Necessary and Sufficient Conditions for Consistency of Generalized M-Estimates.
Neural networks using Bayesian training
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
We introduce new estimates and tests of independence in copula models with unknown margins using -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 -divergence has good properties in terms of efficiency-robustness.
New M-estimators in semi-parametric regression with errors in variables
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...
New models in durability tool-testing: pseudo-Weibull distribution
Neymann-Pearson type test of hypotheses about random parameters
Non-Asymptotic Confidence Bounds for Stochastic Approximation Algorithms with Constant Step Size.
Nonlinear error propagation law
The error propagation law is investigated in the case of a nonlinear function of measured data with non-negligible uncertainty.
Nonnegative multivariate processes
Nonparametric estimation of the ratios of derivatives of a multivariate distribution density from dependent observations.
Nonquadratic stabilization of continuous-time systems in the Takagi-Sugeno form
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...
Nonsensitiveness regions in universal models
Note on a Calibration Problem: Selected Results and Extensions of Professor Kubáček’s
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 dispersion of .
Note on Dispersion of Xalpha
Note on the chi-square statistic of association in 2x2 contingency tables and the correction for continuity
Note on the estimation of parameters of the mean and the variance in -stage linear models
The paper deals with the estimation of the unknown vector parameter of the mean and the parameters of the variance in the general -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.
Note on the truncated Rayleigh variate
Note on unbiased estimability of the larger of two mean values
An unbiased estimator of the larger of two mean values is constructed provided that the number of observations is random.
Nuevas medidas de información paramétricas reales basadas en la matriz de Fisher.
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.