What is the effective sample size of a spatial point process?

Point process models are a natural approach for modelling data that arise as point events. In the case of Poisson counts, these may be fitted easily as a weighted Poisson regression. Point processes lack the notion of sample size. This is problematic for model selection, because various classical criteria such as the Bayesian information criterion (BIC) are a function of the sample size, n, and are derived in an asymptotic framework where n tends to infinity. In this paper, we develop an asymptotic result for Poisson point process models in which the observed number of point events, m, plays the role that sample size does in the classical regression context. Following from this result, we derive a version of BIC for point process models, and when fitted via penalised likelihood, conditions for the LASSO penalty that ensure consistency in estimation and the oracle property. We discuss challenges extending these results to the wider class of Gibbs models, of which the Poisson point process model is a special case.     

Fertility decline and gender bias in

Although it is widely acknowledged that the preference for sons is a barrier to a decline in fertility, considerable disagreement exists as to what actually happens to this preference when fertility declines in a region of low female autonomy. By analyzing the data from the National Family Health Survey (NFHS), we present evidence from northern India to show that the preference for sons is reduced when the ideal family size becomes small, even though it does not completely disappear. This finding appears to contradict trends in the juvenile sex ratio and the incidence of female feticide that suggest the intensification of gender bias. We argue that the anomaly is the result of a diffusion of prenatal sex-diagnostic techniques in regions where there is a large unmet demand for such methods. Using the NFHS data, we estimate that in northern India, girls currently constitute about 60% of the unwanted births and that the elimination of unwanted fertility has the potential to raise the sex ratio at birth to 130 boys per 100 girls.     

The institutional design of the Australian Welfare State

This paper seeks to compare and contrast the character of social policy in Australia with that in other OECD countries by focusing on the distinctive design features of the institutions constituting the wider context of Australian social policy provision. The three substantive sections of the paper successively summarize the distinguishing features of the institutional design of Welfare State provision in continental western Europe, Scandinavia and Australia; outline the wider institutional context of social policy in Australia; and examine the extent to which the distinctiveness of the institutional design of the Australian Welfare State has led to different patterns of policy outcomes from those in other nations.     

On gambling research,social science and the consequences of commercial gambling

Social, political, economic, geographic and cultural processes related to the significant growth of the gambling industries have, in recent years, been the subject of a growing body of research. This body of research has highlighted relationships between social class and gambling expenditure, as well as the design, marketing and location of gambling products and businesses. It has also demonstrated the regressive nature of much gambling revenue, illuminating the influence that large gambling businesses have had on government policy and on researchers, including research priorities, agendas and outcomes. Recently, critics have contended that although such scholarship has produced important insights about the operations and effects of gambling businesses, it is ideologically motivated and lacks scientific rigour. This response explains some basic theoretical and disciplinary concepts that such critique misunderstands, and argues for the value of social, political, economic, geographic and cultural perspectives to the broader, interdisciplinary field of gambling research.     

Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway‐Maxwell Poisson (COM‐Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM‐Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM‐Poisson GLM, and (2) estimate the prediction accuracy of the COM‐Poisson GLM using simulated data sets. The results of the study indicate that the COM‐Poisson GLM is flexible enough to model under‐, equi‐, and overdispersed data sets with different sample mean values. The results also show that the COM‐Poisson GLM yields accurate parameter estimates. The COM‐Poisson GLM provides a promising and flexible approach for performing count data regression.