nils-herrmann/clean_scirepeval huggingface dataset:
55,000 Residents Desperately Need Your Help!

Last week we had an election for the Board of Directors. Many residents believe, as I do, that the election was rigged and what was supposed to be votes being cast by 5,553 of the 15,372 voting households is instead a fixed vote with fixed percentages being assigned to each and every candidate making it impossible to participate in an honest election. The unofficial election results I have faxed along with this letter represent the tallies. Tallies were given after 600 were counted. Then again at 1200,2444,3444,4444, and final count at 5553. After close inspection we believe that there was nothing random about the count and tallies each time and that specific unnatural percentages or rigged percentages were being assigned to each and every candidate. Are we crazy? In a community this diverse and large, can candidates running on separate and oppo-
Fully Bayesian Computing

A fully Bayesian computing environment calls for the possibility of defining vector and array objects that may contain both random and deterministic quantities, and syntax rules that allow treating these objects much like any variables or numeric arrays. Working within the statistical package R, we introduce a new object-oriented framework based on a new random variable data type that is implicitly represented by simulations. We seek to be able to manipulate random variables and posterior simulation objects conveniently and transparently and provide a basis for further development of methods and functions that can access these objects directly. We illustrate the use of this new programming environment with several examples of Bayesian com-puting, including posterior predictive checking and the manipulation of posterior simulations. This new environment is fully Bayesian in that the posterior simulations can be handled directly as random variables.
Bayesian computation for multilevel models: some new methods with applications

Multilevel models (also called hierarchical linear models, random effects regressions, and mixed effects models) are extremely useful in handling hierarchical datasets in statistical applications. Bayesian analysis is a widely accepted formulation for estimating unknown parameters from data. Bayesian methods have found great success in statistical practice. Modern Bayesian inference generally entails computing simulation draws of the parameters from the posterior distribution. However, we often have computational problems such as covergence, computation time, and storage space. The main purpose of this thesis is to propose some computational strategies to handle these problems and discuss using such strategies to simulate from the posterior distribution of the parameters in hierarchical models with respect to specific examples. Two basic computational strategies are discussed. First, we discuss transformed and parameter-expanded Gibbs samplers far multilevel linear and generalized linear models which are used to speed convergence. Second, we propose sampling for Bayesian computation with large datasets. We illustrate with practical examples from our work in statistical application.
Fully Bayesian computing

A fully Bayesian computing environment calls for the possibility of defining vector and array objects that may contain both random and deterministic quantities, and syntax rules that allow treating these objects much like any variables or numeric arrays. Working within the statistical package R, we introduce a new object-oriented framework based on a new random variable data type that is implicitly represented by simulations. We seek to be able to manipulate random variables and posterior simulation objects conveniently and transparently and provide a basis for further development of methods and functions that can access these objects directly. We illustrate the use of this new programming environment with several examples of Bayesian com-puting, including posterior predictive checking and the manipulation of posterior simulations. This new environment is fully Bayesian in that the posterior simulations can be handled directly as random variables.
Should you measure the radon concentration in your home

Radon, a naturally occurring radioactive element, was discovered in the year 1900, and high lungcancer rates among miners were recognized hundreds of years before that, but it wasn’t until the1950s that researchers accepted that exposure to radon decay products was one of the major causesof the very high lung cancer rate among miners. Risks to the general population weren’t recognizeduntil the early 1980s, when it was discovered that even homes can have high concentrations ofradon—indeed, the airborne radioactivity in some (very rare) homes is higher than is allowed inuranium mines!Once a risk from radon was recognized, the U.S. Environmental Protection Agency, and otherorganizations including the Department of Energy, launched research eﬀorts to help assess risks andremediation options: what is the statistical and spatial distribution of indoor radon, what methodscan be used to reduce radon concentrations in homes, what is the risk as a function of exposure,and so on. In this chapter, we summarize some of the data on radon distributions and some of therisk estimates, and then discuss how the available information can be used in a decision analysis tomake recommendations on who should perform radon measuremnents or remediation. This chapterfocuses on the decision analysis itself (originally discussed in Lin et al., 1999), and not on thestatistical model, based on Bayesian hierarchical regression, that fed into the decision analysis. Formore information about hierarchical regression, see Gelman et al. (2003, section 22.4).
Measures of explained variance and pooling in multilevel models

Explained variance (R-squared) has been generalized in various ways to multilevel models for hierarchical data structures in which individuals are grouped into units, and there are variables measured on individuals and each grouping unit. The models are based on regression relationships at different levels, with the first level corresponding to the individual data, and subsequent levels corresponding to between-group regressions of individual predictor effects on grouping unit variables. We present an approach to defining R-squared at each level of the multilevel model, rather than attempting to create a single summary measure of fit, by comparing variances within the model. In simple regression, our measure generalizes the classical adjusted R-squared. We also discuss a related variance comparison to summarize the degree to which estimates at each level of the model are pooled together based on the level-specific regression relationship, rather than estimated separately. This pooling factor is related to the concept of shrinkage in simple hierarchical models. We illustrate the methods on a dataset of radon in houses within counties using a series of multilevel models.
Direct Data Manipulation for Local Decision Analysis as Applied to the Problem of Arsenic in Drinking Water from Tube Wells in Bangladesh

A wide variety of tools are available, both parametric and nonparametric, for analyzing spatial data. However, it is not always clear how to translate statistical inferences into decision recommendations. This article explores the possibilities of estimating the effects of decision options using very direct manipulation of data, bypassing formal statistical analysis. We illustrate with the application that motivated this research, a study of arsenic in drinking water in nearly 5,000 wells in a small area in rural Bangladesh. We estimate the potential benefits of two possible remedial actions: (1) recommendations that people switch to nearby wells with lower arsenic levels; and (2) drilling new community wells. We use simple nonparametric clustering methods and estimate uncertainties using cross‐validation.
Average predictive effects for models with nonlinearity , interactions , and variance components ∗

In a predictive model, what is the expected change in the outcome associated with a unit change in one of the inputs? In a linear regression model without interactions, this average predictive effect is simply a regression coefficient (with associated uncertainty). In a model with nonlinearity or interactions, however, the average predictive effect in general depends on the values of the predictors. We consider various definitions based on averages over a population distribution of the predictors, and we compute standard errors based on uncertainty in model parameters. We illustrate with a study of criminal justice data for urban counties in the United States. The outcome of interest measures whether a convicted felon received a prison sentence rather than a jail or non-custodial sentence, with predictors available at both individual and county levels. We fit three models: a hierarchical logistic regression with varying coefficients for the within-county intercepts as well as for each individual predictor; a hierarchical model with varying intercepts only; and a non-hierarchical model that ignores the multilevel nature of the data. The regression coefficients have different interpretations for the different models; in contrast, the models can be compared directly using predictive effects. Furthermore, predictive effects clarify the interplay between the individual and county predictors for the hierarchical models, as well as illustrating the relative size of varying county effects.
Standard Voting Power Indexes Do Not Work: An Empirical Analysis

Voting power indexes such as that of Banzhaf are derived, explicitly or implicitly, from the assumption that all votes are equally likely (i.e., random voting). That assumption implies that the probability of a vote being decisive in a jurisdiction with n voters is proportional to 1/√n. In this article the authors show how this hypothesis has been empirically tested and rejected using data from various US and European elections. They find that the probability of a decisive vote is approximately proportional to 1/n. The random voting model (and, more generally, the square-root rule) overestimates the probability of close elections in larger jurisdictions. As a result, classical voting power indexes make voters in large jurisdictions appear more powerful than they really are. The most important political implication of their result is that proportionally weighted voting systems (that is, each jurisdiction gets a number of votes proportional to n) are basically fair. This contradicts the claim in the voting power literature that weights should be approximately proportional to √n.
Bayesian Analysis of Serial Dilution Assays

In a serial dilution assay, the concentration of a compound is estimated by combining measurements of several different dilutions of an unknown sample. The relation between concentration and measurement is nonlinear and heteroscedastic, and so it is not appropriate to weight these measurements equally. In the standard existing approach for analysis of these data, a large proportion of the measurements are discarded as being above or below detection limits. We present a Bayesian method for jointly estimating the calibration curve and the unknown concentrations using all the data. Compared to the existing method, our estimates have much lower standard errors and give estimates even when all the measurements are outside the “detection limits.” We evaluate our method empirically using laboratory data on cockroach allergens measured in house dust samples. Our estimates are much more accurate than those obtained using the usual approach. In addition, we develop a method for determining the “effective weight” attached to each measurement, based on a local linearization of the estimated model. The effective weight can give insight into the information conveyed by each data point and suggests potential improvements in design of serial dilution experiments.
An Experimental Study of Storable Votes

The storable votes mechanism is a voting method for committees that meet periodically to consider a series of binary decisions. Each member is allocated a fixed budget of votes to be cast as desired over the sequence of decisions. This provides incentives for voters to spend more votes on those decisions that matter to them more, typically generating welfare gains over standard majority voting with non-storable votes. Equilibrium strategies have a very intuitive feature—the number of votes cast must be monotonic in the voter's intensity of preferences—but are otherwise difficult to calculate, raising questions of practical implementation. We present experimental data where realized efficiency levels were remarkably close to theoretical equilibrium predictions, while subjects adopted monotonic but off-equilibrium strategies. We are led to conclude that concerns about the complexity of the game may have limited practical relevance.
Forming Voting Blocs and Coalitions as a Prisoner's Dilemma: A Possible Theoretical Explanation for Political Instability

Individuals in a committee or election can increase their voting power by forming coalitions. This behavior is shown here to yield a prisoner’s dilemma, in which a subset of voters can increase their power, while reducing average voting power for the electorate as a whole. This is an unusual form of the prisoner’s dilemma in that cooperation is the selfish act that hurts the larger group. Under a simple model, the privately optimal coalition size is approximately 1.4 times the square root of the number of voters. When voters’ preferences are allowed to differ, coalitions form only if voters are approximately politically balanced. We propose a dynamic view of coalitions, in which groups of voters choose of their own free will to form and disband coalitions, in a continuing struggle to maintain their voting power. This is potentially an endogenous mechanism for political instability, even in a world where individuals’ (probabilistic) preferences are fixed and known.
A method for estimating design-based sampling variances for surveys with weighting, poststratification, and

It is common practice to use weighting, poststratiication, and raking to correct for sampling and nonsampling biases and to improve eeciency of estimation in sample surveys. However, there is no standard method for computing sampling variances of estimates that use these adjustments in combination. In this paper we develop such a method, using three ideas: (1) a general notation that uniies the diierent forms of weighting adjustment, (2) a variance decomposition to estimate sampling variances conditional and unconditional on sample sizes within poststratiication categories, and (3) numerical computation using a delta method. We apply our approach to the problem that motivated this research, the New York City Social Indicators Survey, a telephone survey that uses inverse-probability weighting, poststratiication, and raking to correct for sampling design and nonresponse. Our variance estimates systematically diier from those obtained using methods that do not account for the design of the weighting scheme. Assuming simple random sampling leads to underestimating the sampling variance, and treating all weights as inverse-probability causes variances to be overestimated.
Regression Modeling and Meta-Analysis for Decision Making

Regression models are often used, explicitly or implicitly, for decision making. However, the choices made in setting up the models (e.g., inclusion of predictors based on statistical significance) do not map directly into decision procedures. Bayesian inference works more naturally with decision analysis but presents problems in practice when noninformative prior distributions are used with sparse data. We do not attempt to provide a general solution to this problem, but rather present an application of a decision problem in which inferences from a regression model are used to estimate costs and benefits. Our example is a reanalysis of a recent meta-analysis of incentives for reducing survey nonresponse. We then apply the results of our fitted model to the New York City Social Indicators Survey, a biennial telephone survey with a high nonresponse rate. We consider the balance of estimated costs, cost savings, and response rate for different choices of incentives. The explicit analysis of the decision problem reveals the importance of interactions in the fitted regression model.
Methodology as ideology : the prisoner ’ s dilemma as misapplied to trench warfare

The Evolution of Cooperation, by Axelrod (1984), is a highly influential study that identifies the benefits of cooperative strategies in the iterated prisoner’s dilemma. We argue that the most extensive historical analysis in the book, a study of cooperative behavior in First World War trenches, is in error. Contrary to Axelrod’s claims, the soldiers in the Western Front were not in a prisoner’s dilemma (iterated or otherwise), and their cooperative behavior can be explained much more parsimoniously as immediately reducing their risks. We discuss the political implications of this misapplication of game theory.
A Bayesian approach to the selection and testing of mixture models

An important aspect of mixture modeling is the selection of the number of mixture components. In this paper, we discuss the Bayes factor as a selection tool. The discussion will focus on two aspects: computation of the Bayes factor and prior sensitivity. For the computation, we propose a variant of Chib's estimator that accounts for the non-identifiability of the mixture components. To reduce the prior sensitivity of the Bayes factor, we propose to extend the model with a hyperprior. We further discuss the use of posterior predictive checks for examining the fit of the model. The ideas are illustrated by means of a psychiatric diagnosis example.
A Bayesian Formulation of Exploratory Data Analysis and Goodness‐of‐fit Testing *

Exploratory data analysis (EDA) and Bayesian inference (or, more generally, complex statistical modeling)—which are generally considered as unrelated statistical paradigms—can be particularly effective in combination. In this paper, we present a Bayesian framework for EDA based on posterior predictive checks. We explain how posterior predictive simulations can be used to create reference distributions for EDA graphs, and how this approach resolves some theoretical problems in Bayesian data analysis. We show how the generalization of Bayesian inference to include replicated data yrep and replicated parameters θrep follows a long tradition of generalizations in Bayesian theory.
Discussion Paper Series An Experimental Study of Storable Votes

The storable votes mechanism is a method of voting for committees that meet periodically to consider a series of binary decisions. Each member is allocated a fixed budget of votes to be cast as desired over the multiple decisions. Voters are induced to spend more votes on those decisions that matter to themmost, shifting the ex ante probability of winning away from decisions they value less and towards decisions they value more, typically generating welfare gains over standard majority voting with non-storable votes. The equilibrium strategies have a very intuitive feature–the number of votes cast must be monotonic in the voter’s intensity of preferences–but are otherwise difficult to calculate, raising questions of practical implementation. In our experiments, realized efficiency levels were remarkably close to theoretical equilibrium predictions, while subjects adopted monotonic but off-equilibrium strategies. We are lead to conclude that concerns about the complexity of the game may have limited practical relevance.
The Mathematics and Statistics of Voting Power

In an election, voting power—the probability that a single vote is 
decisive—is affected by the rule for aggregating votes into a single outcome. 
Voting power is important for studying political representation, fairness and 
strategy, and has been much discussed in political science. Although power 
indexes are often considered as mathematical definitions, they ultimately 
depend on statistical models of voting. Mathematical calculations of voting 
power usually have been performed under the model that votes are decided 
by coin flips. This simple model has interesting implications for weighted 
elections, two-stage elections (such as the U.S. Electoral College) and 
coalition structures. We discuss empirical failings of the coin-flip model of 
voting and consider, first, the implications for voting power and, second, 
ways in which votes could be modeled more realistically. Under the random 
voting model, the standard deviation of the average of n votes is proportional 
to 1/√n, but under more general models, this variance can have the form cn^(−α) or √a−b log n. Voting power calculations undermore realistic models 
present research challenges in modeling and computation.
Empirically Evaluating the Electoral College

The 2000 U.S. presidential election has once again rekindled interest in possible electoral reform including the possible elimination of the Electoral College. Most arguments against the Electoral College have either been based on ancedotal evidence from particular elections or on highly stylized formal models We take a very different approach here. We develop a set of statistical models based on historical election results to evaluate the Electoral College as it has performed in practice. Thus, while we do not directly address the normative question of the value of the U.S. Electoral College, this paper does provide the necessary tools and evidence to make such an evaluation. We show that when one preforms such an analysis there is not much basis to argue for reforming the Electoral College. We first show that while the Electoral College may once have been biased against the Democrats, given the current distribution of voters, neither party is advantaged by the system. Further, the electoral vote will differ from the popular vote will only when the average votes shares are very close to a half. We then show that while there has been much temporal variation in voting power over the last several decades, the voting power of individual citizens would not likely increase under a popular vote system of electing the president.
Promotion of well-switching to mitigate the current arsenic crisis in Bangladesh.

To survey tube wells and households in Araihazar upazila, Bangladesh, to set the stage for a long-term epidemiological study of the consequences of chronic arsenic exposure.



Water samples and household data were collected over a period of 4 months in 2000 from 4997 contiguous tube wells serving a population of 55000, the position of each well being determined to within +/- 30 m using Global Positioning System receivers. Arsenic concentrations were determined by graphite-furnace atomic-absorption spectrometry. In addition, groundwater samples collected every 2 weeks for an entire year from six tube wells were analysed for arsenic by high-resolution inductively coupled plasma-mass spectrometry.



Half of the wells surveyed in Araihazar had been installed in the previous 5 years; 94% were privately owned. Only about 48% of the surveyed wells supplied water with an arsenic content below 50 micro g/l, the current Bangladesh standard for drinking-water. Similar to other regions of Bangladesh and West Bengal, India, the distribution of arsenic in Araihazar is spatially highly variable (range: 5-860 micro g/l) and therefore difficult to predict. Because of this variability, however, close to 90% of the inhabitants live within 100 m of a safe well. Monitoring of six tube wells currently meeting the 50 micro g/l standard showed no indication of a seasonal cycle in arsenic concentrations coupled to the hydrological cycle. This suggests that well-switching is a viable option in Araihazar, at least for the short term.



Well-switching should be more systematically encouraged in Araihazar and many other parts of Bangladesh and West Bengal, India. Social barriers to well-switching need to be better understood and, if possible, overcome.