Patients
… patients. Secondary data analysis using logistic regression was conducted on retrospective data from OASIS collected during the time period of July 1, 2006 to June 30, 2007. This study was conducted in a Medicare certified Home Health organization that is part of the largest public health …
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Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
… Phase II clinical trial designs usually employ a logistic regression model to analyze the efficacy of a new drug and, therefore, assumes monotone dose-response relationship. Also, the logistic regression model requires the response to be categorical and, thus, it is not applicable for continuous …
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Automatic prediction of solar flares and super geomagnetic storms
… on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms …
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Case-Mix Adjustment of Adherence Based Pharmacy Quality Indicator Scores
… study examines the performance of a classical logistic regression model containing only patient characteristics and a random-effect model including patient characteristics and a pharmacy-specific effect in predicting medication adherence. These models were used to compute three different …
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The Influence of Higher Education on Promotional Outcomes in the New Jersey State Police
… group analyses were conducted using binary logistic regression modelling. The participant data examined in this study, which represents a total population sample, pertained to 3,515 enlisted members of the New Jersey State Police considered for promotion during one, or both, of the …
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A Research on Corporate Bond Defaults in the Chinese Market
… fixed income market, this thesis builds up a logistic regression model mainly consisting of both financial condition variables and financial report quality variables. The analysis suggests the degree of effect for different variables and thus provides a reference for credit risk assessment. …
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Clinicopathological correlation in erythema induratum
… inter-rater variability. A multi-variate logistic regression model determined the clinical and histological features that contribute most to an accurate diagnosis. Results - After assessing the clinical picture 48.8% of the EI cases and 74% of the EN cases were correctly diagnosed. With …
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Two Methodologies: How Well Can Universities Predict Retention
… new methodologies such as the Classification and Regression Tree (CART) have proven to yield significant results in a variety of research fields. As these new statistical methodologies emerge, it is always worthwhile to compare the modern approaches with the longstanding classical statistical …
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Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.
Researchers often analyze data assuming models with constrained parameters. Order constrained parameters are of particular interest. In this dissertation, we examine three Bayesian models which incorporate ordered parameters. We investigate ordered differential response misclassification in a …
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