The aim of this paper is to describe an innovative Logistic Game® developed by the authors. It creates a competition between different teams in terms of strategic decisions in logistic and production problems in a real production system. The game is innovative in three ways. The first is the type of game. It is a game representing a production and logistic system, with the dynamic problems of a...

©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...

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the associati...

A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general spatially- or temporally-dependent data, imposing the belief that proximate data are more likely to be clustered together. The sticks in the LSBP are realized via multiple logistic regression functions, with shrinkage priors employed to favor contiguous and spatially localized segments. The LSBP is als...

Anchoring this work to the classical human capital theory, the authors examine the effects of various human capital factors on IT professional compensation. Dividing IT salary into LOW (<$75,000) and HIGH (>=$75,000) ranges and using binomial logistic regression analysis, this paper estimates the effects of IT experience, education, IT degrees, IT certifications, and managerial positions on the...

This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. We focus on privacy-preserving logistic regression. First we apply an idea of Dwork et al. [6] to design a privacy-preserving logistic regression algorithm. This involves bounding the sensitivity of regularized logistic regression, and perturbing the learn...

Semi-supervised learning has recently emerged as a new paradigm in the machine learning community. It aims at exploiting simultaneously labeled and unlabeled data for classification. We introduce here a new semi-supervised algorithm. Its originality is that it relies on a discriminative approach to semisupervised learning rather than a generative approach, as it is usually the case. We present ...

A common problem in software cost estimation is the manipulation of incomplete or missing data in databases used for the development of prediction models. In such cases, the most popular and simple method of handling missing data is to ignore either the projects or the attributes with missing observations. This technique causes the loss of valuable information and therefore may lead to inaccura...