Results 291 to 300 of about 1,790,008 (339)
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2021
This chapter covers the logistic regression concept and implementation in a structured way. Preceding chapters introduced supervised learning and concentrated on the parametric method. In supervised learning, we present a model with a set of correct answers, and we then allow a model to predict unseen data.
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This chapter covers the logistic regression concept and implementation in a structured way. Preceding chapters introduced supervised learning and concentrated on the parametric method. In supervised learning, we present a model with a set of correct answers, and we then allow a model to predict unseen data.
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Regression analysis of logistic model with latent variables
Statistics in Medicine, 2023We propose a joint modeling approach to investigating the effects of social‐psychological factors on the onset of depression. The proposed model comprises two components. The first one is a confirmatory factor analysis model that summarizes latent factors through multiple correlated observed variables. The second one is a logistic regression model that
Yuan Ye +3 more
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2020
This chapter describes the logistic regression. It is a powerful statistical technique for examining the assumed causal relationships between a set of independent variables on the probability of occurrence of an event which is one category of a binary dependent variable. Here, the effects of the variables are presented through odds, which are the ratio
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This chapter describes the logistic regression. It is a powerful statistical technique for examining the assumed causal relationships between a set of independent variables on the probability of occurrence of an event which is one category of a binary dependent variable. Here, the effects of the variables are presented through odds, which are the ratio
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On logistic regression analysis of dichotomized responses
Pharmaceutical Statistics, 2016We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution.
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1990
In chapter 8 the connection to log-linear models for contingency tables was stressed. The direct connection to regression analysis for continuous response variables will now be brought more clearly into focus. Assume as before that the response variable is binary and that it is observed together with p explanatory variables.
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In chapter 8 the connection to log-linear models for contingency tables was stressed. The direct connection to regression analysis for continuous response variables will now be brought more clearly into focus. Assume as before that the response variable is binary and that it is observed together with p explanatory variables.
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Comparing Penalized Regression Analysis of Logistic Regression Model with Multicollinearity
Proceedings of the 2019 2nd International Conference on Mathematics and Statistics, 2019The goal of this research is to estimate the parameter of the logistic regression model by penalized regression analysis which consisted of ridge regression, lasso, and elastic net method. The logistic regression is considered between a binary dependent variable and 3 and 5 independent variables.
Autcha Araveeporn +1 more
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Pairwise interaction analysis of logistic regression models
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016An important problem in the field of bioinformaties is to identify interactive effects among profiled variables for outcome prediction. In this paper, a simple logistic regression model with pairwise interactions among a set of binary covariates is considered. Modeling the structure of the interactions by a graph, our goal is to recover the interaction
Easton Li Xu +3 more
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Logistic regression analysis of sample survey data
Biometrika, 1987Standard chi-squared, \(X^ 2\), or likelihood ratio, \(G^ 2\), test statistics for logistic regression analysis, involving a binary response variable, are adjusted to take account of the survey design. These adjustments are based on certain generalized design effects.
Roberts, G., Rao, J. N. K., Kumar, S.
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ANALYSIS OF LOGISTIC REGRESSION FOR ERGODIC PREDICTOR VECTORS
Statistics & Risk Modeling, 1994Summary: A random effects logistic regression model, where the explanatory variables follow a time series is proposed. The asymptotic normality of the maximum likelihood estimates of the logistic regression coefficients is established under certain conditions. The analysis of such data can be carried out with the standard statistical packages.
Selukar, Rajesh, Kulkarni, Pandu
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Logistic regression and CART in the analysis of multimarker studies
Clinica Chimica Acta, 2008The rapid development of new biomarkers increasingly motivates multimarker studies to assess/compare the value of different markers for risk stratification or diagnostic prediction. Analysis of these studies is usually governed by logistic regression (LR) which is however often applied quite uncritically resulting in unclear or even deceptive results ...
Muller, Reinhold, Moeckel, Martin
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