Optimal sampling for repeated binary measurements

The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.     

The Contribution of NPOs to Family Policy Formulation in EU Member and Applicant States

This paper examines the role of nonprofit organizations (NPOs) in the process of family policy formulation in eight member states and three applicant states of the European Union. Drawing upon interviews with representatives from the nonprofit sector, the author argues that the experiences of NPOs in family policy-making vary considerably between countries because of the way state–nonprofit relations operate. Social origins theory illustrates why these variations exist by pointing to broad societal influences on the nonprofit sector, but new empirical evidence presented here suggests that, in a particular policy field, other factors, such as the significance of the field on policy agendas, the strength of one particular organization in a specific cultural context, party ideology, and financial viability of the NPO, shape the role of organizations in the formulation of policies     

Couple Attitudes and Agreement Regarding Pregnancy Wantedness in the Philippines

We compare husbands' and wives' views of the wantedness of their most recent pregnancy in the Philippines, a country where relatively high levels of unintended fertility persist. This research is an extension of earlier work that looked separately at individual men's and women's responses. We analyze survey data for 369 couples and find that those most at risk of experiencing a pregnancy that neither spouse wanted tended to be higher parity couples, those who were more fatalistic, those who were practicing Catholics who attended religious services frequently, and those among whom the husband was the sole breadwinner. Higher parity women and older women were also more apt to experience a pregnancy that was wanted by only 1 spouse. Women who had difficulty discussing sexual matters with their husbands were more likely than other women to have a pregnancy that their husbands wanted but they did not.     

International Tax Competition: A Case for International Cooperation in Globalization

The liberalization of international financial flows and foreign direct investment has induced countries to use diverse measures to attract inflow of foreign capital and foreign direct investment, which is expected to have a positive effect on the growth of GDP and thus a positive effect on social welfare. Tax exemption, reduction of tax rate, tax holiday, or diverse subsidies are some of the most important measures used. In this paper we study international tax cooperation, i.e., countries change and especially reduce tax rate for corporate income or for asset revenues to attract inflow of foreign direct investment. Both theoretical and empirical studies have shown the sensibility of foreign direct investment decision with respect to tax rate differences between home countries and host countries. In general, more inflow of foreign direct investment can be expected if the tax rate of the home country is lower than that of foreign countries. This is the main reason for international tax cooperation. In this paper we propose a simple model to prove the sub-optimal Nash non cooperative solution in a two-country tax-competition game. The model shows that international tax cooperation can improve welfare of the participating countries. How to reach a cooperative solution for an international tax competition game (ITCG) is therefore an important issue for further discussions and studies. International institutions can play a crucial role to reach international tax cooperation or international tax harmonization.     

Predictive Bayesian Microbial Dose-Response Assessment Based on Suggested Self-Organization in Primary Illness Response: Cryptosporidium parvum

The probability of illness caused by very low doses of pathogens cannot generally be tested due to the numbers of subjects that would be needed, though such assessments of illness dose response are needed to evaluate drinking water standards. A predictive Bayesian dose-response assessment method was proposed previously to assess the unconditional probability of illness from available information and avoid the inconsistencies of confidence-based approaches. However, the method uses knowledge of the conditional dose-response form, and this form is not well established for the illness endpoint. A conditional parametric dose-response function for gastroenteric illness is proposed here based on simple numerical models of self-organized host-pathogen systems and probabilistic arguments. In the models, illnesses terminate when the host evolves by processes of natural selection to a self-organized critical value of wellness. A generalized beta-Poisson illness dose-response form emerges for the population as a whole. Use of this form is demonstrated in a predictive Bayesian dose-response assessment for cryptosporidiosis. Results suggest that a maximum allowable dose of 5.0 x 10(-7) oocysts/exposure (e.g., 2.5 x 10(-7) oocysts/L water) would correspond with the original goals of the U.S. Environmental Protection Agency Surface Water Treatment Rule, considering only primary illnesses resulting from Poisson-distributed pathogen counts. This estimate should be revised to account for non-Poisson distributions of Cryptosporidium parvum in drinking water and total response, considering secondary illness propagation in the population.     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

On an adaptive version of the Metropolis–Hastings algorithm with independent proposal distribution

In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π.     

Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.     

Consortium for Atlantic Regional Assessment: Information Tools for Community Adaptation to Changes in Climate or Land Use

To inform local and regional decisions about protecting short-term and long-term quality of life, the Consortium for Atlantic Regional Assessment (CARA) provides data and tools (for the northeastern United States) that can help decision makers understand how outcomes of their decisions could be affected by potential changes in both climate and land use. On an interactive, user-friendly website, CARA has amassed data on climate (historical records and future projections for seven global climate models), land cover, and socioeconomic and environmental variables, along with tools to help decision makers tailor the data for their own decision types and locations. CARA Advisory Council stakeholders help identify what information and tools stakeholders would find most useful and how to present these; they also provide in-depth feedback for subregion case studies. General lessons include: (1) decision makers want detailed local projections for periods short enough to account for extreme events, in contrast to the broader spatial and temporal observations and projections that are available or consistent at a regional level; (2) stakeholders will not use such a website unless it is visually appealing and easy to find the information they want; (3) some stakeholders need background while others want to go immediately to data, and some want maps while others want text or tables. This article also compares what has been learned across case studies of Cape May County, New Jersey, Cape Cod, Massachusetts, and Hampton Roads, Virginia, relating specifically to sea-level rise. Lessons include: (1) groups can be affected differently by physical dangers compared with economic dangers; (2) decisions will differ according to decision makers' preferences about waiting and risk tolerance; (3) future scenarios and maps can help assess the impacts of dangers to emergency evacuation routes, homes, and infrastructure, and the natural environment; (4) residents' and decision makers' perceptions are affected by information about potential local impacts from global climate change.     

Progress and cumulation in the human sciences after the fall

What do we mean by progress and cumulation in the social and human sciences? Recent thinking in the philosophy and history of science has led to an abandonment of some versions of logical positivism and of verificationism that had a strong deductive and theory testing orientation. What is to replace them is less clear. This paper argues that progress and cumulation can be seen as a process of evaluation and retention within an epistemic community. Scholarly disciplines differ in their social structure and in their epistemic and normative commitments. Since sociology is a fragmented discipline, progress and cumulation differ within its multiple subdisciplines, which to varying extents represent epistemic communities. Brief sketches of progress (advance) and cumulation in several subdisciplines are offered.