Results 61 to 70 of about 2,178,475 (325)
Targeting: Logistic Regression, Special Cases and Extensions
ISPRS International Journal of Geo-Information, 2014 Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form ...
Helmut Schaeben
doaj +1 more source
Efficient posterior sampling for high-dimensional imbalanced logistic regression
, 2019 High-dimensional data are routinely collected in many areas. We are particularly interested in Bayesian classification models in which one or more variables are imbalanced.
Dunson, David +3 more
core +1 more source
Vestibular Patient Journey: Insights From Vestibular Disorders Association (VeDA) Registry
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Vestibular symptoms impose a high burden of disability. Understanding real‐world diagnostic and treatment pathways can identify care gaps and guide interventions. We aimed to characterize symptom profiles, diagnostic trends, provider involvement, and treatment patterns in vestibular disorders.
Ali Rafati +10 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Cognitive decline is a disabling and variable feature of Parkinson disease (PD). While cholinergic system degeneration is linked to cognitive impairments in PD, most prior research reported cross‐sectional associations. We aimed to fill this gap by investigating whether baseline regional cerebral vesicular acetylcholine transporter ...
Taylor Brown +6 more
wiley +1 more source
Common pitfalls in statistical analysis: Logistic regression
Perspectives in Clinical Research, 2017 Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous).
Priya Ranganathan +2 more
doaj +1 more source
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
Location‐Specific Hematoma Volume Predicts Early Neurological Deterioration in Supratentorial ICH
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Early neurological deterioration (END) adversely affects outcomes in patients with intracerebral hemorrhage (ICH). This study aimed to determine the location‐specific hematoma volumes for END in supratentorial ICH patients. Methods We retrospectively analyzed supratentorial ICH patients presenting from two prospective cohorts.
Zuoqiao Li +10 more
wiley +1 more source
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
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Retrograde trans‐synaptic degeneration (rTSD) from posterior visual pathway lesions in multiple sclerosis (MS) is characterized by hemi‐macular ganglion cell‐inner plexiform layer (GCIPL) thinning and contralateral visual field loss.
Abdul Jaber Tayem +17 more
wiley +1 more source
Model selection in logistic regression
, 2015 This paper is devoted to model selection in logistic regression. We extend the model selection principle introduced by Birg\'e and Massart (2001) to logistic regression model. This selection is done by using penalized maximum likelihood criteria.
Kwemou, Marius +2 more
core +1 more source