Election Forensics and the 2004 Venezuelan Presidential Recall Referendum as a Case Study

A referendum to recall President Hugo Ch\'{a}vez was held in Venezuela in August of 2004. In the referendum, voters were to vote YES if they wished to recall the President and NO if they wanted him to continue in office. The official results were 59% NO and 41% YES. Even though the election was monitored by various international groups including the Organization of American States and the Carter Center (both of which declared that the referendum had been conducted in a free and transparent manner), the outcome of the election was questioned by other groups both inside and outside of Venezuela. The collection of manuscripts that comprise this issue of Statistical Science discusses the general topic of election forensics but also focuses on different statistical approaches to explore, post-election, whether irregularities in the voting, vote transmission or vote counting processes could be detected in the 2004 presidential recall referendum. In this introduction to the Venezuela issue, we discuss the more recent literature on post-election auditing, describe the institutional context for the 2004 Venezuelan referendum, and briefly introduce each of the five contributions.Comment: Published in at http://dx.doi.org/10.1214/11-STS379 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Assessing the Adequacy of Diets: A Brief Commentary

Estimating the proportion of the population at risk of a dietary deficiency has long been a problem and different approaches have been advocated. The author presents a summary of the features of a method developed to estimate usual nutrient intake distributions. She also discusses the type of data needed to make inferences about the proportion of the population at risk of deficiencies, and argues that, under certain assumptions, it may be possible to address the problem with data already available

Sampling genotypes in large pedigrees with loops

Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Scalar-Gibbs is easy to implement, and it is widely used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. These problems do not arise if the genotypes are sampled jointly from the entire pedigree. This paper proposes a method to jointly sample genotypes. The method combines the Elston-Stewart algorithm and iterative peeling, and is called the ESIP sampler. For a hypothetical pedigree, genotype probabilities are estimated from samples obtained using ESIP and also scalar-Gibbs. Approximate probabilities were also obtained by iterative peeling. Comparisons of these with exact genotypic probabilities obtained by the Elston-Stewart algorithm showed that ESIP and iterative peeling yielded genotypic probabilities that were very close to the exact values. Nevertheless, estimated probabilities from scalar-Gibbs with a chain of length 235 000, including a burn-in of 200 000 steps, were less accurate than probabilities estimated using ESIP with a chain of length 10 000, with a burn-in of 5 000 steps. The effective chain size (ECS) was estimated from the last 25 000 elements of the chain of length 125 000. For one of the ESIP samplers, the ECS ranged from 21 579 to 22 741, while for the scalar-Gibbs sampler, the ECS ranged from 64 to 671. Genotype probabilities were also estimated for a large real pedigree consisting of 3 223 individuals. For this pedigree, it is not feasible to obtain exact genotype probabilities by the Elston-Stewart algorithm. ESIP and iterative peeling yielded very similar results. However, results from scalar-Gibbs were less accurate

The Effects of Environmental Policy on Trade-offs in Weed Control Management

This paper presents a novel approach for generating information for regulatory and policy analysis, based on farmers\u27 adoption of weed control technology. A simulation model, WISH, is used to generate cost and risk information on 258 weed control strategies. The environmental policies simulated are bans on triazines and broadcast application technology

STATISTICAL ANALYSIS OF GENE EXPRESSION MICROARRAYS

This manuscript is composed of two major sections. In the first section of the manuscript we introduce some of the biological principles that form the bases of cDNA microarrays and explain how the different analytical steps introduce variability and potential biases in gene expression measurements that can sometimes be dificult to properly address. We address statistical issues associated to the measurement of gene expression (e.g., image segmentation, spot identification), to the correction for back-ground fluorescence and to the normalization and re-scaling of data to remove effects of dye, print-tip and others on expression. In this section of the manuscript we also describe the standard statistical approaches for estimating treatment effect on gene expression, and briefly address the multiple comparisons problem, often referred to as the big p small n paradox. In the second major section of the manuscript, we discuss the use of multiple scans as a means to reduce the variability of gene expression estimates. While the use of multiple scans under the same laser and sensor settings has already been proposed (Romualdi et al. 2003), we describe a general hierarchical modeling approach proposed by Love and Carriquiry (2005) that enables use of all the readings obtained under varied laser and sensor settings for each slide in the analyses, even if the number of readings per slide vary across slides. This technique also uses the varied settings to correct for some amount of the censoring discussed in the first section. It is to be expected that when combining scans and correcting for censoring, the estimate of gene expression will have smaller variance than it would have if based on a single spot measurement. In turn, expression estimates with smaller variance are expected to increase the power of statistical tests performed on them

Gene expression patterns during somatic embryo development and germination in maize Hi II cultures

Gene expression changes associated with embryogenic callus formation and with somatic embryo maturation and germination were examined in a regenerationproficient hybrid line of Zea mays, Hi II. 12,060 element maize cDNA microarrays were used to generate gene expression profiles from embryogenic calli induced to undergo embryo maturation and germination. No statistically significant gene expression changes were detected in comparing embryogenic with total callus. On the other hand, over 1,000 genes showed significant time variation during somatic embryo development. In general, a substantial number of genes were downregulated during embryo maturation, largely histone and ribosomal protein genes, which may result from a slow down in cell proliferation and growth during embryo maturation. The expression of these genes dramatically recovered at germination. Other genes upregulated during embryo maturation included genes encoding hydrolytic enzymes (nucleases, glucosidases and proteases) and a few storage genes (zein and caleosin), which are good candidates for developmental marker genes. Germination is accompanied by the upregulation of a number of stress response and membrane transporter genes, and, as expected, greening is associated with the upregulation of many genes encoding photosynthetic and chloroplast components. Thus, some, but not all genes, typically associated with zygotic embryogenesis are significantly up or downregulated during somatic embryogenesis in Hi II maize line regeneration