Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects...

One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed...

Multilevel regression with poststratification (MRP) is a statistical technique used for correcting model estimates for known differences between a sample...

Snijders, Bosker, T.A.B, R.J (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Vol. 2nd edition. London:Sage.{{cite...

In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word...

of X on a given individual. GLMMs are also referred to as multilevel models and as mixed model. In general, fitting GLMMs is more computationally complex...

In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables...

multi-group modeling, longitudinal modeling, partial least squares path modeling, latent growth modeling and hierarchical or multilevel modeling. SEM researchers...

interspecific interactions, multilevel and kinship selection can change the population dynamics of an ecosystem. Multilevel selection attempts to explain...

problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes...

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

marginal models (Heagerty & Zeger, 2000) are a technique for obtaining regression estimates in multilevel modeling, also called hierarchical linear models. People...

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...

In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more...

1214/aos/1176343056. JSTOR 2958945. Goldstein, H. (1986). "Multilevel Mixed Linear Model Analysis Using Iterative Generalized Least Squares". Biometrika...

the U.S. Department of Defense (DoD) multilevel security (MLS) policy. The model is a formal state transition model of computer security policy that describes...

Christian B. (2007). "Generalized Least Squares Inference in Panel and Multilevel Models with Serial Correlation and Fixed Effects". Journal of Econometrics...

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called...

Multilevel security or multiple levels of security (MLS) is the application of a computer system to process information with incompatible classifications...

In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables...

you make is based on your sales to the public, it may be a legitimate multilevel marketing plan. If the money you make is based on the number of people...

Andrew; Hill, Jennifer (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. New York: Cambridge University Press. pp. 119–124. ISBN 978-0-521-68689-1...

generalized logistic function in epidemiological modeling is its relatively easy application to the multilevel model framework, where information from different...

2018. Not all multilevel marketing plans are legitimate. Some are pyramid schemes. "Multilevel Marketing". FTC. May 18, 2021. "Multilevel Marketing". FTC...

S2CID 237204627. Grimm, Kevin J. (2017). Growth modeling : structural equation and multilevel modeling approaches. Nilam Ram, Ryne Estabrook. New York...

are used to model binary choice. Binary regression models can be interpreted as latent variable models, together with a measurement model; or as probabilistic...

entries of the covariance matrix of the errors, are null. The fit of a model to a data point is measured by its residual, r i {\displaystyle r_{i}} ...

the alternative: a score-based approach with application to segmented modelling" (PDF). Journal of Statistical Computation and Simulation. 86 (15): 3059–3067...

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy...

regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. In some fields, LOESS is known and commonly referred...