Multilevel models (also known as hierarchical linear models, linear mixed-effect models, 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...

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...

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...

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

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

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...

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...

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...

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

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

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...

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

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

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

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...

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

data structures such as multilevel data. A complete overview of the latent Markov models, with special attention to the model assumptions and to their...

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}} ...

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

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

Andrew Gelman proposed a different approach of ANOVA, viewed as a multilevel model. Let us imagine a data set for which a dependent variable may be influenced...

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...

want to estimate the mean of that distribution (the so-called location model). In this case, the errors are the deviations of the observations from the...

Multilevel Flow Modeling (MFM) is a framework for modeling industrial processes. MFM is a kind of functional modeling employing the concepts of abstraction...