Modeling heavy-tailed,skewed and peaked uncertainty phenomena with bounded support

A prevalence of heavy-tailed, peaked and skewed uncertainty phenomena have been cited in literature dealing with economic, physics, and engineering data. This fact has invigorated the search for continuous distributions of this nature. In this paper we shall generalize the two-sided framework presented in Kotz and van Dorp (Beyond beta: other continuous families of distributions with bounded support and applications. World Scientific Press, Singapore, 2004) for the construction of families of distributions with bounded support via a mixture technique utilizing two generating densities instead of one. The family of Elevated Two-Sided Power (ETSP) distributions is studied as an instance of this generalized framework. Through a moment ratio diagram comparison, we demonstrate that the ETSP family allows for a remarkable flexibility when modeling heavy-tailed and peaked, but skewed, uncertainty phenomena. We shall demonstrate its applicability via an illustrative example utilizing 2008 US income data.     

Epsilon Birnbaum–Saunders distribution family: properties and inference

In this paper we introduce a new extension for the Birnbaum–Saunder distribution based on the family of the epsilon-skew-symmetric distributions studied in Arellano-Valle et al. (J Stat Plan Inference 128(2):427–443, 2005). The extension allows generating Birnbaun–Saunders type distributions able to deal with extreme or outlying observations (Dupuis and Mills, IEEE Trans Reliab 47:88–95, 1998). Basic properties such as moments and Fisher information matrix are also studied. Results of a real data application are reported illustrating good fitting properties of the proposed model.     

Simulation study of confidence regions for canonical variate analysis

Canonical variate analysis often involves the construction of confidence regions round points representing group means in a 2-dimensional plot. Traditionally circles have always been constructed, but some authors have recently advocated ellipses as being more appropriate. This paper describes a Monte Carlo study investigating the effect of a range of factors on the inclusion rates of true population means within both types of region for normal data. The traditional circles do not perform too badly within a restricted range, but they are nearly always under-included. The ellipses usually have higher inclusion rates, and so are often closer to the nominal rate, but are sometimes over-included.     

AN EXACT CONDITIONAL TEST FOR COVARIANCE SELECTION MODELS

An exact conditional test is developed for testing the absence of an edge in a graphical covariance selection model and is shown to be equivalent to a test based on the partial correlation coefficient. An example is given.     

Adjustment of provisional mortality series: the dynamic linear model with structured measurement errors

The authors "consider the problem of adjusting provisional time series using a bivariate structural model with correlated measurement errors. Maximum likelihood estimators and a minimum mean squared error adjustment procedure are derived for a provisional and final series containing common trend and seasonal components. The model also includes measurement errors common to both series and errors that are specific to the provisional series. [The authors] illustrate the technique by using provisional data to forecast ischemic heart disease mortality."     

The Agree Consultant:more agreeable software?

This paper first describes a program AGREE calculating many variants of coefficients for interobserver agreement. A pilot program MOCK was written, aimed at helping unitiated users of AGREE to select the most appropriate coeflcient, given the data type and the research goal. It is a mock-up version of the data entrance and analysis sections of AGREE, to which are added some menus and a knowledge-based CONSULTANT system questioning the user. Results of a small experiment with four variants of the CONSULTANT are presented. This leads to a discussion of desirable features for this kind of help programs and preprocessors for specialized statistical software.     

Reclassifying causes of death to study the epidemiological transition in The Netherlands, 1875–1992

This article describes a method for reclassifying causes of death in the Netherlands for the period 1875–1992. Two criteria should be met to obtain a useful classification to study the epidemiological transition. First, the categories should be nosologically continuous over the period under study and second, there should be enough detail in causes which are important in the context of the epidemiological transition viz. communicable diseases, non-communicable diseases and external causes of death. A method developed by Vallin and Meslé (1988), which involves ‘dual correspondence tables’ and ‘fundamental associations’, was used to create nosologically continuous categories. These categories were tested for statistical continuity during the transition years of one ICD-revision to the next, using ordinary least squares regression analysis. The reclassification procedure resulted in a nested classification consisting of three levels of refinement of causes of death: 27 causes, 1875–1992; 65 causes, 1901–1992; and 92 causes, 1931–1992. On the basis of this classification, 43% of all deaths in 1875–79 and 98% of all deaths in 1992 could be allocated to either communicable diseases, non-communicable diseases or external causes.     

On simultaneous closeness probabilities of order statistics from odd sample sizes to the population median

In this note, we present alternative derivations for the probability that an individual order statistic is closest to the target parameter among all order statistics from a complete random sample. This approach is simpler than the geometric arguments used earlier. We also provide a simple direct proof for the symmetry property of the simultaneous closeness probabilities among order statistics for the estimation of percentiles from a symmetric family. Finally, we offer an alternative simpler proof for the result that sample medians from larger odd sample sizes are Pitman closer to the population median than sample medians from smaller odd sample sizes.     

Moments of Inertia and Graph Separators

Graphs that arise from the finite element or finite difference methods often include geometric information such as the coordinates of the nodes of the graph. The geometric separator algorithm of Miller, Teng, Thurston, and Vavasis uses some of the available geometric information to find small node separators of graphs. The algorithm utilizes a random sampling technique based on the uniform distribution to find a good separator. We show that sampling from an elliptic distribution based on the inertia matrix of the graph can significantly improve the quality of the separator. More generally, given a cost function f on the unit d-sphere Ud, we can define an elliptic distribution based on the second moments of f. The expectation of f with respect to the elliptic distribution is less than or equal to the expectation with respect to the uniform distribution, with equality only in degenerate cases. We also demonstrate experimentally that the benefit gained by the use of the additional geometric information is significant. Some previous algorithms have used the moments of inertia heuristically, and suffer from extremely poor worst case performance. This is the first result, to our knowledge, that incorporates the moments of inertia into a provably good strategy.