Random walks on trees and matchings.
Ranggrößen als Schätzfunktionen.
Rank conditions for estimability of covariances
Rate of convergence for a class of RCA estimators
This work deals with Random Coefficient Autoregressive models where the error process is a martingale difference sequence. A class of estimators of unknown parameter is employed. This class was originally proposed by Schick and it covers both least squares estimator and maximum likelihood estimator for instance. Asymptotic behavior of such estimators is explored, especially the rate of convergence to normal distribution is established.
Ratio-type Estimatorsfor Multinomial Probabilities in two-phase Sampling
Recovering decay rates from noisy measurements with maximum entropy in the mean.
Recursive estimation in autoregressive models with additive outliers
Recursive linear estimation for discrete-time systems in the presence of different multiplicative observation noises.
Reference points based recursive approximation
The paper studies polynomial approximation models with a new type of constraints that enable to get estimates with significant properties. Recently we enhanced a representation of polynomials based on three reference points. Here we propose a two-part cubic smoothing scheme that leverages this representation. The presence of these points in the model has several consequences. The most important one is the fact that by appropriate location of the reference points the resulting approximant of two...
Relationship between Generalized h-Statistics and Polykays.
Reliability estimation from Luceno distributed data.
Reliability in the Rasch model
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.
Remarques sur le maximum de vraisemblance.
Some paradoxes on the maximum likelihood principle are presented and commented. We consider the properties of the maximum likelihood estimators as a particular case of the M-estimators. We propose a unified theory which includes non-dominated models. Several examples are given.
Réponse de M. Cramer au commentaire de M. Rey
Risk bounds for new M-estimation problems
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....
Robust estimation based on spacings in weighted exponential models
Using Zieliński's (1977, 1983) formalization of robustness Błażej (2007) obtained uniformly most bias-robust estimates (UMBREs) of the scale parameter for some statistical models (including the exponential model), in a class of linear functions of order statistics, when violations of the models are generated by weight functions. In this paper the UMBRE of the scale parameter, based on spacings, in two weighted exponential models is derived. Extensions of results of Bartoszewicz (1986, 1987) are...
Robust estimation of the scale and weighted distributions
The concept of robustness given by Zieliński (1977) is considered in cases where violations of models are generated by weight functions. Uniformly most bias-robust estimates of the scale parameter, based on order statistics, are obtained for some statistical models. Extensions of results of Zieliński (1983) and Bartoszewicz (1986) are given.
Robust Estimation of the Structural Errors-in-Variables Model.
Robust estimations in classical regression models versus robust estimations in fuzzy regression models
In this paper are presented two robust estimators of unknown fuzzy parameters in the fuzzy regression model and investigated the relationship between these robust estimators in the classical regression model and in the fuzzy regression model.
Robust median estimator for generalized linear models with binary responses
The paper investigates generalized linear models (GLM's) with binary responses such as the logistic, probit, log-log, complementary log-log, scobit and power logit models. It introduces a median estimator of the underlying structural parameters of these models based on statistically smoothed binary responses. Consistency and asymptotic normality of this estimator are proved. Examples of derivation of the asymptotic covariance matrix under the above mentioned models are presented. Finally some comments...