1,720,959 research outputs found
Genetic Optimization Using Derivatives: The rgenoud Package for R
Get PDFgenoud is an R function that combines evolutionary algorithm methods with a derivative-based (quasi-Newton) method to solve difficult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model's parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.
Election Forensics Toolkit DRG Center Working Paper
Get PDFThere is an acute need for methods of detecting and investigating fraud in elections, because the consequences of electoral fraud are grave for democratic stability and quality. When the electoral process is compromised by fraud, intimidation, or even violence, elections can become corrosive and destabilizing—sapping support for democratic institutions; inflaming suspicion; and stimulating demand for extra-constitutional means of pursuing political agendas, including violence. Accurate information about irregularities can help separate false accusations from evidence of electoral malfeasance. Accurate information about the scope of irregularities can also provide a better gauge of election quality. Finally, accurate information about the geographic location of malfeasance—the locations where irregularities occurred and how they cluster—can allow election monitors and pro-democracy organizations to focus attention and resources more efficiently and to substantiate their assessments of electoral quality.Election forensics is an emerging field in which scholars use a diverse set of statistical tools—including techniques similar to those developed to detect financial fraud—to analyze numerical electoral data and detect where patterns deviate from those that should occur naturally, following demonstrated mathematical principles. Numbers that humans have manipulated present patterns that are unlikely to occur if produced by a natural process—such as free and fair elections or normal commercial transactions. These deviations suggest either that the numbers were intentionally altered or that other factors—such as a range of normal strategic voting practices—influenced the electoral results. The greater the number of statistical tests that identify patterns that deviate from what is expected to naturally occur, the more likely that the deviation results from fraud rather than legal strategic voting.Through a Research and Innovation Grant funded by USAID's Center of Excellence on Democracy, Human Rights, and Governance under the Democracy Fellows and Grants Program, a research team from the University of Michigan, led by Professors Walter Mebane and Allen Hicken, built an innovative online tool, the Election Forensics Toolkit, that allows researchers and practitioners to conduct complex statistical analysis on detailed, localized data produced through the electoral process. The Election Forensics Toolkit presents results in a variety of ways—including detailed country maps showing "hot spots" of potential fraud—that allow practitioners not only to see where electoral fraud may have occurred but also the probability that the disturbances in the election data that the statistical analyses detect are attributable to fraud, rather than to other cultural or political influences, such as gerrymandering or geographic distribution of voting constituencies, among others.The team also produced two publications under the DFG grant: a Guide to Election Forensics and a more detailed Elections Forensics Toolkit DRG Center Working Paper. The Guide provides a more general introduction to election forensics as a field, and the DRG Center Working Paper focuses on presenting in detail the results of applying election forensics to specific elections in Afghanistan, Albania, Bangladesh, Cambodia, Kenya, Libya, South Africa, and Ugand
Congressional Campaign Contributions, District Service and Electoral Outcomes in the United States: Statistical Tests of a Formal Game Model with Nonlinear Dynamics
No full textUsing a two-stage game model, with the second stage being a system of ordinary differential equations, I argue that candidates, political parties and financial contributors interact strategically in American congressional elections in a way that is inherently nonlinear. The nonlinearity explains longstanding anomalies in the congressional elections literature regarding candidate finances, district service and votes for the incumbent. Congressional races in which the incumbent faces a challenge are generated by dynamical systems that have Hopf and saddle connection bifurcations. A small change in the challenger's quality or in the type of district service can change a stable incumbent advantage into a race with growing oscillations in which the incumbent's chances are uncertain. Normal form equations from local bifurcation theory, and topological considerations, motivate a statistical model that can recover qualitative features of the dynamics from cross-sectional data. I estimate and test the model using district-level data from the 1984 and 1986 U.S. House election periods for political action committee campaign contributions, intergovernmental transfers and general election vote shares
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
kkalininMI/EFToolkit: Election Forensics Toolkit
No full textThe "EFToolkit" package helps to implement election forensics analysis using different methods and estimators. It is partly built on the code from "Election Forensics Toolkit" web application sponsored by the USAID and developed by Walter Mebane and Kirill Kalinin
Robust Estimation and Outlier Detection for Overdispersed Multinomial Models of Count Data
No full textWe develop a robust estimator -- the hyperbolic tangent (tanh) estimator --for overdispersed multinomial regression models of count data. The tanh estimator provides accurate estimates and reliable inferences even when the specified model is not good for as much as half of the data. Seriously ill-fitted counts -- outliers -- are identified as part of the estimation. A Monte Carlo sampling experiment shows that the tanh estimator produces good results at practical sample sizes even when ten percent of the data are generated by a significantly dierent process. The experiment shows that, with contaminated data, estimation fails using four other estimators: the nonrobust maximum likelihood estimator, the additive logistic model and two SUR models. Using the tanh estimator to analyze data from Florida for the 2000 presidential election matches well-known features of the election that the other four estimators fail to capture. In an analysis of data from the 1993 Polish parliamentary election, the tanh estimator gives sharper inferences than does a previously proposed heteroscedastic SUR model
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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