Table of contents, International Statistical Review, vol. 74, no. 1 (2006)

Critique of p-Values; Authors: Bill Thompson

Percentage Points of the Multivariate t Distribution; Authors: Saralees Nadarajah, Samuel Kotz

Smoothing Observational Data: A Philosophy and Implementation for the Health Sciences; Authors: Sander Greenland

On Testing for the Nullity of Some Skewness Coefficients; Authors: Joseph Ngatchou-Wandji

Stratification of Skewed Populations: A review; Authors: Jane M. Horgan

Regression Analysis with a Stochastic Design Variable; Authors: Hakan S. Sazak,, Moti L. Tiku, M. Qamarul Islam

Estimating Urbanization Levels in Chinese Provinces in 1982-2000; Authors: Jianfa Shen

Skewed Normal Variance-Mean Models for Asset Pricing and the Method of Moments; Authors: Annelies Tjetjep, Eugene Seneta

Editorial board, International Statistical Review, vol. 74, no. 1 (2006)

E. Seneta (School Math. & Statist., Univ. Sydney, Australia), Joint Editor

J. van Sinderen (Statistics Netherlands, Voorburg), Joint Editor

A.M. Manninen (City of Helsinki, Urban Facts, Finland)., Retiring Editor

Ann Daniels (ISI, Voorburg, The Netherlands)., Executive Editor

R. Acx (National Bank of Belgium, Brussels), Associate Editor

T. Alanko (Statistics Finland), Associate Editor

R. Gill (Univ. Utrecht, The Netherlands),, Associate Editor

P. Guttorp (Univ. Washington, Seattle, USA), Associate Editor

W. Lutz (Internat. Inst. Appl. Systems Analysis, Laxenburg, Austria),, Associate Editor

N. Singpurwalla (George Washington Univ.,Washington, USA),, Associate Editor

I. Stamhuis (Free Univ., Amsterdam, The Netherlands),, Associate Editor

P. Verdier (INSEE, Paris, France),, Associate Editor

N. Weber (School Math. & Statist., Univ. Sydney, Australia),, Associate Editor

E. Wegman (George Mason Univ., Fairfax, USA),, Associate Editor

C. Wild (Univ. Auckland, New Zealand)., Associate Editor

T.E. Dalenius,, Former Editor

L.A.M. Vessereau,, Former Editor

W.R. Buckland,, Former Editor

O.E. Barndorff-Nielsen,, Former Editor

D.R. Brillinger,, Former Editor

B.W. Silverman,, Former Editor

D.J. Trewin,, Former Editor

V.N. Nair,, Former Editor

W.F.M. de Vries,, Former Editor

E. Arjas., Former Editor

This paper generalizes the notion of p-value to obtain a system for assessing evidence in favor of an hypothesis. It is not quite a quantification in that evidence is a pair of numbers (the p-value and the p-value with null and alternative interchanged) with evidence for the alternative being claimed when the first number is small and the second is at least moderately large. Traditional significance tests present p-values as a measure of evidence {against} a theory. This usage is rarely called for since scientists usually wish to accept theories (for the time being) not just not reject them; they are more interested in evidence {for} a theory. P-values are not just good or bad for this purpose; their efficacy depends on specifics. We find that a single p-value does not measure evidence for a simple hypothesis relative to a simple alternative, but consideration of both p-values leads to a satisfactory theory. This consideration does not, in general, extend to composite hypotheses since there, best evidence calls for optimization of a bivariate objective function. But in some cases, notably one sided tests for the exponential family, the optimization can be solved, and a single p-value does provide an appealing measure of best evidence for a hypothesis. One possible extension of this theory is proposed and illustrated with a practical safety analysis problem involving the difference of two random variables.

The known methods for computing percentage points of multivariate t distributions are reviewed. We believe that this review will serve as an important reference and encourage further research activities in the area.

Standard statistical methods (such as regression analysis) presume the data are generated by an identifiable random process, and attempt to model that process in a parsimonious fashion. In contrast, observational data in the health sciences are generated by complex, nonidentified, and largely nonrandom mechanisms, and are analyzed to form inferences on latent structures. Despite this gap between the methods and reality, most observational data analysis comprises application of standard methods, followed by narrative discussion of the problems of entailed by doing so. Alternative approaches employ latent-structure models that include components for nonidentified mechanisms. Standard methods can still be useful, however, provided their modeling philosophy is modified to encourage preservation of structure, rather than achieving parsimonious description. With this modification they can be viewed as smoothing or filtering methods for separating noise from signal before the task of latent-structure modeling begins. I here give a detailed justification of this view, and a hierarchical-modeling implementation that can be carried out with popular software. Concepts are illustrated in the smoothing of a contingency table from an analysis of magnetic fields and childhood leukemia.

Three tests for the skewness of an unknown distribution are derived for iid data. They are based on suitable normalization of estimators of some usual skewness coefficients. Their asymptotic null distributions are derived. The tests are next shown to be consistent and their power under some sequences of local alternatives is investigated. Their finite sample properties are also studied through a simulation experiment, and compared to those of the \sqrt{b₁}-test.

When Dalenius provided a set of equations for the determination of stratum boundaries of a single auxiliary variable, that minimise the variance of the Horvitz-Thompson estimator of the mean or total under Neyman allocation for a fixed sample size, he pointed out that, though mathematically correct, those equations are troublesome to solve. Since then there has been a proliferation of approximations of an iterative nature, or otherwise cumbersome, tendered for this problem; many of these approximations assume a uniform distribution within strata, and, in the case of skewed populations, that all strata have the same relative variation. What seems to have been missed is that the combination of these two assumptions offers a much simpler and equally effective method of subdivision for skewed populations; take the stratum boundaries in geometric progression.

In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.

No consistent and reliable annual data series on the urbanization level for provincial regions of China is available. Making use of urban population data from the 1982 and 2000 population censuses, this paper estimates an annual data series of the urbanization level for provincial regions using an estimation approach developed on the basis of a conceptual model of dual-track urbanization. Based on such estimated new urban data of provincial regions, the major trends of urbanization in Chinese provinces and the relationship between urbanization and economic development are analysed for the period\linebreak 1982-2000.

Financial returns (log-increments) data, Y_t, t=1,2,..., are treated as a stationary process, with the common distribution at each time point being not necessarily symmetric.

We consider as possible models for the common distribution four instances of the General Normal Variance-Mean Model (GNVM), which is described by $Y|V\sim N(a(b+V),c^{2}V+d^2)$ where V is a non-negative random variable and a, b, c and d are constants. When V is Gamma distributed and d=0, Y has the skewed Variance-Gamma distribution (VG). When V follows a Half Normal distribution and c=0, Y has the well-known Skew Normal (SN) distribution. We also consider two cases where V is Exponentially distributed. Bounds for skewness and kurtosis in each case are found in terms of the moments of the V. These are useful in determining whether the Method of Moments for a given model is feasible. The problem of overdetermination of parameters via estimating equations is examined. 5 data sets of actual returns data, chosen because of their earlier occurrence in the literature, are analysed using each of the 4 models.