Results 1 to 10 of about 1,921,898 (301)
Conditional logistic regression [PDF]
American Journal of Orthodontics and Dentofacial Orthopedics, 2017 In this article, we will describe how to analyze binary data from matched studies in orthodontics. We have previously discussed matched analysis for paired binary data (McNemar test), but now we will focus on the use of regression methods to model our data.1 The idea is the same as with simple logistic regression models for binary data2,3; however, we ...
Nikolaos Pandis, Despina Koletsi
openaire +3 more sources
The Origins of Logistic Regression [PDF]
SSRN Electronic Journal, 2003 This paper describes the origins of the logistic function, its adoption in bio-assay, and its wider acceptance in statistics. Its roots spread far back to the early 19th century; the survival of the term logistic and the wide application of the device have been determined decisively by the personal histories and individual actions of a few scholars.
openaire +4 more sources
Bayesian logistic regression for presence-only data [PDF]
, 2015 Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the \textit{presence} of an attribute of interest. A typical example
Antti Pettinen +3 more
core +1 more source
Sparse Bilinear Logistic Regression [PDF]
, 2014 In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices.
Baraniuk, Richard G. +2 more
core
Expectation-maximization for logistic regression [PDF]
, 2013 We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).
Scott, James G., Sun, Liang
core
Linear and logistic regression analysis [PDF]
Kidney International, 2008 In previous articles of this series, we focused on relative risks and odds ratios as measures of effect to assess the relationship between exposure to risk factors and clinical outcomes and on control for confounding. In randomized clinical trials, the random allocation of patients is hoped to produce groups similar with respect to risk factors.
Carmine Zoccali +4 more
openaire +4 more sources
Structured Learning via Logistic Regression [PDF]
, 2014 A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each.
Domke, Justin
core
Measuring overlap in logistic regression [PDF]
Research Papers in Economics, 1999 In this paper we show that the recent notion of regression depth can be used as a data-analytic tool to measure the amount of separation between successes and failures in the binary response framework. Extending this algorithm allows us to compute the overlap in data sets which are commonly fitted by logistic regression models.
Christmann, Andreas, Rousseeuw, Peter J.
openaire +5 more sources
Resampling Logistic Regression Untuk Penanganan Ketidakseimbangan Class Pada Prediksi Cacat Software [PDF]
, 2015 Software yang berkualitas tinggi adalah software yang dapat membantu proses bisnis Perusahaan dengan efektif, efesien dan tidak ditemukan cacat selama proses pengujian, pemeriksaan, dan implementasi.
Rianto, H. (Harsih) +1 more
core
Model Logistic Regression dalam Penentuan Kebijakan Dividen Perusahaan di Indonesia [PDF]
, 2012 This paper investagates dividend policy decision in Indonesian Stock Exchange (IDX) through studying non-financial firms. Panel data were obtained from 1490 non-financial firms over the five year period from 2006 to 2010, where 310 firms pay dividend and
Satmoko, A. (Agung) +1 more
core