Was the theory of chance born by chance? (La théorie du hasard est-elle née par hasard?)
Wavelet estimation of the long memory parameter for Hermite polynomial of gaussian processes
We consider stationary processes with long memory which are non-Gaussian and represented as Hermite polynomials of a Gaussian process. We focus on the corresponding wavelet coefficients and study the asymptotic behavior of the sum of their squares since this sum is often used for estimating the long–memory parameter. We show that the limit is not Gaussian but can be expressed using the non-Gaussian Rosenblatt process defined as a Wiener–Itô integral of order 2. This happens even if the original...
Wavelets for time series analysis - a survey and new results
Weak Consistency: A New Approach to Consistency in the Saaty’s Analytic Hierarchy Process
In the decision making methods based on the pairwise comparison there is very important to enter the preferences of compared elements in the rational way. Only in this case we are able to obtain the reasonable solution. In the Analytic Hierarchy Process (AHP) there is set a strict consistency condition in order to keep the rationality of preference intensities between compared elements. But this requirement for the Saaty’s matrix is not achievable in the real situations because of the Saaty’s scale...
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 -consistency of the least weighted squares under heteroscedasticity
Weak nonlinearity in a model which arises from the Helmert transformation
Nowadays, the algorithm most frequently used for determination of the estimators of parameters which define a transformation between two coordinate systems (in this case the Helmert transformation) is derived under one unreal assumption of errorless measurement in the first system. As it is practically impossible to ensure errorless measurements, we can hardly believe that the results of this algorithm are “optimal”. In 1998, Kubáček and Kubáčková proposed an algorithm which takes errors in both...
Weak nonlinearity of growth curve models
Weak pointwise consistency of the cross validatory window estimate in non parametric regression estimation
Weakly nonlinear constraints in regression models
Weakly nonlinear regression model with constraints I: nonlinear hypothesis
The problem considered is under which conditions in weakly nonlinear regression model with constraints I a weakly nonlinear hypothesis can be tested by linear methods. The aim of the paper is to find a region around the approximate value of the regression parameter with the following property. If we are certain that the actual value of the regression parameter is in this region, then the linear method of testing can be used without any significant deterioration of the inference.
Weakly stationary processes with non–positive autocorrelations
We deal with real weakly stationary processes with non-positive autocorrelations , i. e. it is assumed that for all . We show that such processes have some special interesting properties. In particular, it is shown that each such a process can be represented as a linear process. Sufficient conditions under which the resulting process satisfies for all are provided as well.
Weighted Elastic Net Model for Mass Spectrometry Imaging Processing
In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promising new technique for protein analysis from intact biological tissues. Though it has shown great potential and is very promising for rapid mapping of protein localization and the detection of sizeable differences in protein expression, challenges remain in data processing due to the difficulty of high dimensionality and the fact that the number of input variables in...
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.
Weighting, likelihood ratio order and life distributions
We use weighted distributions with a weight function being a ratio of two densities to obtain some results of interest concerning life and residual life distributions. Our theorems are corollaries from results of Jain et al. (1989) and Bartoszewicz and Skolimowska (2006).
What does intrinsic mean in statistical estimation?
In this paper we review different meanings of the word intrinsic in statistical estimation, focusing our attention on the use of this word in the analysis of the properties of an estimator. We review the intrinsic versions of the bias and the mean square error and results analogous to the Cramér-Rao inequality and Rao-Blackwell theorem. Different results related to the Bernoulli and normal distributions are also considered.