Ad
related to: hosmer-lemeshow statistic logistic regression
Search results
Results From The WOW.Com Content Network
The Hosmer–Lemeshow test is a statistical test for goodness of fit and calibration for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event rates match expected event rates in subgroups of the model population.
Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. [6]
Logistic regression; ... Hosmer–Lemeshow test; ... G-tests have been recommended at least since the 1981 edition of the popular statistics textbook by Robert R ...
Main page; Contents; Current events; Random article; About Wikipedia; Contact us
Holland PW, Welsch RE. Robust regression using iteratively reweighted least-squares, 1977, 526 cites. Sugiura N. Further analysts of the data by Akaike's information criterion and the finite corrections, 1978, 490 cites. Hosmer DW, Lemeshow S. Goodness of fit tests for the multiple logistic regression model, 1980, 401 cites. Iman RL, Conover WJ.
Commonly used models in the GLM family include binary logistic regression [5] for binary or dichotomous outcomes, Poisson regression [6] for count outcomes, and linear regression for continuous, normally distributed outcomes. This means that GLM may be spoken of as a general family of statistical models or as specific models for specific ...
In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic function, which appears in logistic regression and feedforward neural networks. It resembles the normal distribution in shape but has heavier tails (higher kurtosis).
Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching. Its main field of application is observational studies and in particular epidemiology. It was devised in 1978 by Norman Breslow, Nicholas Day, Katherine Halvorsen, Ross L. Prentice and C. Sabai. [1]