Maximum Probability Estimators in the Case of Exponential Distribution.
Mean square error matrix of an approximate least squares estimator in a nonlinear regression model with correlated errors.
Minimum distance estimator for a hyperbolic stochastic partial differentialequation
We study a minimum distance estimator in -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.
Minimum Kolmogorov Distance Estimates of Parameters an Parametrized Distributions.
Modelling of Repairable Systems with Various Degrees of Repair.
Moindres carrés pondérés et poursuite
Motivation, existence and equivariance of -estimators
Necessary and Sufficient Conditions for Consistency of Generalized M-Estimates.
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...
Nonnegative multivariate processes
Nonparametric estimation of the ratios of derivatives of a multivariate distribution density from dependent observations.
Note on dispersion of .
Note on Dispersion of Xalpha
Number of hidden states and memory: a joint order estimation problem for Markov chains with Markov regime
This paper deals with order identification for Markov chains with Markov regime (MCMR) in the context of finite alphabets. We define the joint order of a MCMR process in terms of the number k of states of the hidden Markov chain and the memory m of the conditional Markov chain. We study the properties of penalized maximum likelihood estimators for the unknown order (k, m) of an observed MCMR process, relying on information theoretic arguments. The novelty of our work relies in the joint...
On a class of estimators in a multivariate RCA(1) model
This work deals with a multivariate random coefficient autoregressive model (RCA) of the first order. A class of modified least-squares estimators of the parameters of the model, originally proposed by Schick for univariate first-order RCA models, is studied under more general conditions. Asymptotic behavior of such estimators is explored, and a lower bound for the asymptotic variance matrix of the estimator of the mean of random coefficient is established. Finite sample properties are demonstrated...
On a method of estimating parameters in non-negative ARMA models
On adaptive estimation in nonlinear regression
On asymptotics of the maximum likelihood scale invariant estimator of the shape parameter of the gamma distribution
The maximum likelihood scale invariant estimator of the shape parameter of the gamma distribution, proposed by the authors [Statist. Probab. Lett. 78 (2008)], is considered. The asymptotics of the mean square error of this estimator, with respect to that of the usual maximum likelihood estimator, is established.
On Bayesian estimation in an exponential distribution under random censorship
The paper gives some basic ideas of both the construction and investigation of the properties of the Bayesian estimates of certain parametric functions of the parent exponential distribution under the model of random censorship assuming the Koziol–Green model. Various prior distributions are investigated and the corresponding estimates are derived. The stress is put on the asymptotic properties of the estimates with the particular stress on the Bayesian risk. Small sample properties are studied...