IPO estimation of heaviness of the distribution beyond regularly varying tails

Abstract

We introduce a completely novel method for estimation of the parameter which governs the tail behavior of the cumulative distribution function of the observed random variable. We call it Inverse Probabilities for p-Outside values (IPO) estimation method. We show that this approach is applicable for wider class of distributions than the one with regularly varying tails. We demonstrate that IPO method is a valuable competitor to regularly varying tails based estimation methods. Some of the properties of the estimators are derived. The results are illustrated by a convenient simulation study.     

On the Non-Commutative Neutrix Product of the Distributions x＋^λ and x^＋μ

Let f and g be distributions and let gn = （g ＊ δn）（x）, where δn （x） is a certain converging to the Dirac delta function. The non-commutative neutrix product fog of f and g to be the limit of the sequence {fgn }, provided its limit h exists in the sense that sequence is defined N-lim n-∞（f（x）g,, （x）, φ（x）〉 = （h（x）, φ（x）},for all functions p in 2. It is proved that （x^λ＋1n^px＋）0（x^μ＋1n^qx＋）=x＋^λμ1n^p＋qx＋,（x^λ-1n^qx-）=x-^λ＋μ1n^p＋qx-,for λ＋μ〈-1; λ,μ, λ＋μ≠-1,-2…and p,q=0,1,2……     

Generalizing the Pareto to the log-Pareto model and statistical inference

In this article we introduce a full-fledged statistical model of log-Pareto distribution functions (dfs) parametrized by two shape parameters and a scale parameter. Pareto dfs can be regained in the limit by varying parameters of log-Pareto dfs, whence the log-Pareto model can be regarded as an extension of the Pareto model. Log-Pareto dfs are first of all obtained by means of exponential transformations of Pareto dfs. We also indicate an iterated application of such a procedure. A class of generalized log-Pareto dfs is considered as well. In addition, power-pot (p-pot) stable dfs – related to p-max stable dfs – are introduced and log-Pareto dfs are identified as special cases. A modification of a quick (systematic) estimator is proposed as an initial estimator for the numerical computation of the maximum likelihood estimator (MLE) in the 3-parameter model.      

Asymptotic theory of simultaneous estimation of Poisson means

The Poisson distribution is often a good approximation to the underlying sampling distribution and is central to the study of categorical data. In this paper, we propose a new unified approach to an investigation of point properties of simultaneous estimations of Poisson population parameters with general quadratic loss functions. The main accent is made on the shrinkage estimation. We build a series of estimators that could be represented as a convex combination of linear statistics such as maximum likelihood estimator (benchmark estimator), restricted estimator, composite estimator, preliminary test estimator, shrinkage estimator, positive rule shrinkage estimator (James-Stein type estimator). All these estimators are represented in a general integrated estimation approach, which allows us to unify our investigation and order them with respect to the risk. A simulation study with numerical and graphical results is conducted to illustrate the properties of the investigated estimators.     

Extension of Colombeau algebra to derivatives of arbitrary order , . Application to ODEs and PDEs with entire and fractional derivatives

We give an extension of Colombeau algebra of generalized functions to fractional derivatives. We apply it in solving ODEs and PDEs with entire and fractional derivatives with respect to temporal and spatial variables. We give applications to ODEs and PDEs driven by fractional derivatives of delta distribution.     

A stochastic Bass innovation diffusion model for studying the growth of electricity consumption in Greece

In this paper a stochastic innovation diffusion model is proposed derived by introducing stochasticity into the well-known Bass model. The stochastic model is solved analytically by using the theory of reducible stochastic differential equations and the first moment of the resulting stochastic process is presented. The parameter estimators of the model are derived by using a procedure which provides the maximum likelihood estimators (MLE) using time series data. Finally, the model is applied to the data of electricity consumption in Greece. Using a simulation technique, it is possible to predict the performance of the consumption process by defining a subdomain to which all possible trajectories of the process should belong with a predefined probability. © 1997 by John Wiley & Sons, Ltd.     

M-Estimators Converging to a Stable Limit

We discuss the asymptotic linearization of multivariate M-estimators, when the limit distribution is stable. We consider two different types of kernels: VC and bracketing. When applied to the case of normal limits, our work improves the known results to obtain the limit distribution of M-estimators. We give weak conditions for the asymptotic normality of M-estimators over differentiable kernels. To obtain these results, we present an inequality on empirical processes satisfying a bracketing condition with respect to a norm smaller than the L₂ norm.     

Numerical Solutions for second-kind Volterra integral equations by Galerkin methods

In this paper, we study the global convergence for the numerical solutions of nonlinear Volterra integral equations of the second kind by means of Galerkin finite element methods. Global superconvergence properties are discussed by iterated finite element methods and interpolated finite element methods. Local superconvergence and iterative correction schemes are also considered by iterated finite element methods. We improve the corresponding results obtained by collocation methods in the recent papers [6] and [9] by H. Brunner, Q. Lin and N. Yan. Moreover, using an interpolation post-processing technique, we obtain a global superconvergence of the O(h ^2r )-convergence rate in the piecewise-polynomial space of degree not exceeding (r–1). As a by-product of our results, all these higher order numerical methods can also provide an a posteriori error estimator, which gives critical and useful information in the code development.     

δ函数SN方法求解含各向异性散射输运方程的本征值问题

将角通量分解成一个δ函数和一个低阶函数,分别采用解析方法和求积组来计算源项.利用δ函数S_N方法,计算了平几何条件下含各向异性散射单速输运方程的临界问题.分析和数值结果表明,采用数量较少的离散方向,能够达到较高计算精度.     

Knot graphs

We consider the equivalence classes of graphs induced by the unsigned versions of the Reidemeister moves on knot diagrams. Any graph that is reducible by some finite sequence of these moves, to a graph with no edges, is called a knot graph. We show that the class of knot graphs strictly contains the set of delta‐wye graphs. We prove that the dimension of the intersection of the cycle and cocycle spaces is an effective numerical invariant of these classes. © 2000 John Wiley & Sons, Inc. J Graph Theory 34: 100–111, 2000