Weighted least squares estimators for the Parzen tail index

Periodica Mathematica Hungarica - Estimation of the tail index of heavy-tailed distributions and its applications are essential in many research areas. We propose a class of weighted least squares...     

Testing the tail index in autoregressive models

We propose a class of nonparametric tests on the Pareto tail index of the innovation distribution in the linear autoregressive model. The simulation study illustrates a good performance of the tests. Such tests have various applications in a study of flood flows, rainflow data, behavior of solids, atmospheric ozone layer and reliability analysis, in communication engineering, in stock markets and insurance. Research of J. Jurečková and J. Picek was partly supported by Czech Republic Grant 201/05/2340, by the Research Project LC06024 and by the NSF grant DMS 0071619. Research of H. L. Koul was partly supported by the NSF grants DMS 0071619 and 0704130.     

Uniform in bandwidth consistency of kernel estimators of the tail index

We consider a class of kernel estimators [^(t)]_n,hhat{tau}_{n,h} of the tail index of a Pareto-type distribution, which generalizes and includes the classical Hill estimator [^(a)]_n,khat{a}_{n,k}. It is well-known that [^(a)]_n,khat{a}_{n,k} is a consistent estimator of the tail index if and only if k→ ∞ and k/n→0. Under suitable assumptions on the kernel, [^(t)] _n,hhat{tau} _{n,h} is consistent whenever the bandwidth is taken to be a sequence of non-random numbers satisfying h _n →0 and nh _n → ∞. We extend this result and prove the consistency uniformly over a certain range of bandwidths. This permits the treatment of estimators of the tail index based upon data-dependent bandwidths, which are often used in practice. In the process, we establish a uniform in bandwidth result for kernel-type regression estimators with a fixed design, which will likely be of separate interest.     

Bias-reduced estimators for bivariate tail modelling

Ledford and Tawn (1997) introduced a flexible bivariate tail model based on the coefficient of tail dependence and on the dependence of the extreme values of the random variables. In this paper, we extend the concept by specifying the slowly varying part of the model as done by Hall (1982) with the univariate case. Based on Beirlant et al. (2009), we propose a bias-reduced estimator for the coefficient of tail dependence and for the estimation of small tail probabilities. We discuss the properties of these estimators via simulations and a real-life example. Furthermore, we discuss some theoretical asymptotic aspects of this approach.     

Reduced‐bias kernel estimators of a positive extreme value index

In this paper, we deal with the semi‐parametric estimation of the extreme value index, an important parameter in extreme value analysis. It is well known that many classic estimators, such as the Hill estimator, reveal a strong bias. This problem motivated the study of two classes of kernel estimators. Those classes generalize the classical Hill estimator and have a tuning parameter that enables us to modify the asymptotic mean squared error and eventually to improve their efficiency. Since the improvement in efficiency is not very expressive, we also study new reduced bias estimators based on the two classes of kernel statistics. Under suitable conditions, we prove their asymptotic normality. Moreover, an asymptotic comparison, at optimal levels, shows that the new classes of reduced bias estimators are more efficient than other reduced bias estimator from the literature. An illustration of the finite sample behaviour of the kernel reduced‐bias estimators is also provided through the analysis of a data set in the field of insurance.     

Median based covariogram estimators reduce bias

The usual product moment covariogram estimator of a Gaussian process can have appreciable bias. This article shows that the bias is decreased when the sample mean $\text{X}$ in the estimator is replaced with med(X), the sample median (provided a mild condition on the negative covariances is satisfied). Identical conclusions are drawn, even when the Gaussian process is (symmetrically) contaminated.     

Finite sample tail behavior of multivariate location estimators

A finite sample performance measure of multivariate location estimators is introduced based on “tail behavior”. The tail performance of multivariate “monotone” location estimators and the halfspace depth based “non-monotone” location estimators including the Tukey halfspace median and multivariate L-estimators is investigated. The connections among the finite sample performance measure, the finite sample breakdown point, and the halfspace depth are revealed. It turns out that estimators with high breakdown point or halfspace depth have “appealing” tail performance. The tail performance of the halfspace median is very appealing and also robust against underlying population distributions, while the tail performance of the sample mean is very sensitive to underlying population distributions. These findings provide new insights into the notions of the halfspace depth and breakdown point and identify the important role of tail behavior as a quantitative measure of robustness in the multivariate location setting.     

Asymptotic expansion of Bayes estimators for small diffusions

Summary Using the Malliavin calculus we derived asymptotic expansion of the distributions of the Bayes estimators for small diffusions. The second order efficiency of the Bayes estimator is proved.     

The standard error and bias of normal density estimators

The standard error of maximum likelihood estimators is derived for the following three cases: only m is unknown, only is unknown, both parameters of the normal distribution are unknown. Explicit analytical expressions are obtained for the bias of the maximum likelihood estimators in these cases.Translated from Statisticheskie Metody, pp. 147–155, 1980.     

Estimation of the tail index for lattice-valued sequences

If one applies the Hill, Pickands or Dekkers–Einmahl–de Haan estimators of the tail index of a distribution to data which are rounded off one often observes that these estimators oscillate strongly as a function of the number k of order statistics involved. We study this phenomenon in the case of a Pareto distribution. We provide formulas for the expected value and variance of the Hill estimator and give bounds on k when the central limit theorem is still applicable. We illustrate the theory by using simulated and real-life data.     

On the asymptotic bias of estimators under parameter drift

It is shown that, under a natural assumption, minimum discrepancy estimators in the analysis of moment structures are asymptotically unbiased.     

Asymptotic bias and variance for a general class of varying bandwidth density estimators

Summary We consider a general class of varying bandwidth estimators of a probability density function. The class includes the Abramson estimator, transformation kernel density estimator (TKDE), Jones transformation kernel density estimator (JTKDE), nearest neighbour type estimator (NN), Jones-Linton-Nielsen estimator (JLN), Taylor series approximations of TKDE (TTKDE) and Simpson's formula approximations of TKDE (STKDE). Each of these estimators needs a pilot estimator. Starting with an ordinary kernel estimator , it is possible to iterate and compute a sequence of estimates , using each estimate as a pilot estimator in the next step. The first main result is a formula for the bias order. If the bandwidths used in different steps have a common orderh=h(n), the bias of is of orderh ^2km ,k=1, ...,t. Hereh ^m is the bias order of the ideal estimator (defined by using the unknownf as pilot). The second main result is a recursive formula for the leading bias and stochastic terms in an asymptotic expansion of the density estimates. Ifm<, it is possible to make asymptotically equivalent to the ideal estimator.     

Consistent estimation of the tail index for dependent data

We consider the estimation of the tail-index for dependent random variables. We establish the consistency of the geometric-type estimator (Brito and Freitas, 2003) for stationary sequences satisfying general mixing conditions and derive a simplified condition, specially adapted for applications.     

The bias of lag window estimators of the fractional difference parameter

An approximation for the bias in lag window estimators of the degree of differencing in, fractionally integrated time series models is derived. The expression obtained is compared with the observed bias from simulations for various windows.     

Multivariate moment based extreme value index estimators

Modeling extreme events is of paramount importance in various areas of science—biostatistics, climatology, finance, geology, and telecommunications, to name a few. Most of these application areas involve multivariate data. Estimation of the extreme value index plays a crucial role in modeling rare events. There is an affine invariant multivariate generalization of the well known Hill estimator—the separating Hill estimator. However, the Hill estimator is only suitable for heavy tailed distributions. As in the case of the separating multivariate Hill estimator, we consider estimation of the extreme value index under the assumptions of multivariate ellipticity and independent identically distributed observations. We provide affine invariant multivariate generalizations of the moment estimator and the mixed moment estimator. These estimators are suitable for both light and heavy tailed distributions. Asymptotic properties of the new extreme value index estimators are derived under multivariate elliptical distribution with known location and scatter. The effect of replacing true location and scatter by estimates is examined in a thorough simulation study. We also consider two data examples: one financial application and one meteorological application.     

Performance of some boundary-seeking mode estimators on the dome bias model

Expert estimates can be systematically biased for various reasons. The dome perspective bias model provides one instance of this phenomenon. Given data with this suspected property, it is desirable to propose mode estimators which have the capability of producing consensus estimates on the boundary of the convex hull of the sample. Affine linear models are no doubt the simplest class of functions with that capability. This paper uses the maximum decisional efficiency (MDE) principle to estimate the parameters of an affine linear group value function. These estimators vary according to the sample aggregator chosen. Estimators are developed or approximated for the aggregator choices of (i) mean, (ii) minimum or Leontief, and (iii) variance. The respective performances of these estimators are assessed and compared on the dome perspective bias model using Monte Carlo simulation experiments. The estimator based on the mean performed uniformly well on a variety of simulated cases. However, those based on range and variance were not effective.     

Distributions and moments for estimators of Gini index in an exponential distribution

In this paper, we propose several estimators of Gini index of the two-parameter exponential distribution and obtain distributions and moments of the proposed estimators. The proposed estimators are shown to cosistency and will be compared in terms of the mean squared error (MSE) through Monte Carlo method.