Going to Pieces: Paul Monette and the Fragments of the Tribe

Author of the highly acclaimed Borrowed Time: An AIDS Memoir (1988) and winner of the 1992 National Book Award for his autobiography Becoming a Man: Half a Life Story, Paul Monette died of complications from AIDS in Los Angeles on February 12, 1995. His most recent work, Last Watch of the Night: Essays Too Personal and Otherwise (1994), is his third volume in a trilogy of autobiographical writing on growing up gay, coming-out, and living with AIDS. A graduate of Phillips Academy and Yale University, Monette received a number of awards for his writing and his work as an outspoken voice in the battle against AIDS, including the National Book Award, three Lambda Literary awards, the PEN Center West Freedom to Write Award, and three honorary doctorate degrees, from Wesleyan University, the City University of New York, and the State University of New York at Oswego. A memorial service was held for Monette on Sunday, February 19, 1995, at the Directors Guild of America on Sunset Boulevard in Los Angeles. Paul Monette was 49.     

Three faces of international inequality

Recent studies of international inequality have focused mostly on the trend in international income inequality. This article extends the analysis of international inequality to also include inequalities in education and health. Analyses of time-series data for more than 100 countries show that international income inequality declined from 1980 to 2003 as several large, poor Asian countries outpaced many Western countries in national income growth. By contrast, international health inequality followed a U-shaped trend, falling in the 1980s before rising in the 1990s. The turnaround in health inequality coincides with a trend of declining life expectancy in sub-Saharan Africa. International educational inequality experienced the sharpest recent decline, spurred by the global expansion of formal schooling. These findings confirm that there is more to international inequality than income inequality alone and suggest that patterns of inequality in the current era of globalization are likely more complex than many leading theories suggest.     

The Analysis of Crop Variety Evaluation Data in Australia

The major aim of crop variety evaluation is to predict the future performance of varieties. This paper presents the routine statistical analysis of data from late-stage testing of crop varieties in Australia. It uses a two-stage approach for analysis. The data from individual trials from the current year are analysed using spatial techniques. The resultant table of variety-by-trial means is combined with tables from previous years to form the data for an overall mixed model analysis. Weights allow for the data being estimates with varying accuracy. In view of the predictive aim of the analysis, variety effects and interactions are regarded as random effects. Appropriate inferential tools have been developed to assist with interpretation of the results. Analyses must be conducted in a timely manner so that variety predictions can be published and disseminated to growers immediately after harvest each year. Factors which facilitate this include easy access to historic data and the use of specialist mixed model software.     

Conditional Generalized Liouville Distributions on the Simplex

Liouville and generalized Liouville distributions on the simplex have been proposed for modeling compositional data and have been shown to be free from the extreme independence structure that characterizes the Dirichlet class. In this article, generalized Liouville distributions are shown to be rich enough to distinguish some lesser modes of independence as well. Unfortunately, it is noted that the applicability of the Liouville family will be limited, owing to the lack of invariance with respect to the chosen fill-up value. As an alternative, a new family of simplex distributions is proposed, one that admits invariance with respect to choice of fill-up value, as well as the ability to differentiate among many forms of independence.     

Geometric characterization of planar regression

The geometric characterization of linear regression in terms of the ‘concentration ellipse’ by Galton [Galton, F., 1886, Family likeness in stature (with Appendix by Dickson, J.D.H.). Proceedings of the Royal Society of London, 40, 42–73.] and Pearson [Pearson, K., 1901, On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559–572.] was extended to the case of unequal variances of the presumably uncorrelated errors in the experimental data [McCartin, B.J., 2003, A geometric characterization of linear regression. Statistics, 37(2), 101–117.]. In this paper, this geometric characterization is further extended to planar (and also linear) regression in three dimensions where a beautiful interpretation in terms of the concentration ellipsoid is developed.     

An Empirical Study of Statistical Properties of Variance Partition Coefficients for Multi-Level Logistic Regression Models

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003 Goldstein , H. ( 2003 ). Multilevel Statistical Models. 3rd ed . London : Edward Arnold . [Google Scholar]) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.     

A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models

In this article, we present a compressive sensing based framework for generalized linear model regression that employs a two-component noise model and convex optimization techniques to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions. We then extend our model to include model order reduction capabilities that can uncover inherent sparsity in regression coefficients and achieve simple, superior fits. Second, we use the mixed ?₂/?₁ norm to develop another model that can efficiently uncover block-sparsity in regression coefficients. By performing model order reduction over all independent variables and basis functions, our algorithms successfully deemphasize the effect of independent variables that become uncorrelated with dependent variables. This desirable property has various applications in real-time anomaly detection, such as faulty sensor detection and sensor jamming in wireless sensor networks. After developing our framework and inheriting a stable recovery theorem from compressive sensing theory, we present two simulation studies on sparse or block-sparse problems that demonstrate the superior performance of our algorithms with respect to (1) classic outlier-invariant regression techniques like least absolute value and iteratively reweighted least-squares and (2) classic sparse-regularized regression techniques like LASSO.     

A Two-Phase Exact Algorithm for MAX-SAT and Weighted MAX-SAT Problems

We describe a two-phase algorithm for MAX-SAT and weighted MAX-SAT problems. In the first phase, we use the GSAT heuristic to find a good solution to the problem. In the second phase, we use an enumeration procedure based on the Davis-Putnam-Loveland algorithm, to find a provably optimal solution. The first heuristic stage improves the performance of the algorithm by obtaining an upper bound on the minimum number of unsatisfied clauses that can be used in pruning branches of the search tree.We compare our algorithm with an integer programming branch-and-cut algorithm. Our implementation of the two-phase algorithm is faster than the integer programming approach on many problems. However, the integer programming approach is more effective than the two-phase algorithm on some classes of problems, including MAX-2-SAT problems.     

China, Globalisation and Health Biotechnology Innovation: Venture Capital and the Adaptive State

This paper explores the changing approach of the Chinese state to health biotechnology innovation and, in particular, the position of venture capital within that process. How has the Chinese state dealt with the opportunities and threats posed by globalisation in this field, what status has it attached to the role of venture capital in innovation and what are the implications of these actions for the role of the state? In addressing these questions, the paper begins by examining the global context of the venture capital and innovation relationship, the varied nature of VC organisation, the business models that inform the VC approach to health biotechnology innovation and the implications of these factors for the role of the state. Secondly, it explores the nature and impact of state intervention in China on the VC contribution to innovation, the particular character of the Chinese VC industry and its economic environment and the capacity of the VC sector to contribute to China’s position as a global player in health technology innovation. The paper concludes by reviewing the insights this case study provides into the problematic character of state adaptation in China to the challenges of globalisation.