On Hodges-Lehmann optimality of LR tests
On interactions in contingency tables
On invariant density estimation for ergodic diffusion processes.
We present a review of several results concerning invariant density estimation by observations of ergodic diffusion process and some related problems. In every problem we propose a lower minimax bound on the risks of all estimators and then we construct an asymptotically efficient estimator.
On Klotz's result on the asymptotic efficiency for the signed rank tests
On -estimators viewed as -estimators
On least squares estimation of Fourier coefficients and of the regression function
On locally most powerful rank tests of independence
On Measures of Uniformly Distributed Sequences and Benford's Law.
On Median Unbiased Estimates.
On Minimum Distance Estimators for Unimodel Densities.
On nonparametric estimators of location of maximum
On optimality of the orthogonal block design
In the paper a usual block design with treatment effects fixed and block effects random is considered. To compare experimental design the asymptotic covariance matrix of a robust estimator proposed by Bednarski and Zontek (1996) for simultaneous estimation of shift and scale parameters is used. Asymptotically A- and D- optimal block designs in the class of designs with bounded block sizes are characterized.
On order statistics for random sample size having compound binomial and Poisson distributions
On orthogonal series estimation of bounded regression functions
The problem of nonparametric estimation of a bounded regression function , [a,b] ⊂ ℝ, d ≥ 1, using an orthonormal system of functions , k=1,2,..., is considered in the case when the observations follow the model , i=1,...,n, where and are i.i.d. copies of independent random variables X and η, respectively, the distribution of X has density ϱ, and η has mean zero and finite variance. The estimators are constructed by proper truncation of the function , where the coefficients are determined...
On pointwise adaptive curve estimation based on inhomogeneous data
We want to recover a signal based on noisy inhomogeneous data (the amount of data can vary strongly on the estimation domain). We model the data using nonparametric regression with random design, and we focus on the estimation of the regression at a fixed point x0 with little, or much data. We propose a method which adapts both to the local amount of data (the design density is unknown) and to the local smoothness of the regression function. The procedure consists of a local polynomial...
On power series, Bell polynomials, Hardy-Ramanujan-Rademacher problem and its statistical applications
On robust GMM estimation with applications in economics and finance
Generalized Methods of Moments (GMM) estimators are a popular tool in econometrics since introduced by Hansen (1982), because this approach provides feasible solutions for many problems present in economic data where least squares or maximum likelihood methods fail when naively applied. These problems may arise in errors-in-variable regression, estimation of labor demand curves, and asset pricing in finance, which are discussed here. In this paper we study a GMM estimator for the rank modelingapproach...
On second order efficiency of a robust test and approximations of its error probabilities
On some properties of estimators of a probability density
On some two-step density estimation method.