Previous methodological and applied studies that used binary logistic regression (LR) for detection of differential item functioning (DIF) in dichotomously scored items either did not report an effect size or did not employ several useful measures of DIF magnitude derived from the LR model. Equations are provided for these effect size indices. Using two large data sets, the authors demonstrate ...

[Purpose] To investigate the predictive value of maximum inspiratory pressure (MIP) and the rapid shallow breathing index (RSBI) in a ventilator weaning protocol and to evaluate the differences between clinical and surgical patients in the intensive care unit. [Subjects and Methods] Patients aged ≥15 years who underwent orotracheal intubation for mechanical ventilation and who met the criteria ...

Logistic regression (LR) is a famous classification technique commonly used in statistics, machine learning, and data mining area of knowledge for learning a response of binary nature. It assumes that the data values are pre-determined precisely, but this is not true for all conditions. Uncertainty data arises in many applications because of data collection methodology as in repeated measures, ...

Our project builds a binary classifier that predicts the existence of a connection between any pair of nodes in a facebook ego-net graph. We investigate the most representative features to use and compare the performance of Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). SVM performs well on the specific task. LR performs decently and will potentially play a larg...

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...

Logistic regression (LR) is widely applied as a powerful classification method in various fields, and a variety of optimization methods have been developed. To cope with large-scale problems, an efficient optimization method for LR is required in terms of computational cost and memory usage. In this paper, we propose an efficient optimization method using non-linear conjugate gradient (CG) desc...

A long-standing challenge for knowledge tracing is how to update estimates of multiple subskills that underlie a single observable step. We characterize approaches to this problem by how they model knowledge tracing, fit its parameters, predict performance, and update subskill estimates. Previous methods allocated blame or credit among subskills in various ways based on strong assumptions about...

This paper compares the performance of logistic regression to decision-tree induction in classifying patients as having acute cardiac ischemia. This comparison was performed using the database of 5773 patients originally used to develop the logistic-regression tool and test it prospectively. Both the ability to classify cases and the ability to estimate the probability of ischemia were compared...

We consider the problem of scoring Bayesian Network Classifiers (BNCs) on the basis of the conditional loglikelihood (CLL). Currently, optimization is usually performed in BN parameter space, but for perfect graphs (such as Naive Bayes, TANs and FANs) a mapping to an equivalent Logistic Regression (LR) model is possible, and optimization can be performed in LR parameter space. We perform an emp...