Different prediction methods give different performance predictions when used for daily fresh food sales forecasting. Logistic Regression (LR) is a good choice for binary data, the Moving Average (MA) method is good for simple prediction, while the Back-Propagation Neural Network (BPNN) method is good for long term data. In this study we develop and compare the performance of three sales foreca...

When the outcome is binary, psychologists often use nonlinear modeling strategies such as logit or probit. These are neither optimal nor justified when objective to estimate causal effects of experimental treatments. Researchers need take extra steps convert and probit coefficients into interpretable quantities, they do, these quantities remain difficult understand. Odds ratios, for instance, d...

Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in t...

BACKGROUND Brain-body associations are essential in influencing outcome in patients with ruptured brain aneurysms. Thus far, there is scarce literature on such important relationships. METHODS The multicenter Tirilazad database (3551 patients) was used to create this clinical outcome prediction model in order to elucidate significant brain-body associations. Traditional binary logistic regres...

Most of the non-asymptotic theoretical work in regression is carried out for the square loss, where estimators can be obtained through closed-form expressions. In this paper, we use and extend tools from the convex optimization literature, namely self-concordant functions, to provide simple extensions of theoretical results for the square loss to the logistic loss. We apply the extension techni...

©FSRH J Fam Plann Reprod Health Care 2008: 34(3) What is it? When a response variable has only two possible values (e.g. recurrence/not), binary logistic regression is commonly used to test or model the association between that response and a number of potential explanatory variables, with each association estimated in terms of an odds ratio (OR). Multinomial logistic regression is an extension...

We aim at proposing a Generalized Linear Model (GLM) with binary dependent variable Y , whose link function defined by the Generalized Extreme Value (GEV) distribution. We define this model as GEV regression. The goal of this paper is to overcome the drawbacks shown by the logistic regression in rare events: the probability of rare events is underestimated and the logit link is a symmetric func...

Methods We analyzed 449 patients with diagnosis of non ST elevation coronary syndrome consecutively admitted in our teaching referrall ICU during the period 01/01/2012 20/ 03/2015. When PCI was performed during ICU it was considered ePCI. Otherwise (patients without PCI or with delayed PCI after ICU stay) were considered non ePCI. We analyzed the influence of ePCI in hospital mortality. An univ...