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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....

Sample partitioning estimation for ergodic diffusions: application to Ornstein-Uhlenbeck diffusion

Luís Ramos (2010)

Discussiones Mathematicae Probability and Statistics

When a diffusion is ergodic its transition density converges to its invariant density, see Durrett (1998). This convergence enabled us to introduce a sample partitioning technique that gives in each sub-sample, maximum likelihood estimators. The averages of these being a natural choice as estimators. To compare our estimators with the optimal we obtained from martingale estimating functions, see Sørensen (1998), we used the Ornstein-Uhlenbeck process for which exact simulations can be carried out....

Sensitivity analysis in singular mixed linear models with constraints

Eva Fišerová, Lubomír Kubáček (2003)

Kybernetika

The singular mixed linear model with constraints is investigated with respect to an influence of inaccurate variance components on a decrease of the confidence level. The algorithm for a determination of the boundary of the insensitivity region is given. It is a set of all shifts of variance components values which make the tolerated decrease of the confidence level only. The problem about geometrical characterization of the confidence domain is also presented.

Sequential monitoring for change in scale

Ondřej Chochola (2008)

Kybernetika

We propose a sequential monitoring scheme for detecting a change in scale. We consider a stable historical period of length m . The goal is to propose a test with asymptotically small probability of false alarm and power 1 as the length of the historical period tends to infinity. The asymptotic distribution under the null hypothesis and consistency under the alternative hypothesis is derived. A small simulation study illustrates the finite sample performance of the monitoring scheme.

Some applications of probability generating function based methods to statistical estimation

Manuel L. Esquível (2009)

Discussiones Mathematicae Probability and Statistics

After recalling previous work on probability generating functions for real valued random variables we extend to these random variables uniform laws of large numbers and functional limit theorem for the empirical probability generating function. We present an application to the study of continuous laws, namely, estimation of parameters of Gaussian, gamma and uniform laws by means of a minimum contrast estimator that uses the empirical probability generating function of the sample. We test the procedure...

Spatial prediction of the mark of a location-dependent marked point process: How the use of a parametric model may improve prediction

Tomáš Mrkvička, François Goreaud, Joël Chadoeuf (2011)

Kybernetika

We discuss the prediction of a spatial variable of a multivariate mark composed of both dependent and explanatory variables. The marks are location-dependent and they are attached to a point process. We assume that the marks are assigned independently, conditionally on an unknown underlying parametric field. We compare (i) the classical non-parametric Nadaraya-Watson kernel estimator based on the dependent variable (ii) estimators obtained under an assumption of local parametric model where explanatory...

Statistical analysis of diabetes mellitus

Hilmar Drygas (2009)

Discussiones Mathematicae Probability and Statistics

This paper deals with an application of regression analysis to the regulation of the blood-sugar under diabetes mellitus. Section 2 gives a description of Gram-Schmidt orthogonalization, while Section 3 discusses the difference between Gauss-Markov estimation and Least Squares Estimation. Section 4 is devoted to the statistical analysis of the blood-sugar during the night. The response change of blood-sugar is explained by three variables: time, food and physical activity ("Bewegung"). At the beginning...

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