Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.     

Systemic risk statistics with scenario analysis

In this paper, we introduce two new classes of risk statistics, named convex and positively homogeneous systemic risk statistics, respectively. Structural decomposition results and representation results for them are provided. These new risk statistics can be considered as a kind of systemic risk extension of risk statistics introduced by Kou, Peng, and Heyde, and also empirical versions of system risk measures introduced by Cehn, Iyengar, and Mollemi and Kromer, Overbeck, and Zich. Finally, some examples are also given.     

空间的凸性和远达点映射的连续性

本文给出空间严格凸、CLUB和LUR的新特征刻划,后用这些特征对中一定理作了改进.     

Testing uniformity for the case of a planar unknown support

A new test is proposed for the hypothesis of uniformity on bi‐dimensional supports. The procedure is an adaptation of the “distance to boundary test” (DB test) proposed in Berrendero, Cuevas, & Vázquez‐Grande (2006). This new version of the DB test, called DBU test, allows us (as a novel, interesting feature) to deal with the case where the support S of the underlying distribution is unknown. This means that S is not specified in the null hypothesis so that, in fact, we test the null hypothesis that the underlying distribution is uniform on some support S belonging to a given class ${\cal C}$ . We pay special attention to the case that ${\cal C}$ is either the class of compact convex supports or the (broader) class of compact λ‐convex supports (also called r‐convex or α‐convex in the literature). The basic idea is to apply the DB test in a sort of plug‐in version, where the support S is approximated by using methods of set estimation. The DBU method is analysed from both the theoretical and practical point of view, via some asymptotic results and a simulation study, respectively. The Canadian Journal of Statistics 40: 378–395; 2012 © 2012 Statistical Society of Canada     

A robust support vector machine for labeling errors

Support vector machine (SVM) is sparse in that its classifier is expressed as a linear combination of only a few support vectors (SVs). Whenever an outlier is included as an SV in the classifier, the outlier may have serious impact on the estimated decision function. In this article, we propose a robust loss function that is convex. Our learning algorithm is more robust to outliers than SVM. Also the convexity of our loss function permits an efficient solution path algorithm. Through simulated and real data analysis, we illustrate that our method can be useful in the presence of labeling errors.     

局部凸空间的一致凸性与一致光滑性

本文对局部凸空间引进一致光滑、拓扑一致光滑的概念,讨论了局部凸空间的一致凸性与一致光滑性之间的某种对偶关系,证明了:(1)若局部凸空间(E′,P~*)是一致凸的,则局部凸空间(E,P)是一致光滑的;(2)若局部凸空间(E′,P~*)是一致光滑的,则局部凸空间(E,P)是一致凸的;(3)亚完备的拓扑一致光滑的局部凸空间是半自反的.     

Gini correlation as a measure of monotonicity and two of its usages

In this paper it is shown that the Gini correlation (suggested recently in this Journal) is a special case of a general family of measures of monotonicity. Two new usages of the GINI coefficient will be discussed: 1. for measuring convexity of time series, and 2. for measuring overlapping between groups. The asymptotic variances of the above two measures will be derived.     

Model averaging based on rank

In this paper, we investigate model selection and model averaging based on rank regression. Under mild conditions, we propose a focused information criterion and a frequentist model averaging estimator for the focused parameters in rank regression model. Compared to the least squares method, the new method is not only highly efficient but also robust. The large sample properties of the proposed procedure are established. The finite sample properties are investigated via extensive Monte Claro simulation study. Finally, we use the Boston Housing Price Dataset to illustrate the use of the proposed rank methods.     

RANDOM PATHS ORIGINATING WITHIN A CONVEX REGION AND TERMINATING ON ITS SURFACE

A ray is defined as a straight line originating within an arbitrary convex body K in n -dimensional Euclidean space and terminating on the surface of K. The distributions of ray length are derived for three types of randomness. The results are expressed as functions of the overlap volume and overlap surface content of K with its translated self.     

Shape constrained kernel density estimation

In this paper, a method for estimating monotone, convex and log-concave densities is proposed. The estimation procedure consists of an unconstrained kernel estimator which is modified in a second step with respect to the desired shape constraint by using monotone rearrangements. It is shown that the resulting estimate is a density itself and shares the asymptotic properties of the unconstrained estimate. A short simulation study shows the finite sample behavior.