cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11716641
title: On Racial Disparities in Recent Fatal Police Shootings

description: Fatal police shootings in the United States continue to be a polarizing social and political issue. Clear disagreement between racial proportions of victims and nationwide racial demographics together with graphic video footage has created fertile ground for controversy. However, simple population level summary statistics fail to take into account fundamental local characteristics such as county-level racial demography, local arrest demography, and law enforcement density. Using data on fatal police shootings between January 2015 and July 2016, I implement a number of straightforward resampling procedures designed to carefully examine how unlikely the victim totals from each race are with respect to these local population characteristics if no racial bias were present in the decision to shoot by police. I present several approaches considering the shooting locations both as fixed and also as a random sample. In both cases, I find overwhelming evidence of a racial disparity in shooting victims with respect to local population demographics but substantially less disparity after accounting for local arrest demographics. I conclude the analyses by examining the effect of police-worn body cameras and find no evidence that the presence of such cameras impacts the racial distribution of victims. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12075852
title: Optimal Legislative County Clustering in North Carolina

description: North Carolina’s constitution requires that state legislative districts should not split counties. However, counties must be split to comply with the “one person, one vote” mandate of the U.S. Supreme Court. Given that counties must be split, the North Carolina legislature and the courts have provided guidelines that seek to reduce counties split across districts while also complying with the “one person, one vote” criterion. Under these guidelines, the counties are separated into clusters; each cluster contains a specified number of districts and that are drawn independent from other clusters. The primary goal of this work is to develop, present, and publicly release an algorithm to optimally cluster counties according to the guidelines set by the court in 2015. We use this tool to investigate the optimality and uniqueness of the enacted clusters under the 2017 redistricting process. We verify that the enacted clusters are optimal, but find other optimal choices. We emphasize that the tool we provide lists <i>all</i> possible optimal county clusterings. We also explore the stability of clustering under changing statewide populations and project what the county clusters may look like in the next redistricting cycle beginning in 2020/2021. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12075852.v1
title: Optimal Legislative County Clustering in North Carolina

description: North Carolina’s constitution requires that state legislative districts should not split counties. However, counties must be split to comply with the “one person, one vote” mandate of the U.S. Supreme Court. Given that counties must be split, the North Carolina legislature and the courts have provided guidelines that seek to reduce counties split across districts while also complying with the “one person, one vote” criterion. Under these guidelines, the counties are separated into clusters; each cluster contains a specified number of districts and that are drawn independent from other clusters. The primary goal of this work is to develop, present, and publicly release an algorithm to optimally cluster counties according to the guidelines set by the court in 2015. We use this tool to investigate the optimality and uniqueness of the enacted clusters under the 2017 redistricting process. We verify that the enacted clusters are optimal, but find other optimal choices. We emphasize that the tool we provide lists <i>all</i> possible optimal county clusterings. We also explore the stability of clustering under changing statewide populations and project what the county clusters may look like in the next redistricting cycle beginning in 2020/2021. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14885532
title: Variable Selection with Second-Generation P-Values

description: Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach called Penalized regression with Second-Generation P-Values (ProSGPV). It captures the true model at the best rate achieved by current standards, is easy to implement in practice, and often yields the smallest parameter estimation error. The idea is to use an ℓ0 penalization scheme with second-generation p-values (SGPV), instead of traditional ones, to determine which variables remain in a model. The approach yields tangible advantages for balancing support recovery, parameter estimation, and prediction tasks. The ProSGPV algorithm can maintain its good performance even when there is strong collinearity among features or when a high dimensional feature space with <i>p</i> &gt; <i>n</i> is considered. We present extensive simulations and a real-world application comparing the ProSGPV approach with smoothly clipped absolute deviation (SCAD), adaptive lasso (AL), and minimax concave penalty with penalized linear unbiased selection (MC+). While the last three algorithms are among the current standards for variable selection, ProSGPV has superior inference performance and comparable prediction performance in certain scenarios. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.14885532.v2
title: Variable Selection with Second-Generation P-Values

description: Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach called Penalized regression with Second-Generation P-Values (ProSGPV). It captures the true model at the best rate achieved by current standards, is easy to implement in practice, and often yields the smallest parameter estimation error. The idea is to use an ℓ0 penalization scheme with second-generation p-values (SGPV), instead of traditional ones, to determine which variables remain in a model. The approach yields tangible advantages for balancing support recovery, parameter estimation, and prediction tasks. The ProSGPV algorithm can maintain its good performance even when there is strong collinearity among features or when a high dimensional feature space with <i>p</i> &gt; <i>n</i> is considered. We present extensive simulations and a real-world application comparing the ProSGPV approach with smoothly clipped absolute deviation (SCAD), adaptive lasso (AL), and minimax concave penalty with penalized linear unbiased selection (MC+). While the last three algorithms are among the current standards for variable selection, ProSGPV has superior inference performance and comparable prediction performance in certain scenarios. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.14885532.v1
title: Variable Selection With Second-Generation <i>P</i>-Values

description: Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here, we report on a novel variable selection approach called penalized regression with second-generation p-values (ProSGPV). It captures the true model at the best rate achieved by current standards, is easy to implement in practice, and often yields the smallest parameter estimation error. The idea is to use an l0 penalization scheme with second-generation p-values (SGPV), instead of traditional ones, to determine which variables remain in a model. The approach yields tangible advantages for balancing support recovery, parameter estimation, and prediction tasks. The ProSGPV algorithm can maintain its good performance even when there is strong collinearity among features or when a high-dimensional feature space with p &gt; n is considered. We present extensive simulations and a real-world application comparing the ProSGPV approach with smoothly clipped absolute deviation (SCAD), adaptive lasso (AL), and minimax concave penalty with penalized linear unbiased selection (MC+). While the last three algorithms are among the current standards for variable selection, ProSGPV has superior inference performance and comparable prediction performance in certain scenarios.
DOI: 10.6084/m9.figshare.1478002
title: Explaining the Importance of Variability to Engineering Students

description: One of the main challenges of teaching statistics to engineering students is to convey the importance of being conscious of the presence of variability and of taking it into account when making technical and managerial decisions. Often, technical subjects are explained in an ideal and deterministic environment. This article shows the possibilities of simple electrical circuits—the Wheatstone Bridge among them—to explain to students how to characterize variability, how it is transmitted, and how it affects decisions. Additionally, they can be used to introduce the importance of robustness by showing that taking into account the variability of components allows the design of cheaper products with greater benefits than if one were to simply apply formulas that consider variables as exact values. The results are quite unexpected, and they arouse the interest and motivation of students. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1478002.v3
title: Explaining the Importance of Variability to Engineering Students

description: One of the main challenges of teaching statistics to engineering students is to convey the importance of being conscious of the presence of variability and of taking it into account when making technical and managerial decisions. Often, technical subjects are explained in an ideal and deterministic environment. This article shows the possibilities of simple electrical circuits—the Wheatstone Bridge among them—to explain to students how to characterize variability, how it is transmitted, and how it affects decisions. Additionally, they can be used to introduce the importance of robustness by showing that taking into account the variability of components allows the design of cheaper products with greater benefits than if one were to simply apply formulas that consider variables as exact values. The results are quite unexpected, and they arouse the interest and motivation of students. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1478002.v2
title: Explaining the Importance of Variability to Engineering Students

description: ABSTRACTOne of the main challenges of teaching statistics to engineering students is to convey the importance of being conscious of the presence of variability and of taking it into account when making technical and managerial decisions. Often, technical subjects are explained in an ideal and deterministic environment. This article shows the possibilities of simple electrical circuits –the Wheatstone Bridge among them— to explain to students how to characterize variability, how it is transmitted and how it affects decisions. Additionally they can be used to introduce the importance of robustness by showing that taking into account the variability of components allows the design of cheaper products with greater benefits than if one were to simply apply formulas that consider variables as exact values. The results are quite unexpected, and they arouse the interest and motivation of students. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3085162.v6
title: The ASA Statement on <i>p</i>-Values: Context, Process, and Purpose

description: The ASA Statement on <i>p</i>-Values: Context, Process, and Purpose
DOI: 10.6084/m9.figshare.8865977
title: Correction of Survival Bias in a Study About Increased Mortality of Heads of Government

description: A recent study reported increased mortality of heads in government. To avoid the time-dependent bias (also known as immortal-time bias), survival from <i>last</i> election was compared between election winners and runners-up. We claim that this data manipulation results in bias due to conditioning on future events; survival should be compared from <i>first</i> election as well as winning should be considered as a time-dependent covariate. We collected the missing life-time periods and redesigned the study to display this bias using Lexis diagrams and multistate methodology. We found that the bias that we termed the healthy candidate bias was even more severe than the time-dependent bias.
DOI: 10.6084/m9.figshare.7988687
title: Sampling Strategies for Fast Updating of Gaussian Markov Random Fields

description: Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the sparse precision matrices needed for random variable generation. Typically in Bayesian computation, GMRFs are updated jointly in a block Gibbs sampler or componentwise in a single-site sampler via the full conditional distributions. The former approach can speed convergence by updating correlated variables all at once, while the latter avoids solving large matrices. We consider a sampling approach in which the underlying graph can be cut so that conditionally independent sites are updated simultaneously. This algorithm allows a practitioner to parallelize updates of subsets of locations or to take advantage of “vectorized” calculations in a high-level language such as R. Through both simulated and real data, we demonstrate computational savings that can be achieved versus both single-site and block updating, regardless of whether the data are on a regular or an irregular lattice. The approach provides a good compromise between statistical and computational efficiency and is accessible to statisticians without expertise in numerical analysis or advanced computing.
DOI: 10.6084/m9.figshare.7988687.v2
title: Sampling Strategies for Fast Updating of Gaussian Markov Random Fields

description: Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the sparse precision matrices needed for random variable generation. Typically in Bayesian computation, GMRFs are updated jointly in a block Gibbs sampler or componentwise in a single-site sampler via the full conditional distributions. The former approach can speed convergence by updating correlated variables all at once, while the latter avoids solving large matrices. We consider a sampling approach in which the underlying graph can be cut so that conditionally independent sites are updated simultaneously. This algorithm allows a practitioner to parallelize updates of subsets of locations or to take advantage of “vectorized” calculations in a high-level language such as R. Through both simulated and real data, we demonstrate computational savings that can be achieved versus both single-site and block updating, regardless of whether the data are on a regular or an irregular lattice. The approach provides a good compromise between statistical and computational efficiency and is accessible to statisticians without expertise in numerical analysis or advanced computing.
DOI: 10.6084/m9.figshare.8079644
title: Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

description: When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II–Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with <i>R Shiny</i> to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.
DOI: 10.6084/m9.figshare.8079644.v3
title: Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

description: When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II–Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with <i>R Shiny</i> to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.
DOI: 10.6084/m9.figshare.8865977.v2
title: Correction of Survival Bias in a Study About Increased Mortality of Heads of Government

description: A recent study reported increased mortality of heads in government. To avoid the time-dependent bias (also known as immortal-time bias), survival from <i>last</i> election was compared between election winners and runners-up. We claim that this data manipulation results in bias due to conditioning on future events; survival should be compared from <i>first</i> election as well as winning should be considered as a time-dependent covariate. We collected the missing life-time periods and redesigned the study to display this bias using Lexis diagrams and multistate methodology. We found that the bias that we termed the healthy candidate bias was even more severe than the time-dependent bias.
DOI: 10.6084/m9.figshare.7887860
title: Exact Bayes Factors for the Comparison of Multinomial Distributions

description: This article deals with the problem of comparing multinomial distributions with multiple ordered categories. A graphical procedure is proposed for obtaining the posterior probabilities for the hypotheses of a stochastic dominance relationship, positive cumulative odds ratios, and a likelihood ratio ordering. From these expressions we subsequently obtain exact expressions for the Bayes factors related to these hypotheses. Supplemental materials for running the analysis for the examples presented in the article are available online.
DOI: 10.6084/m9.figshare.7887860.v2
title: Exact Bayes Factors for the Comparison of Multinomial Distributions

description: This article deals with the problem of comparing multinomial distributions with multiple ordered categories. A graphical procedure is proposed for obtaining the posterior probabilities for the hypotheses of a stochastic dominance relationship, positive cumulative odds ratios, and a likelihood ratio ordering. From these expressions we subsequently obtain exact expressions for the Bayes factors related to these hypotheses. Supplemental materials for running the analysis for the examples presented in the article are available online.
DOI: 10.6084/m9.figshare.7988687.v1
title: Sampling Strategies for Fast Updating of Gaussian Markov Random Fields

description: Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the sparse precision matrices needed for random variable generation. Typically in Bayesian computation, GMRFs are updated jointly in a block Gibbs sampler or componentwise in a single-site sampler via the full conditional distributions. The former approach can speed convergence by updating correlated variables all at once, while the latter avoids solving large matrices. We consider a sampling approach in which the underlying graph can be cut so that conditionally independent sites are updated simultaneously. This algorithm allows a practitioner to parallelize updates of subsets of locations or to take advantage of ‘vectorized’ calculations in a high-level language such as R. Through both simulated and real data, we demonstrate computational savings that can be achieved versus both single-site and block updating, regardless of whether the data are on a regular or an irregular lattice. The approach provides a good compromise between statistical and computational efficiency and is accessible to statisticians without expertise in numerical analysis or advanced computing.
DOI: 10.6084/m9.figshare.8855948
title: Hypothesis Testing in Presence of Adversaries

description: We present an extension to the classical problem of hypothesis testing by incorporating actions of an adversary who intends to mislead the decision-maker and attain a certain benefit. After presenting the general problem within an adversarial statistical decision theory framework, we consider the cases of adversaries who can either perturb the data received or modify the underlying data-generating process parametrically. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.8855948.v2
title: Hypothesis Testing in Presence of Adversaries

description: We present an extension to the classical problem of hypothesis testing by incorporating actions of an adversary who intends to mislead the decision-maker and attain a certain benefit. After presenting the general problem within an adversarial statistical decision theory framework, we consider the cases of adversaries who can either perturb the data received or modify the underlying data-generating process parametrically. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7887860.v1
title: Exact Bayes Factors for the Comparison of Multinomial Distributions

description: This article deals with the problem of comparing multinomial distributions with multiple ordered categories. A graphical procedure is proposed for obtaining the posterior probabilities for the hypotheses of a stochastic dominance relationship, positive cumulative odds ratios, and a likelihood ratio ordering. From these expressions we subsequently obtain exact expressions for the Bayes factors related to these hypotheses. Supplemental materials for running the analysis for the examples presented in the article are available online.