Mohd Tahir IsmailSiti Nor Shadila AliasNational Symposium on Mathematical SciencesIsmail, M., & Alias, S. (2014). Binary logistic regression modeling: Measuring the probability of relapse cases among drug addicts. AIP Conference Proceedings, 1605, 792-797....
Results from Binary Logistic Regression The BLR method provides more details compared to neural networks including CNN. Table 6 provides the modeling results from the BLR method. Eventually, after setting the significance level at 0.05 (p-value < 0.05), thirteen variables among all varied varia...
Use logistic regression to model a binomial, multinomial or ordinal variable using quantitative and/or qualitative explanatory variables.
Konstantinos FokianosSpringer USKedem, B. and Fokanios, K. (2002). Regression models for binary time series. In Modeling uncertainty. vol. 46 of Internat. Ser. Oper. Res. Management Sci. Kluwer Academic Publisher, Boston, MA. pp. 185-199....
By exploiting the theory of skew-symmetric distributions, we generalise existing results in sensitivity analysis by providing the analytic expression of the bias induced by marginalization over an unobserved continuous confounder in a logistic regression model. The expression is approximated and mimics Cochr...
We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies ... DL Miglioretti,PJ Heagerty - 《Biostatistics》 被引量: 92发表: 2004年 Multilevel Factor Analysis and Structural Equation Modeling of Dail...
Therefore, our approach providesscientists with an appropriate statistical modeling framework for analyses involvingagronomically important binary traits.Keywords: Logistic regression; Complete separation; Quantitative genetics1 IntroductionThe application of quantitative genetics approaches to crops has facilitated ...
On the other hand, if one is interested in modeling the relationship between the true dose and the outcome or in predicting the outcome given a true dose, then the errors-in-variables model is needed. Madansky (1959) discussed these points for errors-in-variables in linear regression. This...
In these settings, a logistic regression analysis is no more straightforward. We develop a maximum likelihood estimation procedure, based on the joint modeling of the binary response of interest and the cure status. We investigate the identifiability of the resulting model and establish the asymptotic...
The development of high-performance binary classification models is a challenge for researchers in the modeling field. Deployment both of logistic regression and decision tree model use the dataset having predictor features which are a mixture of categorical and numerical features, both models tend to ...