Conditional logistic regression information

Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching. Its main field of application...

independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients...

etc.). Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit...

Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional mean...

In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...

commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability...

may also be analyzed using conditional logistic regression. This technique has the advantage of allowing users to regress case-control status against...

common binary regression models are the logit model (logistic regression) and the probit model (probit regression). Binary regression is principally...

or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). Regression analysis is primarily...

existing datapoints. Types of discriminative models include logistic regression (LR), conditional random fields (CRFs), decision trees among many others....

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...

In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

predictive performance than other linear models, such as logistic regression and linear regression.[citation needed] Classifying data is a common task in...

used, since this is more familiar in everyday life". The logit in logistic regression is a special case of a link function in a generalized linear model:...

case, the "regression" effect is statistically likely to occur, but in the second case, it may occur less strongly or not at all. Regression toward the...

neighbors algorithm Logistic regression Support Vector Machines Decision Tree Learning Random Forest Maximum-entropy Markov models Conditional random fields...

Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...

response model. As such it treats the same set of problems as does logistic regression using similar techniques. When viewed in the generalized linear model...

Regression diagnostic Regression dilution Regression discontinuity design Regression estimation Regression fallacy Regression-kriging Regression model validation...

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...

especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent...

It is a generalization of the logistic function to multiple dimensions, and used in multinomial logistic regression. The softmax function is often used...

{\displaystyle p(C,\mathbf {x} )} , while logistic regression fits the same probability model to optimize the conditional p ( C ∣ x ) {\displaystyle p(C\mid...

have their intended interpretation. Paired data Sign test Conditional logistic regression Derrick, B; Broad, A; Toher, D; White, P (2017). "The impact...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

discriminant analysis are joint probability models, whereas logistic regression is a conditional probability model. There are two basic approaches to choosing...