# Linear modeling

*34,191*pages on

this wiki

Assessment |
Biopsychology |
Comparative |
Cognitive |
Developmental |
Language |
Individual differences |
Personality |
Philosophy |
Social |

Methods |
Statistics |
Clinical |
Educational |
Industrial |
Professional items |
World psychology |

**Statistics:**
Scientific method ·
Research methods ·
Experimental design ·
Undergraduate statistics courses ·
Statistical tests ·
Game theory ·
Decision theory

In statistics the **linear model** is a model given by

where *Y* is an *n*×1 column vector of random variables, *X* is an *n*×*p* matrix of "known" (i.e., observable and non-random) quantities, whose rows correspond to statistical units, β is a *p*×1 vector of (unobservable) parameters, and ε is an *n*×1 vector of "errors", which are uncorrelated random variables each with expected value 0 and variance σ^{2}. Often one takes the components of the vector of errors to be independent and normally distributed. Having observed the values of *X* and *Y*, the statistician must estimate β and σ^{2}. Typically the parameters β are estimated by the method of maximum likelihood, which in the case of normal errors is equivalent (by the Gauss-Markov theorem) to the method of least squares.

If, rather than taking the variance of ε to be σ^{2}*I*, where *I* is the *n*×*n* identity matrix, one assumes the variance is σ^{2}*M*, where *M* is a known matrix other than the identity matrix, then one estimates β by the method of "generalized least squares", in which, instead of minimizing the sum of squares of the residuals, one minimizes a different quadratic form in the residuals — the quadratic form being the one given by the matrix *M*^{-1}. This leads to the estimator

which is the best linear unbiased estimator for . If all of the off-diagonal entries in the matrix *M* are 0, then one normally estimates β by the method of "weighted least squares", with weights proportional to the reciprocals of the diagonal entries.

Ordinary linear regression is a very closely related topic.

## Contents

[show]## Generalizations Edit

### Generalized linear models Edit

Generalized linear models, for which rather than

- E(
*Y*) =*X*β,

one has

*g*(E(*Y*)) =*X*β,

where *g* is the "link function". An example is the Poisson regression model, which states that

*Y*_{i}has a Poisson distribution with expected value*e*^{γ+δxi}.

The link function is the natural logarithm function.
Having observed *x*_{i} and *Y*_{i} for
*i* = 1, ..., *n*, one can estimate γ and δ by the method of maximum likelihood.

### General linear model Edit

The general linear model (or multivariate regression model) is a linear model with multiple measurements per object. Each object may be represented in a vector.

### See also Edit

- ANOVA, or analysis of variance, is historically a precursor to the development of linear models. Here the model parameters themselves are not computed, but
*X*column contributions and their significance are identified using the ratios of within-group variances to the error variance and applying the F test.de:Lineares Modell

This page uses Creative Commons Licensed content from Wikipedia (view authors). |