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

Page 1 Next

Displaying 1 – 20 of 23

Showing per page

Neural networks using Bayesian training

Gabriela Andrejková, Miroslav Levický (2003)

Kybernetika

Bayesian probability theory provides a framework for data modeling. In this framework it is possible to find models that are well-matched to the data, and to use these models to make nearly optimal predictions. In connection to neural networks and especially to neural network learning, the theory is interpreted as an inference of the most probable parameters for the model and the given training data. This article describes an application of Neural Networks using the Bayesian training to the problem...

New estimates and tests of independence in semiparametric copula models

Salim Bouzebda, Amor Keziou (2010)

Kybernetika

We introduce new estimates and tests of independence in copula models with unknown margins using φ -divergences and the duality technique. The asymptotic laws of the estimates and the test statistics are established both when the parameter is an interior or a boundary value of the parameter space. Simulation results show that the choice of χ 2 -divergence has good properties in terms of efficiency-robustness.

New M-estimators in semi-parametric regression with errors in variables

Cristina Butucea, Marie-Luce Taupin (2008)

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

In the regression model with errors in variables, we observe n i.i.d. copies of (Y, Z) satisfying Y=fθ0(X)+ξ and Z=X+ɛ involving independent and unobserved random variables X, ξ, ɛ plus a regression function fθ0, known up to a finite dimensional θ0. The common densities of the Xi’s and of the ξi’s are unknown, whereas the distribution of ɛ is completely known. We aim at estimating the parameter θ0 by using the observations (Y1, Z1), …, (Yn, Zn). We propose an estimation procedure based on the least...

Nonlinear error propagation law

Lubomír Kubáček (1996)

Applications of Mathematics

The error propagation law is investigated in the case of a nonlinear function of measured data with non-negligible uncertainty.

Nonquadratic stabilization of continuous-time systems in the Takagi-Sugeno form

Miguel Bernal, Petr Hušek, Vladimír Kučera (2006)

Kybernetika

This paper presents a relaxed scheme for controller synthesis of continuous- time systems in the Takagi-Sugeno form, based on non-quadratic Lyapunov functions and a non-PDC control law. The relaxations here provided allow state and input dependence of the membership functions’ derivatives, as well as independence on initial conditions when input constraints are needed. Moreover, the controller synthesis is attainable via linear matrix inequalities, which are efficiently solved by commercially available...

Note on a Calibration Problem: Selected Results and Extensions of Professor Kubáček’s

Gejza Wimmer, Viktor Witkovský (2011)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

Professor Lubomír Kubáček has provided exceptional contributions to mathematical statistics and its applications. Because of his excellent knowledge in mathematical statistics as well as in the different fields of natural and especially technical sciences, he contributed to solution of a large number of real world problems. The continuation of Professor Kubáček’s scientific work and his scientific school is demonstrated by the results of his numerous students. Here we present just one illustration...

Note on the estimation of parameters of the mean and the variance in n -stage linear models

Júlia Volaufová (1988)

Aplikace matematiky

The paper deals with the estimation of the unknown vector parameter of the mean and the parameters of the variance in the general n -stage linear model. Necessary and sufficient conditions for the existence of the uniformly minimum variance unbiased estimator (UMVUE) of the mean-parameter under the condition of normality are given. The commonly used least squares estimators are used to derive the expressions of UMVUE-s in a simple form.

Nuevas medidas de información paramétricas reales basadas en la matriz de Fisher.

Agustín Turrero Nogués (1989)

Trabajos de Estadística

Se proponen en este trabajo nuevos funcionales reales de la matriz de información de Fisher como medidas de información paramétricas. Se analizan las propiedades de dichas medidas. Se presenta un método sencillo, basado en la matriz de Fisher, para obtener medidas de información paramétricas reales con la propiedad de invariancia bajo transformaciones biyectivas del espacio paramétrico.

Currently displaying 1 – 20 of 23

Page 1 Next