Use of a Median Test for a Generalized Behrens-Fischer Problem.
Using auxiliary information in statistical function estimation
In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based...
Using auxiliary information in statistical function estimation
In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based...
Using randomization to improve performance of a variance estimator of strongly dependent errors
We consider a fixed-design regression model with long-range dependent errors which form a moving average or Gaussian process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We estimate the variance of the errors using the Rice estimator. The estimator is shown to exhibit weak (i.e. in probability) consistency. Simulation results confirm this property for moderate and large sample sizes when randomization...
Valeurs extrémales de suites stationnaires de variables aléatoires m-dépendantes
Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models
In testing that a given distribution Pbelongs to a parameterized family , one is often led to compare a nonparametric estimateAn of some functional A of P with an element Aθn corresponding to an estimate θn of θ. In many cases, the asymptotic distribution of goodness-of-fit statistics derived from the process n1/2(An−Aθn) depends on the unknown distribution P. It is shown here that if the sequences An and θn of estimators are regular in some sense, a parametric bootstrap approach yields valid approximations...
Variable selection through CART
This paper deals with variable selection in regression and binary classification frameworks. It proposes an automatic and exhaustive procedure which relies on the use of the CART algorithm and on model selection via penalization. This work, of theoretical nature, aims at determining adequate penalties, i.e. penalties which allow achievement of oracle type inequalities justifying the performance of the proposed procedure. Since the exhaustive procedure cannot be realized when the number of variables...
Variance function estimation via model selection
The problem of estimating an unknown variance function in a random design Gaussian heteroscedastic regression model is considered. Both the regression function and the logarithm of the variance function are modelled by piecewise polynomials. A finite collection of such parametric models based on a family of partitions of support of an explanatory variable is studied. Penalized model selection criteria as well as post-model-selection estimates are introduced based on Maximum Likelihood (ML) and Restricted...
Variational theorems in gnostical theory of uncertain data
Various limit theorems for ratios from the uniform distribution
In this paper, we consider the ratios of order statistics in samples from uniform distribution and establish strong and weak laws for these ratios.
Verbesserung des nicht-randomisierten exakten Testes von R.A. Fisher
Vergleich des verallgemeinerten nichtzentralen t-Tests mit dem Binomialtest.
Verhalten des Chi-Quadrat-Tests für Prüfung der Gleichwahrscheinlichkeit der Lotto-Zahlen bei Nichtgültigkeit der Hypothese.
Weak convergence for empirical processes of associated sequences
Weak convergence of a Dirichlet-multinomial process.
Weak convergence of linear rank statistics
Weak Hölder convergence of processes with application to the perturbed empirical process
We consider stochastic processes as random elements in some spaces of Hölder functions vanishing at infinity. The corresponding scale of spaces is shown to be isomorphic to some scale of Banach sequence spaces. This enables us to obtain some tightness criterion in these spaces. As an application, we prove the weak Hölder convergence of the convolution-smoothed empirical process of an i.i.d. sample under a natural assumption about the regularity of the marginal distribution function F of the...
Weak pointwise consistency of the cross validatory window estimate in non parametric regression estimation
Weighted halfspace depth
Generalised halfspace depth function is proposed. Basic properties of this depth function including the strong consistency are studied. We show, on several examples that our depth function may be considered to be more appropriate for nonsymetric distributions or for mixtures of distributions.
Weighted uniform consistency of kernel density estimators with general bandwidth sequences.