Assessing the Potential Impact of the HIV-1 Epidemic on Orphanhood and the Demographic Structure of Populations in sub-Saharan Africa

Much of the debate on the demographic consequences of the HIV epidemic in sub-Saharan Africa has so far centred around the plausibility of population declines in areas where unprecendently high rates of population growth have recently been in evidence. In this article, the authors use a mathematical model, which combines epidemiological and demographic processes, to illustrate how, under a broad range of impacts on population growth, major changes in demographic features, such as the extent of orphanhood within populations, are likely to occur. At the same time, HIV epidemics are liable to cause significant shifts in the age and sex composition of affected populations, which may have important implications for the ways in which they are best able to cope with the increases in orphanhood, as well as those in infant, early childhood and adult mortality.     

How Does Centrality in Coopetition Networks Matter? An Empirical Investigation in the Mobile Telephone Industry

This research examines how a firm's position in a coopetitive network (formed through cooperation among firms within an industry) influences the extent of the firm's competitive aggressiveness and market performance. The authors collected data on the competitive and cooperative actions of firms in the mobile telephone industry from 2000 to 2006, using structured content analysis of news reports. The results show that the centrality of a firm in a coopetitive network contributes to the firm's competitive aggressiveness through increased volume and variety of competitive actions. Further, the more central a firm is in the network, the greater is its market performance. Firms that undertake more volume and variety of competitive actions improve their market performance. Overall, these results show that being in a central position in a coopetition network is quite advantageous for the firm.     

Minutes of Committee of Presidents of Statistical Societies

The general approach to generating random variates through transformations with multiple roots is discussed. Multinomial probabilities are determined for the selection of the different roots. An application of the general result yields a new and simple technique for the generation of variates from the inverse Gaussian distribution.     

Efficient Computing of Regression Diagnostics

Multiple regression diagnostic methods have recently been developed to help data analysts identify failures of data to adhere to the assumptions that customarily accompany regression models. However, the mathematical development of regression diagnostics has not generally led to efficient computing formulas. Conflicting terminology and the use of closely related but subtly different statistics has caused confusion. This article attempts to make regression diagnostics more readily available to those who compute regressions with packaged statistics programs. We review regression diagnostic methodology, highlighting ambiguities of terminology and relationships among similar methods. We present new formulas for efficient computing of regression diagnostics. Finally, we offer specific advice on obtaining regression diagnostics from existing statistics programs, with examples drawn from Minitab and SAS.     

Promoting Individual and Social Changes: A Hybrid Model of Social Work with Groups

Taking as a starting point two influential, yet different approaches in group work, that is, the self-directed group work and mutual aid models, this article examines a possible alternative for conducting social work with groups. Drawing from structuration theory, which makes a strong dialectical relation between agency and structure possible, this article highlights how our alternative model could lead to a greater integration of the micro- and macrodimensions in group-work practice. The Discussion section proposes three key principles for group work, namely, a belief in people’s strengths and capacities, a focus on critical thinking, and a concern for the development of a democratic culture in groups. These principles are conveyed through the group worker’s roles as consciousness raiser and process facilitator and provide a flexible and participatory process that can be used with a broad range of service-user groups. The article concludes with a discussion on the strengths and limitations of the model.     

On a measure of information gain for regression models in survival analysis

Papers dealing with measures of predictive power in survival analysis have seen their independence of censoring, or their estimates being unbiased under censoring, as the most important property. We argue that this property has been wrongly understood. Discussing the so-called measure of information gain, we point out that we cannot have unbiased estimates if all values, greater than a given time τ, are censored. This is due to the fact that censoring before τ has a different effect than censoring after τ. Such τ is often introduced by design of a study. Independence can only be achieved under the assumption of the model being valid after τ, which is impossible to verify. But if one is willing to make such an assumption, we suggest using multiple imputation to obtain a consistent estimate. We further show that censoring has different effects on the estimation of the measure for the Cox model than for parametric models, and we discuss them separately. We also give some warnings about the usage of the measure, especially when it comes to comparing essentially different models.     

Bayesian Accelerated Life Testing under Competing Weibull Causes of Failure

Consider a J-component series system which is put on Accelerated Life Test (ALT) involving K stress variables. First, a general formulation of ALT is provided for log-location-scale family of distributions. A general stress translation function of location parameter of the component log-lifetime distribution is proposed which can accommodate standard ones like Arrhenius, power-rule, log-linear model, etc., as special cases. Later, the component lives are assumed to be independent Weibull random variables with a common shape parameter. A full Bayesian methodology is then developed by letting only the scale parameters of the Weibull component lives depend on the stress variables through the general stress translation function. Priors on all the parameters, namely the stress coefficients and the Weibull shape parameter, are assumed to be log-concave and independent of each other. This assumption is to facilitate Gibbs sampling from the joint posterior. The samples thus generated from the joint posterior is then used to obtain the Bayesian point and interval estimates of the system reliability at usage condition.     

Markov Perfect Industry Dynamics With Many Firms

We propose an approximation method for analyzing Ericson and Pakes (1995)‐style dynamic models of imperfect competition. We define a new equilibrium concept that we call oblivious equilibrium, in which each firm is assumed to make decisions based only on its own state and knowledge of the long‐run average industry state, but where firms ignore current information about competitors' states. The great advantage of oblivious equilibria is that they are much easier to compute than are Markov perfect equilibria. Moreover, we show that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain “light‐tail” condition, then oblivious equilibria closely approximate Markov perfect equilibria. This theorem justifies using oblivious equilibria to analyze Markov perfect industry dynamics in Ericson and Pakes (1995)‐style models with many firms.     

Estimating and Testing a Structured Covariance Matrix for Three-Level Multivariate Data

This article considers an approach to estimating and testing a new Kronecker product covariance structure for three-level (multiple time points (p), multiple sites (u), and multiple response variables (q)) multivariate data. Testing of such covariance structure is potentially important for high dimensional multi-level multivariate data. The hypothesis testing procedure developed in this article can not only test the hypothesis for three-level multivariate data, but also can test many different hypotheses, such as blocked compound symmetry, for two-level multivariate data as special cases. The tests are implemented with two real data sets.