National Sovereignty and Transnational Philanthropy: The Impact of Countries’ Foreign Aid Restrictions on US Foundation Funding

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Foundations are often criticized as organizations of elite power facing little accountability within their own countries....     

The Role of Nonprofit–Private Collaboration for Nonprofits’ Organizational Resilience

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Growing social, political, and economic uncertainties have shown that organizational resilience is becoming increasingly...     

Bayesian Truncated Poisson Regression with Application to Dutch Illegal Immigrant Data

This article presents a Bayesian approach to the regression analysis of truncated data, with a focus on zero-truncated counts from the Poisson distribution. The approach provides inference not only on the regression coefficients but also on the total sample size and the parameters of the covariate distribution. The theory is applied to some illegal immigrant data from The Netherlands. Several models are fitted with the aid of Markov chain Monte Carlo methods and assessed via posterior predictive p-values. Inferences are compared with those obtained elsewhere using other approaches.     

Semi-parametric analysis of multi-rater data

Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited to multiple labels and typically a pre-processing step (normally assigning the majority class) is performed. We propose Bayesian models for classification and ordinal regression that naturally incorporate multiple expert opinions in defining predictive distributions. The models make use of Gaussian process priors, resulting in great flexibility and particular suitability to text based problems where the number of covariates can be far greater than the number of data instances. We show that using all labels rather than just the majority improves performance on a recent biological dataset.     

Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

This article documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The article is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focus on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process.     

Interval estimation of the median effective dose for a logistic dose-response curve

In 1986, Williams showed how, assuming a logistic dose-response curve, one can construct a confidence interval for the median effective dose from the asymptotic likelihood ratio test. He gave reasons for preferring this likelihood ratio interval to the established interval calculated by applying Fieller's theorem to the maximum-likelihood estimates. Here, we assess the impact of applying a Bartlett adjustment to the likelihood ratio statistic and introduce the score test as an alternative approach for constructing a confidence interval for the median effective dose.     

Bounds on the covariate-time transformation for competing-risks survival analysis

A fundamental problem with the latent-time framework in competing risks is the lack of identifiability of the joint distribution. Given observed covariates along with assumptions as to the form of their effect, then identifiability may obtain. However it is difficult to check any assumptions about form since a more general model may lose identifiability. This paper considers a general framework for modelling the effect of covariates, with the single assumption that the copula dependency structure of the latent times is invariant to the covariates. This framework consists of a set of functions: the covariate-time transformations. The main result produces bounds on these functions, which are derived solely from the crude incidence functions. These bounds are a useful model checking tool when considering the covariate-time transformation resulting from any particular set of further assumptions. An example is given where the widely-used assumption of independent competing risks is checked.