# Category: Regression models

Logistic regression
In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or mo
Stock sampling
Stock sampling is sampling people in a certain state at the time of the survey. This is in contrast to flow sampling, where the relationship of interest deals with duration or survival analysis. In st
Multivariate probit model
In statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly. For example, if it is believed that t
Vector generalized linear model
In statistics, the class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models (GLMs).In particular, VGLMs allow for response
Multilevel regression with poststratification
Multilevel regression with poststratification (MRP) (sometimes called "Mister P") is a statistical technique used for correcting model estimates for known differences between a sample population (the
First-hitting-time model
Events are often triggered when a stochastic or random process first encounters a threshold. The threshold can be a barrier, boundary or specified state of a system. The amount of time required for a
General linear model
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical l
Fixed effects model
In statistics, a fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities. This is in contrast to random effects models and mixed models in which all
Hierarchical generalized linear model
In statistics, hierarchical generalized linear models extend generalized linear models by relaxing the assumption that error components are independent. This allows models to be built in situations wh
Segmented regression
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate l
Censored regression model
Censored regression models are a class of models in which the dependent variable is censored above or below a certain threshold. A commonly used likelihood-based model to accommodate to a censored sam
Response modeling methodology
Response modeling methodology (RMM) is a general platform for statistical modeling of a linear/nonlinear relationship between a response variable (dependent variable) and a linear predictor (a linear
Generalized linear array model
In statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. It based on the generalized linear model with the design matrix written as a Kronecker p
In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear response variable depends linearly on unknown smooth functions of some predictor variables, and inte
Errors-in-variables models
In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models as
Marginal model
In statistics, marginal models (Heagerty & Zeger, 2000) are a technique for obtaining regression estimates in multilevel modeling, also called hierarchical linear models.People often want to know the
Mixed model
A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. These models are useful in a wide variety of disciplines in t
Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it i
Regression dilution
Regression dilution, also known as regression attenuation, is the biasing of the linear regression slope towards zero (the underestimation of its absolute value), caused by errors in the independent v
Multilevel model
Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-p
Conditional change model
The conditional change model in statistics is the analytic procedure in which change scores are regressed on baseline values, together with the explanatory variables of interest (often including indic
Generalized linear model
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the resp
In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) and is an essential part of the ACE algorithm. The AM uses
Simultaneous equations model
Simultaneous equations models are a type of statistical model in which the dependent variables are functions of other dependent variables, rather than just independent variables. This means some of th
Tobit model
In statistics, a tobit model is any of a class of regression models in which the observed range of the dependent variable is censored in some way. The term was coined by Arthur Goldberger in reference
Truncated normal hurdle model
In econometrics, the truncated normal hurdle model is a variant of the Tobit model and was first proposed by Cragg in 1971. In a standard Tobit model, represented as , where This model construction im
Measuring attractiveness by a categorical-based evaluation technique (MACBETH)
Measuring attractiveness through a categorical-based evaluation technique (MACBETH) is a multiple-criteria decision analysis (MCDA) method that evaluates options against multiple criteria. MACBETH was
Discriminative model
Discriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such
Linear model
In statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous wi
The Bradley–Terry model is a probability model that can predict the outcome of a paired comparison. Given a pair of individuals i and j drawn from some population, it estimates the probability that th
Sinusoidal model
In statistics, signal processing, and time series analysis, a sinusoidal model is used to approximate a sequence Yi to a sine function: where C is constant defining a mean level, α is an amplitude for
Factor regression model
Within statistical factor analysis, the factor regression model, or hybrid factor model, is a special multivariate model with the following form: where, is the -th (known) observation. is the -th samp
EM algorithm and GMM model
In statistics, EM (expectation maximization) algorithm handles latent variables, while GMM is the Gaussian mixture model.
Multilevel modeling for repeated measures
One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over ti
Polynomial and rational function modeling
In statistical modeling (especially process modeling), polynomial functions and rational functions are sometimes used as an empirical technique for curve fitting.
Random effects model
In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables. It is a kind of hierarchical linear model, which
Truncated regression model
Truncated regression models are a class of models in which the sample has been truncated for certain ranges of the dependent variable. That means observations with values in the dependent variable bel
GHK algorithm
The GHK algorithm (Geweke, Hajivassiliou and Keane) is an importance sampling method for simulating choice probabilities in the multivariate probit model. These simulated probabilities can be used to
Threshold model
In mathematical or statistical modeling a threshold model is any model where a threshold value, or set of threshold values, is used to distinguish ranges of values where the behaviour predicted by the
Structural equation modeling
Structural equation modeling (SEM) is a label for a diverse set of methods used by scientists in both experimental and observational research across the sciences, business, and other fields. It is use
Total least squares
In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational errors on both dependent and independent variable
History index model
In statistical analysis, the standard framework of varying coefficient models (also known as concurrent regression models), where the current value of a response process is modeled in dependence on th
Proper linear model
In statistics, a proper linear model is a linear regression model in which the weights given to the predictor variables are chosen in such a way as to optimize the relationship between the prediction
Hedonic regression
In economics, hedonic regression, also sometimes called hedonic demand theory, is a revealed preference method for estimating demand or value. It decomposes the item being researched into its constitu
Multi-attribute global inference of quality
Multi-attribute global inference of quality (MAGIQ) is a multi-criteria decision analysis technique. MAGIQ is based on a hierarchical decomposition of comparison attributes and rating assignment using
Nonlinear mixed-effects model
Nonlinear mixed-effects models constitute a class of statistical models generalizing linear mixed-effects models. Like linear mixed-effects models, they are particularly useful in settings where there
Fay-Herriot model
The Fay–Herriot model is a statistical model which includes some distinct variation for each of several subgroups of observations. It is an area-level model, meaning some input data are associated wit