On Brewer's Class of Robust Sampling Designs for Large-Scale Surveys.
On calculating the Laplace transform of a special quadratic functional of the Ornstein-Uhlenbeck velocity process
On calculation of stationary density of autoregressive processes
An iterative procedure for computation of stationary density of autoregressive processes is proposed. On an example with exponentially distributed white noise it is demonstrated that the procedure converges geometrically fast. The AR(1) and AR(2) models are analyzed in detail.
On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators
We study the impact of certain transformations within the class of Archimedean copulas. We give some admissibility conditions for these transformations, and define some equivalence classes for both transformations and generators of Archimedean copulas. We extend the r-fold composition of the diagonal section of a copula, from r ∈ N to r ∈ R. This extension, coupled with results on equivalence classes, gives us new expressions of transformations and generators. Estimators deriving directly from these...
On characteristic functions of kth record values from the generalized extreme value distribution and its characterization
Recurrence relations for the marginal, joint and conditional characteristic functions of kth record values from the generalized extreme value distribution are established. These relations are utilized to obtain recurrence relations for single, product and conditional moments of kth record values. Moreover, by making use of the recurrence relations the generalized extreme value distribution is characterized.
On Characterizations of Distributions by Expected Values of Order Statistics and Record Values with Gap.
On Characterizing Distributions by Conditional Expectations of Functions of Order Statistics.
On Classes of Estimators of the Variance Function of a Linear Estimator.
On comparing several populations with a control population
On computations with causal compositional models
The knowledge of causal relations provides a possibility to perform predictions and helps to decide about the most reasonable actions aiming at the desired objectives. Although the causal reasoning appears to be natural for the human thinking, most of the traditional statistical methods fail to address this issue. One of the well-known methodologies correctly representing the relations of cause and effect is Pearl's causality approach. The paper brings an alternative, purely algebraic methodology...
On computing the OC and ASN for some Markovian SPRT's
On concepts and measures of bivariate stochastic dependence
On concordance measures for discrete data and dependence properties of Poisson model.
On conditional independence and log-convexity
If conditional independence constraints define a family of positive distributions that is log-convex then this family turns out to be a Markov model over an undirected graph. This is proved for the distributions on products of finite sets and for the regular Gaussian ones. As a consequence, the assertion known as Brook factorization theorem, Hammersley–Clifford theorem or Gibbs–Markov equivalence is obtained.
On conditional independence and the relationship between sufficiency and invariance under the Bayesian point of view.
On Conditional Value at Risk (CoVaR) for tail-dependent copulas
The paper deals with Conditional Value at Risk (CoVaR) for copulas with nontrivial tail dependence. We show that both in the standard and the modified settings, the tail dependence function determines the limiting properties of CoVaR as the conditioning event becomes more extreme. The results are illustrated with examples using the extreme value, conic and truncation invariant families of bivariate tail-dependent copulas.
On consistency of kernel density estimators for randomly censored data : rates holding uniformly over adaptive intervals
On consistency of the MLE
On consistent -estimators: Tuning constants, unimodality and breakdown
On construction of confidence intervals for a mean of dependent data
In the report, the performance of several methods of constructing confidence intervals for a mean of stationary sequence is investigated using extensive simulation study. The studied approaches are sample reuse block methods which do not resort to bootstrap. It turns out that the performance of some known methods strongly depends on a model under consideration and on whether a two-sided or one-sided interval is used. Among the methods studied, the block method based on weak convergence result by...