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

P(N(D)=k)={\frac {(\lambda |D|)^{k}e^{-\lambda |D|}}{k!}}.} Poisson regression and negative binomial regression are useful for analyses where the dependent (response)...

overdispersed alternative to the Poisson distribution, for example for a robust modification of Poisson regression. In epidemiology, it has been used...

j ∼ Poisson ( λ ) Y i , j ∼ Poisson ( μ ) {\displaystyle {\begin{aligned}X_{i,j}&\sim {\text{Poisson}}(\lambda )\\Y_{i,j}&\sim {\text{Poisson}}(\mu...

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares...

Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included, are...

the exponential regression framework, which the following discussion follows closely. While the example focuses on a Poisson regression model, it is possible...

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

is often represented using a Poisson distribution or a negative binomial distribution. Hilbe notes that "Poisson regression is traditionally conceived of...

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

itself be described as a regression model. There is a relationship between proportional hazards models and Poisson regression models which is sometimes...

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

In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...

process Poisson binomial distribution Poisson distribution Poisson hidden Markov model Poisson limit theorem Poisson process Poisson regression Poisson random...

that a proposed regression model fits the data well. See Lack-of-fit sum of squares. The hypothesis that a data set in a regression analysis follows...

Through regression analysis, one can derive the equation for the curve or straight line and obtain the correlation coefficient. Simple linear regression is...

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...

linear regression assumptions hold; further we assume that the slope of the covariate is equal across all treatment groups (homogeneity of regression slopes)...

In statistics, a fixed-effect Poisson model is a Poisson regression model used for static panel data when the outcome variable is count data. Hausman...

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function...

In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...

a distribution from the Poisson family is often dictated by the nature of the empirical data. For example, Poisson regression analysis is commonly used...

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

distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead...

parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y...

developments, including Poisson regression, ordinal logistic regression, quantile regression and multinomial logistic regression that described by Fallah...

to multiple regression analysis is sometimes used as an aid to interpretation. (page 95) state the following. "The standardized regression slope is the...

testing. In regression problems, case resampling refers to the simple scheme of resampling individual cases – often rows of a data set. For regression problems...