A Geometric Appraoch to Finite Sample and Large Deviation Properties in Two-Way ANOVA with Spherically Distributed Error Vectors.
A Modified Maximum Likelihood Test.
A note on almost sure convergence and convergence in measure
The present article studies the conditions under which the almost everywhere convergence and the convergence in measure coincide. An application in the statistical estimation theory is outlined as well.
A note on asymptotic linearity of -statistics in nonlinear models
A Note on Convergence in Probability Uniformly in a Parameter
A Note on Efficiency.
A note on Hammersley's inequality for estimating the normal integer mean.
A note on the strong consistency of least squares estimates
The strong consistency of least squares estimates in multiples regression models with i.i.d. errors is obtained under assumptions on the design matrix and moment restrictions on the errors.
A Proof of Asymptotic Normality for some VARX Models.
A remark on existence of statistical functionals
A simple robust estimator of correlations for Gaussian stationary random sequences
ABSTRACT - Procedures for Epidemic Alternatives.
Accuracies in the theory of logistic models.
Adaptive estimation of the transition density of a Markov chain
Adaptive goodness-of-fit testing from indirect observations
In a convolution model, we observe random variables whose distribution is the convolution of some unknown density f and some known noise density g. We assume that g is polynomially smooth. We provide goodness-of-fit testing procedures for the test H0: f=f0, where the alternative H1is expressed with respect to -norm (i.e. has the form ). Our procedure is adaptive with respect to the unknown smoothness parameterτ of f. Different testing rates (ψn) are obtained according to whether f0 is polynomially...
Adaptive maximum-likelihood-like estimation in linear models. II. Asymptotic normality
Adaptive maximum-likelihood-like estimation in linear models. I. Consistency
Adaptive trimmed likelihood estimation in regression
In this paper we derive an asymptotic normality result for an adaptive trimmed likelihood estimator of regression starting from initial high breakdownpoint robust regression estimates. The approach leads to quickly and easily computed robust and efficient estimates for regression. A highlight of the method is that it tends automatically in one algorithm to expose the outliers and give least squares estimates with the outliers removed. The idea is to begin with a rapidly computed consistent robust...
Almost Unbiased Estimation in Multiplicative Models.
Alternative parametric estimation in the exponential case under random censorship