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

A database of two-dimensional images of footwear outsole impressions

Footwear outsole images were obtained from 150 pairs of used shoes. The motivation for constructing the database was to enable a statistical analysis of two-dimensional (2D) images of shoe outsoles, to understand within shoe (between replicate images of the same shoe) and between shoe variability, and to develop methods for the evaluation of forensic pattern evidence of shoeprints. Since we scanned the outsole of the used shoes, the images capture not only the outsole pattern design but also the marks that arise from wear and tear and that may help identify the shoe that made the impression. Each shoe in a pair was scanned five times, so that replicate images can be used to estimate within-shoe variability. In total, there are 1500 2D images in the database. The EverOS footwear scanner was used to capture the outsole of each shoe. The scanner detects the weight distribution of the person wearing the shoe when he or she steps on the scanning surface. It images the portions of the outsole that make contact with the scanning surface. The database is a useful resource for forensic scientists or for anybody else with an interest in image comparison. The database we describe, was constructed by researchers in the Center for Statistics and Applications in Forensic Evidence (CSAFE) at Iowa State University

MULTI-PRODUCT DRY MILLING YIELDS PREDICTION WHEN PRODUCTS ARE NOT INDEPENDENT

The yield of products in the dry milling industry is largely determined by the physical properties of the corn kernel. The main objective of this paper is to investigate several statistical models of dry milling yield prediction based on physical characteristics of corn. Data consisting of one hundred corn samples representing a range of genetic traits and quality differences are used. For each corn sample, sixteen physical and chemical properties together with six dry milling product yields were measured, in a controlled laboratory environment . For each corn sample, we consider a vector of dry milling product yields, and a vector of physical corn characteristics. Several single product models are investigated, two of which implicitly take into account the simplex sample space of product yields. A multivariate model is considered which consists of mapping the sample space from a simplex to unrestricted Euclidean space. Comparisons are performed using a jack-knife like approach

Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr

An Analysis of Grain Production Decline During the Early Transition in Ukraine: Bayesian Inference

The first years of reforms in the former Soviet Union resulted in a sharp decline in agricultural production. Several reasons for the fall have been advanced, including a drop in state deliveries of production inputs, labor and management migration from the largescale collective system to the private sector, and the transition-related break in old production ties and networks. Little is known, however, about the relative contribution of all these factors to the decline in production efficiency. In this study, we quantify the contributions of weather variability, decline in input quantities, and changes in technical efficiency to the decline in Ukrainian grain production over 1989-1992. We model the stochastic production frontier using a three-level hierarchical model, and estimate its parameters from within a Bayesian framework. In the model, the time-varying technical efficiency depends on farm-specific factors. Non-informative or diffuse prior distributions are chosen where possible. We find that the decline in the use of production inputs accounts for over half of total output decline, while weather effects account for about 35% of the decline. The rest is attributable to a decline in the technical efficiency of collective farms during the transition years

Bayesian Estimation of Technical Efficiency of a Single Input

We propose estimation of a stochastic production frontier model within a Bayesian framework to obtain the posterior distribution of single-input-oriented technical efficiency at the firm level. The proposed method is applicable to the estimation of environmental efficiency of agricultural production when the technology interaction with the environment is modeled via public inputs such as soil quality and environmental conditions. All computations are carried out using Markov chain Monte Carlo methods. We illustrate the approach by applying it to production data from Ukrainian collective farms