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...

In this paper, we consider the logistic regression model with an integrated regressor driven by a general linear process. In particular, we derive the limit distributions of the nonlinear least squares (NLS) estimators and their t-ratios of the parameters in the model. It is shown that the NLS estimators are generally not efficient. Moreover, the t-ratios for the level parameters have limit dis...

Based on the experience of teaching logistic regression to non-mathematicians, a number of areas of possible confusion are identified that may arise particularly when the method is contrasted with multiple linear regression. The fact that the model is multiplicative in odds ratios means that the concept of interaction needs to be clearly defined. Confidence intervals for the estimates of the od...

We extend logistic regression by using t-exponential families which were introduced recently in statistical physics. We examine our algorithm for both binary classfication and multiclass classfication with both L1 and L2 regularizer. The objective function of our algorithm is non-convex, an efficient block coordinate descent optimization scheme is derived for estimating the parameters. Because ...

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...