All a b c d e f g h i j k l m n o p q r s t u v w x y z Other

Previous Page 14 Next

Displaying 261 – 280 of 383

Showing per page

Estimation of the transition density of a Markov chain

Mathieu Sart (2014)

Annales de l'I.H.P. Probabilités et statistiques

We present two data-driven procedures to estimate the transition density of an homogeneous Markov chain. The first yields a piecewise constant estimator on a suitable random partition. By using an Hellinger-type loss, we establish non-asymptotic risk bounds for our estimator when the square root of the transition density belongs to possibly inhomogeneous Besov spaces with possibly small regularity index. Some simulations are also provided. The second procedure is of theoretical interest and leads...

Estimation of variance components in mixed linear models

Júlia Volaufová, Viktor Witkovský (1992)

Applications of Mathematics

The MINQUE of the linear function ∫ ' ϑ of the unknown variance-components parameter ϑ in mixed linear model under linear restrictions of the type 𝐑 ϑ = c is defined and derived. As an illustration of this estimator the example of the one-way classification model with the restrictions ϑ 1 = k ϑ 2 , where k ≥ 0 , is given.

Estimation of variances in a heteroscedastic RCA(1) model

Hana Janečková (2002)

Kybernetika

The paper concerns with a heteroscedastic random coefficient autoregressive model (RCA) of the form X t = b t X t - 1 + Y t . Two different procedures for estimating σ t 2 = E Y t 2 , σ b 2 = E b t 2 or σ B 2 = E ( b t - E b t ) 2 , respectively, are described under the special seasonal behaviour of σ t 2 . For both types of estimators strong consistency and asymptotic normality are proved.

Estimation variances for parameterized marked Poisson processes and for parameterized Poisson segment processes

Tomáš Mrkvička (2004)

Commentationes Mathematicae Universitatis Carolinae

A complete and sufficient statistic is found for stationary marked Poisson processes with a parametric distribution of marks. Then this statistic is used to derive the uniformly best unbiased estimator for the length density of a Poisson or Cox segment process with a parametric primary grain distribution. It is the number of segments with reference point within the sampling window divided by the window volume and multiplied by the uniformly best unbiased estimator of the mean segment length.

Estimator selection in the gaussian setting

Yannick Baraud, Christophe Giraud, Sylvie Huet (2014)

Annales de l'I.H.P. Probabilités et statistiques

We consider the problem of estimating the mean f of a Gaussian vector Y with independent components of common unknown variance σ 2 . Our estimation procedure is based on estimator selection. More precisely, we start with an arbitrary and possibly infinite collection 𝔽 of estimators of f based on Y and, with the same data Y , aim at selecting an estimator among 𝔽 with the smallest Euclidean risk. No assumptions on the estimators are made and their dependencies with respect to Y may be unknown. We establish...

Estimators and tests for variance components in cross nested orthogonal designs

Miguel Fonseca, João Tiago Mexia, Roman Zmyślony (2003)

Discussiones Mathematicae Probability and Statistics

Explicit expressions of UMVUE for variance components are obtained for a class of models that include balanced cross nested random models. These estimators are used to derive tests for the nullity of variance components. Besides the usual F tests, generalized F tests will be introduced. The separation between both types of tests will be based on a general theorem that holds even for mixed models. It is shown how to estimate the p-value of generalized F tests.

Currently displaying 261 – 280 of 383