cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17096746.v2
title: Spatial Confounding in Generalized Estimating Equations

description: Spatial confounding, where the inclusion of a spatial random effect introduces multicollinearity with spatially structured covariates, is a contentious and active area of research in spatial statistics. However, the majority of research into this topic has focused on the case of spatial mixed models. In this article, we demonstrate that spatial confounding can also arise in the setting of generalized estimating equations (GEEs). The phenomenon occurs when a spatially structured working correlation matrix is used, as it effectively induces a spatial effect which may exhibit collinearity with the covariates in the marginal mean. As a result, the GEE ends up estimating a so-called unpartitioned effect of the covariates. To overcome spatial confounding, we propose a restricted spatial working correlation matrix that leads the GEE to instead estimate a partitioned covariate effect, which additionally captures the portion of spatial variability in the response spanned by the column space of the covariates. We also examine the construction of sandwich-based standard errors, showing that the issue of efficiency is tied to whether the working correlation matrix aligns with the target effect of interest. We conclude by highlighting the need for practitioners to make clear the assumptions and target of interest when applying GEEs in a spatial setting, and not simply rely on the robustness property of GEEs to misspecification of the working correlation matrix.
DOI: 10.6084/m9.figshare.13615799.v2
title: Bayes Factors Based on <i>p</i>-Values and Sets of Priors With Restricted Strength

description: This article focuses on the minimum Bayes factor compatible with a <i>p</i>-value, considering a set of priors with restricted strength. The resulting minimum Bayes factor depends on both the strength of the set of priors and the sample size. The results can be used to interpret the evidence for/against the hypothesis provided by a <i>p</i>-value in a way that accounts for the strength of the priors and the sample size. In particular, the results suggest further lowering the <i>p</i>-value cutoff for “statistical significance.”
DOI: 10.6084/m9.figshare.13615799
title: Bayes Factors Based on <i>p</i>-Values and Sets of Priors With Restricted Strength

description: This article focuses on the minimum Bayes factor compatible with a <i>p</i>-value, considering a set of priors with restricted strength. The resulting minimum Bayes factor depends on both the strength of the set of priors and the sample size. The results can be used to interpret the evidence for/against the hypothesis provided by a <i>p</i>-value in a way that accounts for the strength of the priors and the sample size. In particular, the results suggest further lowering the <i>p</i>-value cutoff for “statistical significance.”
DOI: 10.6084/m9.figshare.14885530
title: Incorporating Minimum Variances into Weighted Optimality Criteria

description: Weighted optimality criteria allow an experimenter to express hierarchical interest across estimable functions through a concise weighting system. We show how such criteria can be implicitly influenced by the estimable functions’ minimum variances, leading to nonintuitive variance properties of the optimal designs. To address this, we propose a new optimality and evaluation approach that incorporates these minimum variances. A modified <i>c</i>-optimality criterion is introduced to calculate an estimable function’s minimum variance while requiring estimability of all other functions of interest. These minimum variances are then incorporated into a standardized weighted <i>A</i>-criterion that has an intuitive weighting system. We argue that optimal designs under this criterion tend to satisfy the conditions of a new design property we call weight adherence that sets appropriate expectations for how a given weighting system will influence variance properties. A practical, exploratory approach is then described for weighted optimal design generation and evaluation. Examples of the exploratory approach and weight adherence are provided for two types of factorial experiments.
DOI: 10.6084/m9.figshare.16918364
title: Comparative Probability Metrics: Using Posterior Probabilities to Account for Practical Equivalence in A/B tests

description: Recently, online-controlled experiments (i.e., A/B tests) have become an extremely valuable tool used by internet and technology companies for purposes of advertising, product development, product improvement, customer acquisition, and customer retention to name a few. The data-driven decisions that result from these experiments have traditionally been informed by null hypothesis significance tests and analyses based on <i>p</i>-values. However, recently attention has been drawn to the shortcomings of hypothesis testing, and an emphasis has been placed on the development of new methodologies that overcome these shortcomings. We propose the use of posterior probabilities to facilitate comparisons that account for practical equivalence and that quantify the likelihood that a result is practically meaningful, as opposed to statistically significant. We call these posterior probabilities comparative probability metrics (CPMs). This Bayesian methodology provides a flexible and intuitive means of making meaningful comparisons by directly calculating, for example, the probability that two groups are practically equivalent, or the probability that one group is practically superior to another. In this article, we describe a unified framework for constructing and estimating such probabilities, and we develop a sample size determination methodology that may be used to determine how much data are required to calculate trustworthy CPMs.
DOI: 10.6084/m9.figshare.16918364.v1
title: Comparative Probability Metrics: Using Posterior Probabilities to Account for Practical Equivalence in A/B tests

description: Recently, online-controlled experiments (i.e., A/B tests) have become an extremely valuable tool used by internet and technology companies for purposes of advertising, product development, product improvement, customer acquisition, and customer retention to name a few. The data-driven decisions that result from these experiments have traditionally been informed by null hypothesis significance tests and analyses based on <i>p</i>-values. However, recently attention has been drawn to the shortcomings of hypothesis testing, and an emphasis has been placed on the development of new methodologies that overcome these shortcomings. We propose the use of posterior probabilities to facilitate comparisons that account for practical equivalence and that quantify the likelihood that a result is practically meaningful, as opposed to statistically significant. We call these posterior probabilities comparative probability metrics (CPMs). This Bayesian methodology provides a flexible and intuitive means of making meaningful comparisons by directly calculating, for example, the probability that two groups are practically equivalent, or the probability that one group is practically superior to another. In this article, we describe a unified framework for constructing and estimating such probabilities, and we develop a sample size determination methodology that may be used to determine how much data are required to calculate trustworthy CPMs.
DOI: 10.6084/m9.figshare.17096746.v1
title: Spatial Confounding in Generalized Estimating Equations

description: Spatial confounding, where the inclusion of a spatial random effect introduces multicollinearity with spatially structured covariates, is a contentious and active area of research in spatial statistics. However, the majority of research into this topic has focused on the case of spatial mixed models. In this article, we demonstrate that spatial confounding can also arise in the setting of generalized estimating equations (GEEs). The phenomenon occurs when a spatially structured working correlation matrix is used, as it effectively induces a spatial effect which may exhibit collinearity with the covariates in the marginal mean. As a result, the GEE ends up estimating a so-called unpartitioned effect of the covariates. To overcome spatial confounding, we propose a restricted spatial working correlation matrix that leads the GEE to instead estimate a partitioned covariate effect, which additionally captures the portion of spatial variability in the response spanned by the column space of the covariates. We also examine the construction of sandwich-based standard errors, showing that the issue of efficiency is tied to whether the working correlation matrix aligns with the target effect of interest. We conclude by highlighting the need for practitioners to make clear the assumptions and target of interest when applying GEEs in a spatial setting, and not simply rely on the robustness property of GEEs to misspecification of the working correlation matrix.
DOI: 10.6084/m9.figshare.15164921.v1
title: Evaluating real-time probabilistic forecasts with application to National Basketball Association outcome prediction

description: Motivated by the goal of evaluating real-time forecasts of home team win probabilities in the National Basketball Association, we develop new tools for measuring the quality of continuously updated probabilistic forecasts. This includes introducing calibration surface plots, and simple graphical summaries of them, to evaluate at a glance whether a given continuously updated probability forecasting method is well-calibrated, as well as developing statistical tests and graphical tools to evaluate the skill, or relative performance, of two competing continuously updated forecasting methods. These tools are demonstrated in an application to evaluate the continuously updated forecasts published by the United States-based multinational sports network ESPN on its principle webpage espn.com. This application lends statistical evidence that the forecasts published there are well-calibrated, and exhibit improved skill over several naïve models, but do not demonstrate significantly improved skill over simple logistic regression models based solely on a measurement of each teams’ relative strength, and the evolving score difference throughout the game.
DOI: 10.6084/m9.figshare.19175624.v2
title: Expressing Regret: A Unified View of Credible Intervals

description: Posterior uncertainty is typically summarized as a credible interval, an interval in the parameter space that contains a fixed proportion—usually 95%—of the posterior’s support. For multivariate parameters, credible sets perform the same role. There are of course many potential 95% intervals from which to choose, yet even standard choices are rarely justified in any formal way. In this article we give a general method, focusing on the loss function that motivates an estimate—the Bayes rule—around which we construct a credible set. The set contains all points which, as estimates, would have minimally-worse expected loss than the Bayes rule: we call this excess expected loss “regret.” The approach can be used for any model and prior, and we show how it justifies all widely used choices of credible interval/set. Further examples show how it provides insights into more complex estimation problems. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19175624.v1
title: Expressing regret: a unified view of credible intervals

description: Posterior uncertainty is typically summarized as a credible interval, an interval in the parameter space that contains a fixed proportion – usually 95% – of the posterior’s support. For multivariate parameters, credible sets perform the same role. There are of course many potential 95% intervals from which to choose, yet even standard choices are rarely justified in any formal way. In this paper we give a general method, focusing on the loss function that motivates an estimate – the Bayes rule – around which we construct a credible set. The set contains all points which, as estimates, would have minimally-worse expected loss than the Bayes rule: we call this excess expected loss ‘regret’. The approach can be used for any model and prior, and we show how it justifies all widely-used choices of credible interval/set. Further examples show how it provides insights into more complex estimation problems.
DOI: 10.14469/hpc/6714
title: CN(+) Quintet CASSCF(8,8, Nroot=1)/Def2QZVPP alter 8 10 5S 597 cm-1 G =-91.794996

description: Gaussian Calculation
DOI: 10.6084/m9.figshare.14885530.v1
title: Incorporating Minimum Variances into Weighted Optimality Criteria

description: Weighted optimality criteria allow an experimenter to express hierarchical interest across estimable functions through a concise weighting system. We show how such criteria can be implicitly influenced by the estimable functions’ minimum variances, leading to nonintuitive variance properties of the optimal designs. To address this, we propose a new optimality and evaluation approach that incorporates these minimum variances. A modified <i>c</i>-optimality criterion is introduced to calculate an estimable function’s minimum variance while requiring estimability of all other functions of interest. These minimum variances are then incorporated into a standardized weighted <i>A</i>-criterion that has an intuitive weighting system. We argue that optimal designs under this criterion tend to satisfy the conditions of a new design property we call weight adherence that sets appropriate expectations for how a given weighting system will influence variance properties. A practical, exploratory approach is then described for weighted optimal design generation and evaluation. Examples of the exploratory approach and weight adherence are provided for two types of factorial experiments.
DOI: 10.6084/m9.figshare.19175624
title: Expressing Regret: A Unified View of Credible Intervals

description: Posterior uncertainty is typically summarized as a credible interval, an interval in the parameter space that contains a fixed proportion—usually 95%—of the posterior’s support. For multivariate parameters, credible sets perform the same role. There are of course many potential 95% intervals from which to choose, yet even standard choices are rarely justified in any formal way. In this article we give a general method, focusing on the loss function that motivates an estimate—the Bayes rule—around which we construct a credible set. The set contains all points which, as estimates, would have minimally-worse expected loss than the Bayes rule: we call this excess expected loss “regret.” The approach can be used for any model and prior, and we show how it justifies all widely used choices of credible interval/set. Further examples show how it provides insights into more complex estimation problems. Supplementary materials for this article are available online.
DOI: 10.14469/hpc/6715
title: NN(2+) Quintet CASSCF(8,8, Nroot=1)/Def2QZVPP alter 8 10 5Su Dissociative

description: Gaussian Calculation
DOI: 10.14469/hpc/6716
title: CN(+) Quintet CASSCF(8,8, Nroot=3)/Def2QZVPP alter 7 9 4 9 5Sg 1371 cm-1 G = -91.737091

description: Gaussian Calculation
DOI: 10.14469/hpc/6717
title: BN Quintet CASSCF(8,8, Nroot=1)/Def2QZVPP alter 8 10 5Su 903 cm-1 G = -78.999200

description: Gaussian Calculation
DOI: 10.14469/hpc/6718
title: NN(2+) Quintet CASSCF(8,8, Nroot=3)/Def2QZVPP alter 7 9 4 9 1625 G = -107.352713

description: Gaussian Calculation
DOI: 10.14469/hpc/6720
title: BB(2-) Quintet CASSCF(8,8,nroot=1) 8 10 G = -49.006111 BB stretch 978 cm-1

description: Gaussian Calculation
DOI: 10.14469/hpc/6721
title: CB(-) Quintet CASSCF(8,8, Nroot=1)/Def2QZVPP alter 8 10 C=B stretch 758 cm-1 G = -62.362522

description: Gaussian Calculation
DOI: 10.14469/hpc/6722
title: CO(2+) Quintet CASSCF(8,8, Nroot=1)/Def2QZVPP alter 8 10 Dissociative

description: Gaussian Calculation
DOI: 10.14469/hpc/6724
title: BN CASSCF(8,8,root=2) alter 7 9 1.449 B=N stretch 1183 cm-1 (Natural orbitals)

description: Gaussian Calculation
DOI: 10.14469/hpc/6725
title: CO(2+) Quintet CASSCF(8,8, Nroot=2)/Def2QZVPP alter7 9 1.4424 C=O stretch 926 cm-1 Natural orbitals

description: Gaussian Calculation
DOI: 10.14469/hpc/7870
title: Dinesh, Huckel, OH migrating,IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7871
title: Dinesh, double Mobius, OH migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7872
title: Dinesh, double Mobius, NH2 migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7873
title: Dinesh, double Mobius, NH2 migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7874
title: Dinesh, double Mobius, F migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7875
title: Dinesh, Huckel, F migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7876
title: Dinesh, Huckel, NH2, migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7877
title: Dinesh, double Mobius, Me-inv, antara, TS, DG = 62.9

description: Gaussian Calculation
DOI: 10.14469/hpc/7878
title: Dinesh, double Mobius, Me-inv, antara, TS, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7880
title: Dinesh, single Mobius, F migrating, 2+, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7881
title: Dinesh,single Mobius, 1,7 + 1,11 F migrating, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7882
title: Dinesh, 1,9 antarafacial BH2 migrating group, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7883
title: Dinesh, 1,9, BH2 migrating, suprafacial (with inversion?), IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7884
title: Dinesh, Me-inv, antara, TS (biradical), IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/7885
title: Dinesh,single Mobius, F migrating, 1,11 TS

description: Gaussian Calculation
DOI: 10.14469/hpc/7886
title: Dinesh, Me-inv, antara, TS (biradical) G = -388.030248 DG = 35.5

description: Gaussian Calculation
DOI: 10.14469/hpc/7887
title: Dinesh, Me-inv, antara, Reactant, G = -388.086839

description: Gaussian Calculation
DOI: 10.14469/hpc/7888
title: Dinesh, 1,9 antarafacial BH2 migrating group

description: Gaussian Calculation
DOI: 10.6084/m9.figshare.20369340
title: Livro de códigos e teste de confiabilidade (2016-2022)

description: Confira acima o teste de confiabilidade e o livro de códigos sobre a qualidade no telejornalismo.
DOI: 10.6084/m9.figshare.20369340.v1
title: Livro de códigos e teste de confiabilidade (2016-2022)

description: Confira acima o teste de confiabilidade e o livro de códigos sobre a qualidade no telejornalismo.
DOI: 10.3886/icpsr38469.v1
title: Subsidized and Transitional Employment Demonstration (STED), Minnesota, 2014-2019
title: Version 1

description: Minnesota's Temporary Assistance for Needy Families (TANF) program, called the Minnesota Family Investment Program (MFIP), provides a full range of employment services to families receiving MFIP benefits, including job readiness workshops and job search assistance. However, in 2014, despite Minnesota's strong labor market, some MFIP recipients were unable to find employment. The state funded the Minnesota Subsidized and Transitional Employment Demonstration (MSTED) to improve the employment outcomes of MFIP recipients who were not able to find employment after receiving MFIP services for six months or more. MSTED placed participants into two different types of subsidized employment based on their job readiness - either in temporary paid work experience in the nonprofit and public sectors for participants who were less job-ready participants or in subsidized jobs in the private sector designed to roll over into permanent unsubsidized positions for the more job-ready participants. The evaluation of MSTED, conducted as a randomized controlled trial, included an implementation study, an impact study, and a cost analysis. These data are a Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped for release, but not checked or processed.
description: The purpose of this study was to evaluate the Minnesota Subsidized and Transitional Employment Demonstration (MSTED) program as part of the Subsidized and Transitional Employment Demonstration (STED) project.
description: The Minnesota Subsidized and Transitional Employment Demonstration (MSTED) placed participants into two different types of subsidized employment based on their job readiness - either in temporary paid work experience in the nonprofit and public sectors for participants who were less job-ready participants or in subsidized jobs in the private sector designed to roll over into permanent unsubsidized positions for the more job-ready participants. The evaluation of MSTED, conducted as a randomized controlled trial, included an implementation study, an impact study, and a cost analysis.
description: The study sample including 799 Temporary Assistance for Needy Families (TANF) recipients in Minnesota was randomly assigned to the program group (n=403) and the control group (n=396) between November 2014 and June 2016.
description: Datasets: DS1: Subsidized and Transitional Employment Demonstration (STED), Minnesota, 2014-2019
description: Minnesota Family Investment Program (MFIP) recipients in Hennepin, Ramsey, and Dakota Counties who were not able to find employment after receiving MFIP services for six months or more. Smallest Geographic Unit: County
description: on-site questionnaireFor additional information, users can visit the MDRC website.