Asymptotically minimax estimation of a constrained Poisson vector via polydisc transforms
Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients.
Asymptotically normal estimation of a multidimensional parameter in the linear-fractional regression problem.
Asymptotically normal estimation of a parameter in a linear-fractional regression problem.
Asymptotically normal explicit estimation of parameters in the Michaelis--Menten equation.
Asymptotically optimal estimation in a linear fractional regression problem with random errors in coefficients.
Asymptotically optimal estimation in linear regression in the case of violation of some classical assumptions.
Asymptotically Optimal tests in the Independent not Identically Distributed case
Asymptotische Verteilungen einiger Schätzverfahren bei Trend und Fehlern in den Variablen.
Bias of LS estimators in nonlinear regression models with constraints. Part II: Biadditive models
General results giving approximate bias for nonlinear models with constrained parameters are applied to bilinear models in anova framework, called biadditive models. Known results on the information matrix and the asymptotic variance matrix of the parameters are summarized, and the Jacobians and Hessians of the response and of the constraints are derived. These intermediate results are the basis for any subsequent second order study of the model. Despite the large number of parameters involved,...
Bias of LS estimators in nonlinear regression models with constraints. Part I: General case
We derive expressions for the asymptotic approximation of the bias of the least squares estimators in nonlinear regression models with parameters which are subject to nonlinear equality constraints. The approach suggested modifies the normal equations of the estimator, and approximates them up to , where is the number of observations. The “bias equations” so obtained are solved under different assumptions on constraints and on the model. For functions of the parameters the invariance of the approximate...
Branching Processes with Immigration and Integer-valued Time Series
In this paper, we indicate how integer-valued autoregressive time series Ginar(d) of ordre d, d ≥ 1, are simple functionals of multitype branching processes with immigration. This allows the derivation of a simple criteria for the existence of a stationary distribution of the time series, thus proving and extending some results by Al-Osh and Alzaid [1], Du and Li [9] and Gauthier and Latour [11]. One can then transfer results on estimation in subcritical multitype branching processes to stationary...
Bregman superquantiles. Estimation methods and applications
In thiswork,we extend some parameters built on a probability distribution introduced before to the casewhere the proximity between real numbers is measured by using a Bregman divergence. This leads to the definition of the Bregman superquantile (thatwe can connect with severalworks in economy, see for example [18] or [9]). Axioms of a coherent measure of risk discussed previously (see [31] or [3]) are studied in the case of Bregman superquantile. Furthermore,we deal with asymptotic properties of...
Censored regression models with double exponential error distributions: an iterattive estimation procedure based on medians for correcting bias.
In this paper, we consider a simple iterative estimation procedure for censored regression models with symmetrical exponential error distributions. Although each step requires to impute the censored data with conditional medians, its tractability is guaranteed as well as its convergence at geometrical rate. Finally, as the final estimate coincides with a Huber M-estimator, its consistency and asymptotic normality are easily proved.
Change-point estimator in continuous quadratic regression
The paper deals with the asymptotic distribution of the least squares estimator of a change point in a regression model where the regression function has two phases --- the first linear and the second quadratic. In the case when the linear coefficient after change is non-zero the limit distribution of the change point estimator is normal whereas it is non-normal if the linear coefficient is zero.
Change-point estimator in gradually changing sequences
Recently Hušková (1998) has studied the least squares estimator of a change-point in gradually changing sequence supposing that the sequence increases (or decreases) linearly after the change-point. The present paper shows that the limit behavior of the change-point estimator for more complicated gradual changes is similar. The limit variance of the estimator can be easily calculated from the covariance function of a limit process.
Compact hypothesis and extremal set estimators
In extremal estimation theory the estimators are local or absolute extremes of functions defined on the cartesian product of the parameter by the sample space. Assuming that these functions converge uniformly, in a convenient stochastic way, to a limit function g, set estimators for the set ∇ of absolute maxima (minima) of g are obtained under the compactness assumption that ∇ is contained in a known compact U. A strongly consistent test is presented for this assumption. Moreover, when the true...
Conditions for local asymptotic normality of experiment sequences
Confidence intervals for reliability functions of an exponential distribution under random censorship
Consistency and Asymptotic Normality of Maximum Likelihood Estimation for Gaussian Markov Processes from Discrete Obersvations.