On smoothness of Tukey depth contours

The smoothness of Tukey depth contours is a regularity condition often encountered in asymptotic theory, among others. This condition ensures that the Tukey depth fully characterizes the underlying multivariate probability distribution. In this paper we demonstrate that this regularity condition is rarely satisfied. It is shown that even well-behaved probability distributions with symmetrical, smooth and (strictly) quasi-concave densities may have non-smooth Tukey depth contours, and that the smoothness behaviour of depth contours is fairly unpredictable.     

On the Tukey depth of an atomic measure

This paper gives a relation between the convex Tukey trimmed region (see [J.C. Massé, R. Theodorescu, Halfplane trimming for bivariate distributions, J. Multivariate Anal. 48(2) (1994) 188–202]) of an atomic measure and the support of the measure. It is shown that an atomic measure is concentrated on the extreme points of its Tukey trimmed region. A property concerning the extreme points which have 0 mass is given. As a corollary, we give a new method of proof of the Koshevoy characterization result.     

Recursive M-estimators of location

This paper deals with recursive M-estimators of a location parameter θ in stationary processes when scale is regarded as a nuisance parameter. For the nonrecursive M-estimators, the median absolute deviation is a useful estimator of scale. Two recursive variants of the median absolute deviation are proposed and the performance of the resulting recursive estimators is examined in a numerical study.     

The median estimate of the autoregressive location parameter

A median-based estimate of the location (i.e. intercept) parameter in an autoregressive time series is considered. Specifically, the asymptotic joint distribution of the location estimate and a location invariant estimate of the AR parameter vector is derived. Applications of this result to rank-based estimates are briefly discussed and illustrated with a numerical example.     

A new formula for the computation of multivariate factorized moments by using the joint cumulative distribution function

This work presents a closed formula to compute any muitivariate factorized expected value from the knowledge of the joint cumulative distribution function (cdf) of any random variable. Additionally, a new nonparametric estimator alternative to the sample average is presented for the univariate case.     

Stable estimation of location parameter -nonasymptotic approach

Let X₁:, X₂:, …, X_n be iidrv's with cdf F?, F?(x)=F (x-θ), R. Let T be an equivariant median-unbiased estimator of θ. Let πε(F)={G = (1 -ε) F+εH, H any cdf} and let M(G, T) be a median of T if X₁ has cdf G. The oscillation of the bias of T, defined as

Bε(T)=sup (M(G₁ T) :G₁,G₂:∈π_σ:(F)} ,is considered and the estimator with the smallest B$epsi;(T) is explicitly constructed     

Consistency and robustness of tests and estimators based on depth

In this paper it is shown that data depth does not only provide consistent and robust estimators but also consistent and robust tests. Thereby, consistency of a test means that the Type I (α$\alpha$) error and the Type II (β$\beta$) error converge to zero with growing sample size in the interior of the nullhypothesis and the alternative, respectively. Robustness is measured by the breakdown point which depends here on a so-called concentration parameter. The consistency and robustness properties are shown for cases where the parameter of maximum depth is a biased estimator and has to be corrected. This bias is a disadvantage for estimation but an advantage for testing. It causes that the corresponding simplicial depth is not a degenerated U-statistic so that tests can be derived easily. However, the straightforward tests have a very poor power although they are asymptotic α-level$\alpha \mathrm{-}\mathrm{level}$ tests. To improve the power, a new method is presented to modify these tests so that even consistency of the modified tests is achieved. Examples of two-dimensional copulas and the Weibull distribution show the applicability of the new method.     

Asymptotic relative efficiencies of r-estimators of location

A table of asymptotic relative efficiencies of a large variety of R-estimators is given and some of its uses discussed.The family of distributions used includes both those common in the robustness literature and those that correspondent to various rank tests for location.     

Monte carlo comparison of estimation methods for additive two-way tables

We considered the problem of estimating effects in the following linear model for data arranged in a two-way table: Response = Common effect + Row effect + Column effect + Residual. This work was occasioned by a project to analyse Federal Aviation Administration (FAA) data on daily temporal deviations from flight plans for commercial US flights, with rows and columns representing origin and destination airports, respectively. We conducted a large Monte Carlo study comparing the accuracy of three methods of estimation: classical least squares, median polish and least absolute deviations (LAD). The experiments included a wide spectrum of tables of different sizes and shapes, with different levels of non-linearity, noise variance, and percentages of empty cells and outliers. We based our comparison on the accuracy of the estimates and on computational speed. We identified factors that significantly affect accuracy and speed, and compared the methods based on their sensitivity to these factors. We concluded that there is no dominant method of estimation and identified conditions under which each method is most attractive.     

Outliers and Model Selection: Discussion of the Paper by Søren Johansen and Bent Nielsen

It is argued that model selection and robust estimation should be handled jointly. Impulse indicator saturation makes that possible, but leads to the situation where there are more variables than observations. This is illustrated by revisiting the analysis of Tobin's food data.     

Density estimation under the koziol-green model of censoring by penalized likelihood methods

We propose two density estimators of the survival distribution in the setting of the Koziol-Green random-censoring model. The estimators are obtained by maximum-penalized-likelihood methods, and we provide an algorithm for their numerical evaluation. We establish the strong consistency of the estimators in the Hellinger metric, the L_p-norms, p= 1,2, ∞, and a Sobolev norm, under mild conditions on the underlying survival density and the censoring distribution.     

Robust weighted Cramér-von Mises Estimators of location,with minimax variance in ϵ-contamination neighbourhoods

We construct weighted Cramér-von Mises location estimators which are asymptotically normally distributed throughout an ?e-contamination neighbourhood of a given, strongly unimodal distribution function, and which minimize the maximum asymptotic variance in such neighbourhoods. Applications to the estimation of a normal or logistic mean are given.