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We develop several tools for the determination of sample size and design for MediCal audits. This audit setting involves a population of claims for reimbursement by a healthcare provider which need to be reviewed by an auditor to determine…

统计方法学 · 统计学 2018-02-13 Michelle Norris

Risk-limiting audits (RLAs) offer a statistical guarantee: if a full manual tally of the paper ballots would show that the reported election outcome is wrong, an RLA has a known minimum chance of leading to a full manual tally. RLAs…

应用统计 · 统计学 2018-09-13 Kellie Ottoboni , Philip B. Stark , Mark Lindeman , Neal McBurnett

Counting votes is complex and error-prone. Several statistical methods have been developed to assess election accuracy by manually inspecting randomly selected physical ballots. Two 'principled' methods are risk-limiting audits (RLAs) and…

应用统计 · 统计学 2021-05-13 Zhuoqun Huang , Ronald L. Rivest , Philip B. Stark , Vanessa Teague , Damjan Vukcevic

Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical…

神经与进化计算 · 计算机科学 2018-10-16 Felipe Campelo , Fernanda Takahashi

With historic misses in the 2016 and 2020 US Presidential elections, interest in measuring polling errors has increased. The most common method for measuring directional errors and non-sampling excess variability during a postmortem for an…

应用统计 · 统计学 2023-02-21 Graham Tierney , Alexander Volfovsky

When performing supervised learning with the model selected using validation error from sample splitting and cross validation, the minimum value of the validation error can be biased downward. We propose two simple methods that use the…

统计方法学 · 统计学 2018-02-13 Leying Guan

In this paper, a new method of detection of election fraud is proposed. This method is based on the calculation of the ratio of two standard normal random variables; estimation of parameters of obtained sample and comparison of these…

统计方法学 · 统计学 2022-09-20 Ivan H. Krykun

The margin of victory is easy to compute for many election schemes but difficult for Instant Runoff Voting (IRV). This is important because arguments about the correctness of an election outcome usually rely on the size of the electoral…

人工智能 · 计算机科学 2015-08-21 Michelle Blom , Peter J. Stuckey , Vanessa J. Teague , Ron Tidhar

A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performance is evaluated over several repeats, or runs, on the…

机器学习 · 计算机科学 2026-05-18 Moslem Noori , Elisabetta Valiante , Thomas Van Vaerenbergh , Masoud Mohseni , Ignacio Rozada

Presidential primaries are a critical part of the United States Presidential electoral process, since they are used to select the candidates in the Presidential election. While methods differ by state and party, many primaries involve…

计算机与社会 · 计算机科学 2021-09-20 Michelle Blom , Philip B. Stark , Peter J. Stuckey , Vanessa Teague , Damjan Vukcevic

When evaluating the performance of a model for individualised risk prediction, the sample size needs to be large enough to precisely estimate the performance measures of interest. Current sample size guidance is based on precisely…

ONEAudit provides more efficient risk-limiting audits than other extant methods when the voting system cannot report a cast-vote record linked to each cast card. It obviates the need for re-scanning; it is simpler and more efficient than…

统计方法学 · 统计学 2025-08-20 Jacob V Spertus , Amanda K Glazer , Philip B Stark

Risk-limiting audits (RLAs) are a significant tool in increasing confidence in the accuracy of elections. They consist of randomized algorithms which check that an election's vote tally, as reported by a vote tabulation system, corresponds…

计算机与社会 · 计算机科学 2023-05-09 Bar Karov , Moni Naor

One approach to risk-limiting audits (RLAs) compares randomly selected cast vote records (CVRs) to votes read by human auditors from the corresponding ballot cards. Historically, such methods reduce audit sample sizes by considering how…

计算机与社会 · 计算机科学 2026-03-27 Alexander Ek , Michelle Blom , Philip B. Stark , Peter J. Stuckey , Vanessa J. Teague , Damjan Vukcevic

Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels and even comments scraped from the internet can offer valuable insights into political preferences.…

应用统计 · 统计学 2025-03-20 Alberto Arletti , Maria Letizia Tanturri , Omar Paccagnella

While conformal predictors reap the benefits of rigorous statistical guarantees on their error frequency, the size of their corresponding prediction sets is critical to their practical utility. Unfortunately, there is currently a lack of…

机器学习 · 统计学 2024-03-12 Guneet S. Dhillon , George Deligiannidis , Tom Rainforth

While there exists a large amount of literature on the general challenges of and best practices for trustworthy online A/B testing, there are limited studies on sample size estimation, which plays a crucial role in trustworthy and efficient…

统计方法学 · 统计学 2023-08-21 Jing Zhou , Jiannan Lu , Anas Shallah

Measurement error is a pervasive challenge across many disciplines, yet its impact on sample size determination and the accuracy and precision of estimators regarding the association between an exposure and an outcome remains understudied…

统计方法学 · 统计学 2025-05-27 Honghyok Kim

Risk-limiting audits (RLAs) are expected to strengthen the public confidence in the correctness of an election outcome. We hypothesize that this is not always the case, in part because for large margins between the winner and the runner-up,…

计算机与社会 · 计算机科学 2021-09-17 Asmita Dalela , Oksana Kulyk , Carsten Schürmann

Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of meta-analysis of log-odds-ratios, we investigate how the ways in which simulations…

统计方法学 · 统计学 2020-07-06 Elena Kulinskaya , David C. Hoaglin , Ilyas Bakbergenuly