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Random thresholds for linear model selection

Marc Lavielle, Carenne Ludeña (2008)

ESAIM: Probability and Statistics

A method is introduced to select the significant or non null mean terms among a collection of independent random variables. As an application we consider the problem of recovering the significant coefficients in non ordered model selection. The method is based on a convenient random centering of the partial sums of the ordered observations. Based on L-statistics methods we show consistency of the proposed estimator. An extension to unknown parametric distributions is considered. Simulated examples...

Reliability in the Rasch model

Patrícia Martinková, Karel Zvára (2007)

Kybernetika

This paper deals with the reliability of composite measurement consisting of true-false items obeying the Rasch model. A definition of reliability in the Rasch model is proposed and the connection to the classical definition of reliability is shown. As a modification of the classical estimator Cronbach's alpha, a new estimator logistic alpha is proposed. Finally, the properties of the new estimator are studied via simulations in the Rasch model.

Risk bounds for new M-estimation problems

Nabil Rachdi, Jean-Claude Fort, Thierry Klein (2013)

ESAIM: Probability and Statistics

In this paper, we consider a new framework where two types of data are available: experimental data Y1,...,Yn supposed to be i.i.d from Y and outputs from a simulated reduced model. We develop a procedure for parameter estimation to characterize a feature of the phenomenon Y. We prove a risk bound qualifying the proposed procedure in terms of the number of experimental data n, reduced model complexity and computing budget m. The method we present is general enough to cover a wide range of applications....

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