Results 41 to 50 of about 4,991,757 (343)

High-dimensional Ising model selection using ${\ell_1}$-regularized logistic regression [PDF]

, 2010
We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic ...
Lafferty, John D., Ravikumar, Pradeep, Wainwright, Martin J.   +2 more
core   +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   +3 more sources

A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA

Barekeng
The Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development.
Erwan Setiawan, Muhammad Azis Suprayogi, Anang Kurnia   +2 more
doaj   +1 more source

Supporting Regularized Logistic Regression Privately and Efficiently. [PDF]

PLoS ONE, 2016
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on.
Wenfa Li, Hongzhe Liu, Peng Yang, Wei Xie   +3 more
doaj   +1 more source

Uncertain logistic regression models

AIMS Mathematics
Logistic regression is a generalized nonlinear regression analysis model and is often used for data mining, automatic disease diagnosis, economic prediction, and other fields.
Jinling Gao , Zengtai Gong
doaj   +1 more source

Distributed Parallel Sparse Multinomial Logistic Regression

IEEE Access, 2019
Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification.
Dajiang Lei, Meng Du, Hao Chen, Zhixing Li, Yu Wu   +4 more
doaj   +1 more source

Logistic regression models

Allergologia et Immunopathologia, 2011
In the health sciences it is quite common to carry out studies designed to determine the influence of one or more variables upon a given response variable. When this response variable is numerical, simple or multiple regression techniques are used, depending on the case.
S, Domínguez-Almendros, N, Benítez-Parejo, A R, Gonzalez-Ramirez   +2 more
openaire   +2 more sources

Hidden Markov Model Based on Logistic Regression

Mathematics, 2023
A hidden Markov model (HMM) is a useful tool for modeling dependent heterogeneous phenomena. It can be used to find factors that affect real-world events, even when those factors cannot be directly observed.
Byeongheon Lee, Joowon Park, Yongku Kim
doaj   +1 more source

The sign of the logistic regression coefficient

, 2014
Let Y be a binary random variable and X a scalar. Let $\hat\beta$ be the maximum likelihood estimate of the slope in a logistic regression of Y on X with intercept.
Owen, Art B., Roediger, Paul A.
core   +1 more source

On Data-Enriched Logistic Regression

Mathematics
Biomedical researchers typically investigate the effects of specific exposures on disease risks within a well-defined population. The gold standard for such studies is to design a trial with an appropriately sampled cohort.
Cheng Zheng, Sayan Dasgupta, Yuxiang Xie, Asad Haris, Ying-Qing Chen   +4 more
doaj   +1 more source