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Gaussian model selection

Lucien Birgé, Pascal Massart (2001)

Journal of the European Mathematical Society

Our purpose in this paper is to provide a general approach to model selection via penalization for Gaussian regression and to develop our point of view about this subject. The advantage and importance of model selection come from the fact that it provides a suitable approach to many different types of problems, starting from model selection per se (among a family of parametric models, which one is more suitable for the data at hand), which includes for instance variable selection in regression models,...

Generalized linear estimate of functions of random matrix arguments

Pavel Kovanic (1974)

Kybernetika

Generalized method of least squares collocation

Ludmila Kubáčková, Lubomír Kubáček (1982)

Aplikace matematiky

Two general solutions of the collocation problem of physical geodesy are given. Their mutual equivalency and equivalency of them to the classical solution in the regular case are proved. The regularity means the non-singularity of the covariance matrix of those random variables by outcomes of which the measured values of the gravitational field are generated.

Goodness of fit procedures for the multivariate linear model based on spherical harmonics.

Quiroz, A.J., Tapia, J.M. (2007)

Divulgaciones Matemáticas

Goodness-of-fit tests for parametric regression models based on empirical characteristic functions

Marie Hušková, Simon G. Meintanis (2009)

Kybernetika

Test procedures are constructed for testing the goodness-of-fit in parametric regression models. The test statistic is in the form of an L2 distance between the empirical characteristic function of the residuals in a parametric regression fit and the corresponding empirical characteristic function of the residuals in a non-parametric regression fit. The asymptotic null distribution as well as the behavior of the test statistic under contiguous alternatives is investigated. Theoretical results are...

Graphical display in outlier diagnostics; adequacy and robustness.

Nethal K. Jajo (2005)

SORT

Outlier robust diagnostics (graphically) using Robustly Studentized Robust Residuals (RSRR) and Partial Robustly Studentized Robust Residuals (PRSRR) are established. One problem with some robust residual plots is that the residuals retain information from certain predicated values (Velilla, 1998). The RSRR and PRSRR techniques are unaffected by this complication and as a result they provide more interpretable results.