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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
Miin-Shen Yang, Hwei-Ming Chen
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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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Regression analysis of logistic model with latent variables
Statistics in Medicine, 2023We propose a joint modeling approach to investigating the effects of socialâpsychological 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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Attitudes towards accounting and intention to major in accounting: a logistic regression analysis
Journal of Accounting in Emerging Economies, 2018Purpose The purpose of this paper is to examine the attitude of business students towards the accounting profession and investigate the relationship between studentsâ attitude and their intention to pursue a degree in accounting.
Rita Amoah Bekoe +4 more
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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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, 2020
In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN).
Pushparenu Bhattacharjee +2 more
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In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN).
Pushparenu Bhattacharjee +2 more
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Logistic Regression Model Optimization and Case Analysis
International Conference on Computer Science and Network Technology, 2019Traditional logistic regression analysis is widely used in the binary classification problem, but it has many iterations and it takes a long time to train large amounts of data, which is not applicable.
Xiaonan Zou +3 more
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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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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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