linear model

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linear model

A simplistic model that proposed that a single cell’s responses to an external stimulus reflected a summation of the intensity values in the stimulus. Considering the complexity of pathways and cascades which are triggered by any form of stimulation of living cells, this model warrants deletion.
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Parameters to be estimated for all I local linear models can be denotes as
The main advantages are that (1) the measurement of the underlying variables is not necessary, (2) the number of Gaussian components can be estimated, (3) the GMM model can be identified independently of the local linear models, and (4) it is a data-based method; no finite element model is needed.
[9] proposed the general linear model (GLM), which can be used as a unified framework in the analysis of fMRI data and support multiple experimental design, but it cannot be used to rt-fMRI applications, because it needs all of the data to do the statistical analysis.
The linear model used in our study is Spearman's factor analysis (FA) model, usually known as the congeneric model in the psychometric literature (Joreskog, 1971).
After the logistic regression model Poisson regression is the most widely used generalized linear models. Poisson regression models are applied when the response is a count, as the number of events in time.
Generalized linear models require a huge amount of data that small carriers do not have.
For this purpose, linear models were developed for the empirical strength ratios, and the model formulas were compared with the linear formulations of the equations presented in D245 (as outlined in Equations 11 through 13).
In this article we propose a new linear model for matrix-based regression or classification and apply it to some standard benchmark data sets.
Generally, models used to test the effects of genes on traits have been based on parametric methods, such as general linear models or the Animal model (Henderson, 1976).
The techniques outlined in this chapter are then used to develop methods for validating the appropriate statistical model in the chapters on Multiple Linear Regression, Exploiting the Linear Model Framework, and Logistic Regression and Other Generalized Linear Models.
Our texbooks abound with linear models of communication--starting with a sender on the left, ending with a receiver on the right.