Table of contents, International Statistical Review, vol. 72, no. 2 (2004)

Conditional Probabilistic Population Forecasting; Authors: Warren C. Sanderson, Sergei Scherbov, Brian C. O'Neill, Wolfgang Lutz

Conditional Probabilistic Population Projections: An Application to Climate Change; Authors: Brian C. O'Neill

Random Scenario Forecasts Versus Stochastic Forecasts; Authors: Shripad Tuljapurkar, Ronald D. Lee, Qi Li

Developing Official Stochastic Population Forecasts at the US Census Bureau; Authors: John F. Long, Frederick W. Hollmann

A Comparative Simulation Study of Wavelet Shrinkage Estimators for Poisson Counts; Authors: Panagiotis Besbeas, Italia De Feis, Theofanis Sapatinas

Corbelled Domes in Two and Three Dimensions: The Treasury of Atreus; Authors: Nicole A. Lazar, Joseph B. Kadane, Fang Chen, William G. Cavanagh, Clifford D. Litton

Simple Correspondence Analysis: A Bibliographic Review; Authors: Eric J. Beh

Editorial board, International Statistical Review, vol. 72, no. 2 (2004)

Joshua R. Goldstein, Guest editor

Wolfgang Lutz, Guest editor

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

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

Mrs. J.M. Daniels (ISI, Voorburg, The Netherlands)., Executive Editor

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

T. Alanko (Statistics Finland), Associate Editor

D. Balding (Imperial Coll. Fac.Med., London, UK),, Associate Editor

J. Cervera (Instituto Valenciano de Estad´ýstica, Valencia, Spain),, 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

H. Maier (BerlinSchool of Economics, Germany),, Associate Editor

S. Richardson (Imperial Coll. School Med., London, UK),, 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. and 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

Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because it allows them to answer 'what if' type questions properly when outcomes are uncertain. The second is a new category that we call 'future jump-off date forecasts'. Future jump-off date forecasts are valuable because they show policy-makers the likelihood that crucial features of today's forecasts will also be present in forecasts made in the future.

Future changes in population size, composition, and spatial distribution are key factors in the analysis of climate change, and their future evolution is highly uncertain. In climate change analyses, population uncertainty has traditionally been accounted for by using alternative scenarios spanning a range of outcomes. This paper illustrates how conditional probabilistic projections offer a means of combining probabilistic approaches with the scenario-based approach typically employed in the development of greenhouse gas emissions projections. The illustration combines a set of emissions scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) with existing probabilistic population projections from IIASA. Results demonstrate that conditional probabilistic projections have the potential to account more fully for uncertainty in emissions within conditional storylines about future development patterns, to provide a context for judging the consistency of individual scenarios with a given storyline, and to provide insight into relative likelihoods across storylines, at least from a demographic perspective. They may also serve as a step toward more comprehensive quantification of uncertainty in emissions projections.

Probabilistic population forecasts are useful because they describe uncertainty in a quantitatively useful way. One approach (that we call LT) uses historical data to estimate stochastic models (e.g., a time series model) of vital rates, and then makes forecasts. Another (we call it RS) began as a kind of randomized scenario: we consider its simplest variant, in which expert opinion is used to make probability distributions for terminal vital rates, and smooth trajectories are followed over time. We use analysis and examples to show several key differences between these methods: serial correlations in the forecast are much smaller in LT; the variance in LT models of vital rates (especially fertility) is much higher than in RS models that are based on official expert scenarios; trajectories in LT are much more irregular than in RS; probability intervals in LT tend to widen faster over forecast time. Newer versions of RS have been developed that reduce or eliminate some of these differences.

The U.S. Census Bureau is approaching a critical decision regarding a major facet of its methodology for forecasting the United States population. In the past, it has relied on alternative scenarios, low, medium, and high, to reflect varying interpretations of current trends in fertility, mortality, and international migration to forecast population. This approach has limitations that have been noted in the recent literature on population forecasting. Census Bureau researchers are embarking on an attempt to incorporate probabilistic reasoning to forecast prediction intervals around point forecasts to future dates. The current literature offers a choice of approaches to this problem. We are opting to employ formal time series modeling of parameters of fertility, mortality, and international migration, with stochastic renewal processes. The endeavor is complicated by the administrative necessity to produce a large amount of racial and Hispanic origin detail in the population, as well as the ubiquitous cross-categories of age and sex. As official population forecasts must meet user demand, we are faced with the added challenge of presenting and supporting the resulting product in a way that is comprehensible to users, many of whom have little or no comprehension of the technical forecasting literature, and are accustomed to simple, deterministic answers. We may well find a need to modify our strategy, depending on the realities that may emerge from the limitations of data, the administrative requirements of the product, and the diverse needs of our user community.

Using computer simulations, the finite sample performance of a number of classical and Bayesian wavelet shrinkage estimators for Poisson counts is examined. For the purpose of comparison, a variety of intensity functions, background intensity levels, sample sizes, primary resolution levels, wavelet filters and performance criteria are employed. A demonstration is given of the use of some of the estimators to analyse a data set arising in high-energy astrophysics. Following the philosophy of reproducible research, the Matlab programs and real-life data example used in this study are made freely available.

Before the development of the true dome, many ancient cultures used the technique of corbelling to roof spaces. Recently, a series of related statistical models have been proposed in the literature for explaining how corbelled domes might have been constructed. The most sophisticated of these models is based on a piecewise linear structure, with an unknown number of changepoints, to guide the building process. This model is analyzed by the reversible jump Markov Chain Monte Carlo (MCMC) technique. All models considered to date have been two-dimensional, that is, they have taken a single cross section through the dome; even when more extensive data, in the form of measurements on multiple slices through the dome, have been available, these have been averaged together for the purposes of analysis. In this paper, we extend the two-dimensional analysis to a three-dimensional analysis, that takes full advantage of the data collected by the archaeologists and of the rotational symmetries inherent in the structure. We also explore ways of graphically presenting the results from a complex, reversible jump MCMC implementation, in order to check convergence, good mixing, and appropriate exploration of the (high dimensional and varying dimension) parameter space. The model and the graphical techniques are demonstrated on the Treasury of Atreus in Mycenae, Greece, one of the finest extant examples of the corbelling method.

Over the past few decades correspondence analysis has gained an international reputation as a powerful statistical tool for the graphical analysis of contingency tables. This popularity stems from its development and application in many European countries, especially France, and its use has spread to English speaking nations such as the United States and the United Kingdom. Its growing popularity amongst statistical practitioners, and more recently those disciplines where the role of statistics is less dominant, demonstrates the importance of the continuing research and development of the methodology. The aim of this paper is to highlight the theoretical, practical and computational issues of simple correspondence analysis and discuss its relationship with recent advances that can be used to graphically display the association in two-way categorical data.