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2014-09-15T12:42:11Z

capitalisation

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	~~Good day~~, ~~my identify~~ is ~~Norman even though I do not really like being referred~~ to ~~as like that. I've usually loved dwelling in Illinois. Distributing manufacturing is what I do for~~ a ~~dwelling~~. ~~Caravaning is one thing I will hardly ever give up. You can uncover my website here: http://www.antonimiro.com/ingles/botiga/grafica~~/[~~http~~:/~~/www.Alexa.com/search?q~~=~~precios-de-nike-air-max.htm&r~~=~~topsites_index&p=bigtop precios~~-de-~~nike~~-~~air~~-~~max.htm]~~<~~br><br~~>		In [[statistics]], a '''generalized estimating equation (GEE)''' is used to [[Estimator\|estimate]] the parameters of a [[generalized linear model]] with a possible unknown [[Correlation and dependence\|correlation]] between outcomes.
			<ref>{{cite journal \| journal=Biometrika \| volume=73 \| issue=1 \| pages=13–22 \| title=Longitudinal data analysis using generalized linear models \| authors = Kung-Yee Liang and Scott Zeger \| year=1986 }}</ref>
			<ref>{{cite book \| last = Hardin\| first = James \| coauthors = [[Joseph Hilbe\|Hilbe, Joseph]] \| title = Generalized Estimating Equations \| publisher = London: Chapman and Hall/CRC \| year = 2003 \|isbn=978-1-58488-307-4}}</ref>

	~~Look into my webpage~~ ... [http://www.~~antonimiro~~.com/~~ingles~~/~~botiga~~/~~grafica~~/~~precios~~-de-~~nike~~-~~air~~-~~max.htm Precios De Nike Air Max~~]		Parameter estimates from the GEE are [[Consistency (statistics)\|consistent]] even when the [[covariance]] structure is misspecified, under mild regularity conditions. The focus of the GEE is on estimating the average response over the population ("population-averaged" effects) rather than the [[regression analysis\|regression]] parameters that would enable prediction of the effect of changing one or more covariates on a given individual. GEEs are usually used in conjunction with [[Heteroscedasticity-consistent standard errors\|Huber-White standard error]] estimates, also known as "robust standard error" or "sandwich variance" estimates. In the case of a linear model with a working independence variance structure, these are known as "heteroskedasticity consistent standard error" estimators. Indeed, the GEE unified several independent formulations of these standard error estimators in a general framework.

			GEEs belong to a class of [[Semiparametric model\|semiparametric]] regression techniques because they rely on specification of only the first two moments. Under correct model specification and mild regularity conditions, parameter estimates from GEEs are consistent. They are a popular alternative to the likelihood–based [[generalized linear mixed model]] which is more sensitive to variance structure specification.{{Citation needed\|date=December 2013}} They are commonly used in large epidemiological studies, especially multi-site [[Cohort study\|cohort studies]] because they can handle many types of unmeasured dependence between outcomes.

			== Formulation ==

			Given a mean model, <math>\mu_{ij}</math>, and variance structure, <math>V_{i}</math>, the estimating equation is formed via:<ref>{{cite book \| last = Diggle\| first = Peter J.\| coauthors = Patrick Heagerty, Kung-Yee Liang, Scott L. Zeger\| title = Analysis of Longitudinal Data \| publisher = Oxford Statistical Science Series \| year = 2002 \|isbn=978-0-19-852484-7}}</ref>

			: <math> U(\beta) = \sum_{i=1}^N \frac{\partial \mu_{ij}}{\partial \beta_k} V_i^{-1} \{ Y_i - \mu_i(\beta)\} \,\!</math>

			The parameter estimates solve ''U(β)=0'' and are typically obtained via the [[Newton's method\|Newton-Raphson]] algorithm. The variance structure is chosen to improve the efficiency of the parameter estimates. The [[Hessian matrix\|Hessian]] of the solution to the GEEs in the parameter space can be used to calculate robust standard error estimates. The term "variance structure" refers to the algebraic form of the covariance matrix between outcomes, Y, in the sample. Examples of variance structure specifications include independence, exchangeable, autoregressive, stationary m-dependent, and unstructured. The most popular form of inference on GEE regression parameters is the [[Wald test]] using naive or robust standard errors, though the [[Score test]] is also valid and preferable when it is difficult to obtain estimates of [[Fisher information\|information]] under the alternative hypothesis. The [[Likelihood-ratio test\|likelihood ratio test]] is not valid in this setting because the estimating equations are not necessarily likelihood equations. Model selection can be performed with the GEE equivalent of the [[Akaike information criterion\|Akaike Information Criterion]] (AIC) the Quasi-AIC (QIC).

			== Computation ==

			Software for solving generalized estimating equations is available in [[MATLAB]],<ref>{{cite journal \| journal=Journal of Statistical Software \| volume=25 \| issue=14 \| pages=1–14 \| title=GEEQBOX: A MATLAB Toolbox for Generalized Estimating Equations and Quasi-Least Squares \| authors = Sarah J. Ratcliffe and Justine Shults \| year=2008 \|url = http://www.jstatsoft.org/v25/i14}}</ref> [[SAS (software)\|SAS]] (proc '''genmod'''<ref>{{cite web\|url=http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/genmod_toc.htm \|title=The GENMOD Procedure \|location=The SAS Institute}}</ref>), [[SPSS]] (the '''gee''' procedure<ref>{{cite web\|url=http://www.spss.com/software/statistics/advanced-statistics/ \|title=IBM SPSS Advanced Statistics\|location=IBM SPSS website}}</ref>), [[Stata]] (the '''xtgee''' command<ref>{{cite web\|url=http://www.stata.com/support/faqs/stat/gee.html \|title=Stata’s implementation of GEE \|location=Stata website}}</ref>) and [[R (programming language)\|R]] (packages '''gee'''<ref>{{cite web\|url=http://cran.r-project.org/web/packages/gee/index.html \|location=CRAN \|title=gee: Generalized Estimation Equation solver}}</ref> and '''geepack'''<ref>{{citation \|url=http://cran.r-project.org/web/packages/geepack/index.html \|location=CRAN \|title=geepack: Generalized Estimating Equation Package}}</ref>).

			== References ==
			{{reflist}}

			{{DEFAULTSORT:Generalized Estimating Equations}}
			[[Category:Statistical terminology]]
			[[Category:Regression analysis]]
			[[Category:Statistical models]]
			[[Category:M-estimators]]

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Good day, my identify is Norman even though I do not really like being referred to as like that. I've usually loved dwelling in Illinois. Distributing manufacturing is what I do for a dwelling. Caravaning is one thing I will hardly ever give up. You can uncover my website here: http://www.antonimiro.com/ingles/botiga/grafica/[http://www.Alexa.com/search?q=precios-de-nike-air-max.htm&r=topsites_index&p=bigtop precios-de-nike-air-max.htm]<br><br>

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Hartree equation - Revision history

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