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The robustness of the F-test to spatial autocorrelation among regression disturbances
Krämer, Walter - 2002 - Version December 2002
autocorrelation among the regression disturbances. In particular, the true size of the test tends to either zero or unity when the … spatial autocorrelation coefficient approaches the boundary of the parameter space. …
Persistent link: https://www.econbiz.de/10009770521
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Finite sample power of Cliff-Ord-type-tests for spatial disturbance correlation in linear regression
Krämer, Walter - 2002 - Version August 2002
The paper considers tests against for autocorrelation among the disturbances in linear regression models that can be …
Persistent link: https://www.econbiz.de/10009770908
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OLS-based estimation of the disturbance variance under spatial autocorrelation
Krämer, Walter (contributor); Hanck, Christoph (contributor) - 2006
We investigate the OLS-based estimator s 2 of the disturbance variance in the standard linear regression model with cross section data when the disturbances are homoskedastic, but spatially correlated. For the most popular model of spatially autoregressive disturbances, we show that s 2 can be...
Persistent link: https://www.econbiz.de/10003394588
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GMM estimation of the autoregressiveparameter in a spatial autoregressive errormodel using regression residuals
Arnold, Matthias (contributor) - 2007
This paper suggests an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model … bias can be reduced by 65 - 80% compared to a GMM estimator that neglects the difference between disturbances and residuals …. The mean squared error is smaller, too. -- GMM estimation ; spatial autoregression ; regression residuals …
Persistent link: https://www.econbiz.de/10003581880
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An alternative method for meta-analysis
Hartung, Joachim - 1998
In many fields of applications, test statistics are obtained by combining estimates from several experiments, studies or centres of a multicentre trial. The commonly used test procedure to judge the evidence of a common overall effect can result in a considerable overestimation of the...
Persistent link: https://www.econbiz.de/10010438776
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Bayesian and maximin optimal designs for heteroscedastic regression models
Dette, Holger; Haines, Linda M.; Imhof, Lorens - 2003
The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by Dette, Haines and Imhof (2003), such designs...
Persistent link: https://www.econbiz.de/10010511729
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On the meta-analysis of treatment differences in heteroscedastic samples
Makambi, Kepher Henry; Argaç, Dogan; Hartung, Joachim - 2000
Persistent link: https://www.econbiz.de/10001512393
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A geometric characterization of c-optimal designs for heteroscedastic regression
Dette, Holger; Holland-Letz, Tim - 2008
We consider the common nonlinear regression model where the variance as well as the mean is a parametric function of the explanatory variables. The c-optimal design problem is investigated in the case when the parameters of both the mean and the variance function are of interest. A geometric...
Persistent link: https://www.econbiz.de/10003837744
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Robustness properties of minimally-supported Bayesian D-optimal designs for heteroscedastic models
Dette, Holger; Song, Dale; Wong, Weng Kee - 2001
Persistent link: https://www.econbiz.de/10009777471
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Parameter estimation in enzyme-kinetics with consideration of heteroscedasticity and low dose data
Brunnert, Marcus; Gilberg, Frank - 1999
Eadie-Hofstee-plot. Due to heteroscedasticity of enzyme-kinetic data in low dose experiments the proposed estimators are …
Persistent link: https://www.econbiz.de/10009789913