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FUZZY CLASS LOGISTIC REGRESSION ANALYSIS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that
Yang, Miin-Shen, Chen, Hwei-Ming
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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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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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LOGISTIC REGRESSION IN SURVIVAL ANALYSIS
American Journal of Epidemiology, 1985Logistic regression has been applied to numerous investigations that examine the relationship between risk factors and various disease events. Recently, the ability to consider the time element of event occurrences by proportional hazards models has meant that logistic regression has played a less important role in the analysis of survival data.
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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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Multinomial Logistic Regression Analysis
2017The usage of mixed methods approach on qualitative data has been exemplified in this chapter. The chapter presents the relevance of using multinomial regression approach in the study and discusses its results. The chapter is insightful for readers looking forward to learning practical applications of quantitative techniques on qualitative data.
Nausheen Nizami, Narayan Prasad
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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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Choosing Between Logistic Regression and Discriminant Analysis
Journal of the American Statistical Association, 1978Abstract Classifying an observation into one of several populations is discriminant analysis, or classification. Relating qualitative variables to other variables through a logistic cdf functional form is logistic regression. Estimators generated for one of these problems are often used in the other.
S. James Press, Sandra Wilson
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Odontometric sex assessment from logistic regression analysis
International Journal of Legal Medicine, 2010Odontometric sex assessment is considered a useful adjunct to more robust predictors such as pelvic and cranial bones, and discriminant function analysis (DA) has been widely applied in dental sex assessment. Logistic regression analysis (LRA) is considered a better alternative, although still untested in odontometric sex prediction.
Ashith B, Acharya +2 more
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Random Effects Logistic Regression Analysis with Auxiliary Covariates
Biometrics, 2002We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the random effects model using the best linear unbiased ...
Zhou, Haibo, Chen, Jianwei, Cai, Jianwen
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