Robust Diagnostic Regression Analysis

Robust regression
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The book could thus be used as a text for a second course in regression as well providing statisticians and scientists with a new set of tools for data analysis. A companion volume on the analysis of multivariate data is in active preparation. Email: a.

Robust variable selection in linear regression models / Alshqaq, Shokrya Saleh A

Email: mriani unipr. Links are provided to Springer's on line home page of the book.

The identification of high leverage points and residual outliers are believed to be vital in order to improve the performance of the MLE. The presence of high leverage points and the residual outliers give adverse effect on the inferences by inducing large values to the Influence Function IF. For the identification of high leverage points, Imon proposed the Distance from the Mean DM diagnostic method.

The weakness of the DM method is that it tends to swamp some low leverage points even though it can identify the high leverage points correctly. Deleting the low leverage points may lead to a loss of efficiency and precision of the parameter estimates.

Bibliographic Information

This book is about using graphs to understand the relationship between a regression model and the data to which it is fitted. Because of the way in which models. Mirros of this material will be available in London and New York. Description: The authors develop new, highly informative graphs for the.

The RLGD method incorporates robust approaches and diagnostic procedures. For confirmation, the diagnostic procedure is used to compute potential.

The RLGD method ensures only correct high leverage points are identified and free from the swamping and masking effects. The real examples and the simulation results indicate that the RLGD method correctly identify the high leverage points increase the probability of the Detection of Capability DC and manage to reduce the number of swamping low leverage points decrease the probability of the False Alarm Rate FAR. The SPR method is less effective when residual outliers are present in the covariates.

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The attractive feature of the MSPR method is that it is easier to apply.