A Re-examination of the Relation between Democracy and International Trade The Case of Africa

Scholars and policy makers believe that democracy will bring prosperity through integration into the global economy via increased international trade. This study tests two theories as to why democracies might trade more. First, political freedom may be correlated with economic freedom, thus prompting higher levels of economic activity, thereby driving states to trade more. Second, democracy implies higher quality governance either through institutions or policy-making procedures. I utilize a bilateral gravity trade model covering approximately 150 countries from 1950 to 1999, with fixed effects for time, importers and exporters. I find the theory that democracy, and many of its components, promotes international trade unconvincing. Economic freedom does not have the expected impact on international trade levels, but quality of governance variables have broad economic and statistical significance.trade, democracy, governance, Africa, gravity model

Population Structure and Cryptic Relatedness in Genetic Association Studies

We review the problem of confounding in genetic association studies, which arises principally because of population structure and cryptic relatedness. Many treatments of the problem consider only a simple ``island'' model of population structure. We take a broader approach, which views population structure and cryptic relatedness as different aspects of a single confounder: the unobserved pedigree defining the (often distant) relationships among the study subjects. Kinship is therefore a central concept, and we review methods of defining and estimating kinship coefficients, both pedigree-based and marker-based. In this unified framework we review solutions to the problem of population structure, including family-based study designs, genomic control, structured association, regression control, principal components adjustment and linear mixed models. The last solution makes the most explicit use of the kinships among the study subjects, and has an established role in the analysis of animal and plant breeding studies. Recent computational developments mean that analyses of human genetic association data are beginning to benefit from its powerful tests for association, which protect against population structure and cryptic kinship, as well as intermediate levels of confounding by the pedigree.Comment: Published in at http://dx.doi.org/10.1214/09-STS307 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Improving the Efficiency of Genomic Selection

We investigate two approaches to increase the efficiency of phenotypic prediction from genome-wide markers, which is a key step for genomic selection (GS) in plant and animal breeding. The first approach is feature selection based on Markov blankets, which provide a theoretically-sound framework for identifying non-informative markers. Fitting GS models using only the informative markers results in simpler models, which may allow cost savings from reduced genotyping. We show that this is accompanied by no loss, and possibly a small gain, in predictive power for four GS models: partial least squares (PLS), ridge regression, LASSO and elastic net. The second approach is the choice of kinship coefficients for genomic best linear unbiased prediction (GBLUP). We compare kinships based on different combinations of centring and scaling of marker genotypes, and a newly proposed kinship measure that adjusts for linkage disequilibrium (LD). We illustrate the use of both approaches and examine their performances using three real-world data sets from plant and animal genetics. We find that elastic net with feature selection and GBLUP using LD-adjusted kinships performed similarly well, and were the best-performing methods in our study.Comment: 17 pages, 5 figure

Multiple Quantitative Trait Analysis Using Bayesian Networks

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this paper we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP), and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.Comment: 28 pages, 1 figure, code at http://www.bnlearn.com/research/genetics1

Mandatory Vaccination: Why We Still Got to Get Folks to Take Their Shots

Vaccination is widely considered one of the greatest medical achievements of modern civilization. Childhood diseases that were commonplace less than a generation ago are now increasingly rare because of vaccines. In order to be effective at eliminating communicable diseases, vaccines must be administered to sufficient levels of persons in the community. Because of this, public health officials have mandated vaccination for certain diseases as a condition to school attendance. The overwhelming effectiveness of vaccination programs may lead individuals to ignore the benefits of vaccination and focus more on the risk of side effects. Moreover, some have criticized the coercive nature of these programs. These objections may lead to an unacceptably high number of exemptions, which can compromise vaccination programs and leave the population susceptible to outbreaks. This paper explores vaccination programs with an eye toward greater public safety without ignoring the reality of a small but committed group of vaccine critics. The paper begins with a discussion of the historical development of mandatory vaccination policies and the issues posed by exemptions. It then addresses some of these issues in the context of vaccine safety. It also seeks solution by framing the discussion in economic terms. It concludes by recommending stricter enforcement of mandatory requirements for most vaccines and greater dissemination of information on the continued importance of vaccination

Forensic identification: the Island Problem and its generalisations

In forensics it is a classical problem to determine, when a suspect $S$ shares a property $\Gamma$ with a criminal $C$, the probability that $S=C$. In this paper we give a detailed account of this problem in various degrees of generality. We start with the classical case where the probability of having $\Gamma$, as well as the a priori probability of being the criminal, is the same for all individuals. We then generalize the solution to deal with heterogeneous populations, biased search procedures for the suspect, $\Gamma$-correlations, uncertainty about the subpopulation of the criminal and the suspect, and uncertainty about the $\Gamma$-frequencies. We also consider the effect of the way the search for $S$ is conducted, in particular when this is done by a database search. A returning theme is that we show that conditioning is of importance when one wants to quantify the "weight" of the evidence by a likelihood ratio. Apart from these mathematical issues, we also discuss the practical problems in applying these issues to the legal process. The posterior probabilities of $C=S$ are typically the same for all reasonable choices of the hypotheses, but this is not the whole story. The legal process might force one to dismiss certain hypotheses, for instance when the relevant likelihood ratio depends on prior probabilities. We discuss this and related issues as well. As such, the paper is relevant both from a theoretical and from an applied point of view

Keeping Pace with the Times: Exploring the Meaning of Limited Partner for Purposes of the Internal Revenue Code

This is the published version