cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17137009
title: Toward Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia

description: How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates <i>X</i> on a real-valued response <i>Y</i>. We introduce a procedure for estimating causal effects and a nonparametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (<i>P</i> = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, <i>P</i> = 0.047) and negative effects (e.g., Magdalena, <i>P</i> = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online.
DOI: 10.6084/m9.figshare.17137009.v2
title: Toward Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia

description: How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates <i>X</i> on a real-valued response <i>Y</i>. We introduce a procedure for estimating causal effects and a nonparametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (<i>P</i> = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, <i>P</i> = 0.047) and negative effects (e.g., Magdalena, <i>P</i> = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online.
DOI: 10.6084/m9.figshare.12984285.v1
title: Estimating Number of Factors by Adjusted Eigenvalues Thresholding

description: Determining the number of common factors is an important and practical topic in high-dimensional factor models. The existing literature is mainly based on the eigenvalues of the covariance matrix. Owing to the incomparability of the eigenvalues of the covariance matrix caused by the heterogeneous scales of the observed variables, it is not easy to find an accurate relationship between these eigenvalues and the number of common factors. To overcome this limitation, we appeal to the correlation matrix and demonstrate, surprisingly, that the number of eigenvalues greater than 1 of the population correlation matrix is the same as the number of common factors under certain mild conditions. To utilize such a relationship, we study random matrix theory based on the sample correlation matrix in order to correct biases in estimating the top eigenvalues and to take into account of estimation errors in eigenvalue estimation. Thus, we propose a tuning-free scale-invariant adjusted correlation thresholding (ACT) method for determining the number of common factors in high-dimensional factor models, taking into account the sampling variabilities and biases of top sample eigenvalues. We also establish the optimality of the proposed ACT method in terms of minimal signal strength and the optimal threshold. Simulation studies lend further support to our proposed method and show that our estimator outperforms competing methods in most test cases.
DOI: 10.6084/m9.figshare.13056173
title: A Minimax Optimal Ridge-Type Set Test for Global Hypothesis With Applications in Whole Genome Sequencing Association Studies

description: Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling’s <i>T</i> <sup>2</sup> test, the <i>F</i>-test, and the empirical Bayes based score test. These classical tests, however, are not robust to the signal strength and could have a substantial loss of power when signals are weak or moderate, a situation we commonly encounter in contemporary applications. In this article, we propose a minimax optimal ridge-type set test (MORST), a simple and generic method for testing a global hypothesis. The power of MORST is robust and considerably higher than that of the classical tests when the strength of signals is weak or moderate. In the meantime, MORST only requires a slight increase in computation compared to these existing tests, making it applicable to the analysis of massive genome-wide data. We also provide the generalizations of MORST that are parallel to the traditional Wald test and Rao’s score test in asymptotic settings. Extensive simulations demonstrated the robust power of MORST and that the Type I error of MORST was well controlled. We applied MORST to the analysis of the whole-genome sequencing data from the Atherosclerosis Risk in Communities study, where MORST detected 20%–250% more signal regions than the classical tests. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13056173.v2
title: A Minimax Optimal Ridge-Type Set Test for Global Hypothesis With Applications in Whole Genome Sequencing Association Studies

description: Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling’s <i>T</i> <sup>2</sup> test, the <i>F</i>-test, and the empirical Bayes based score test. These classical tests, however, are not robust to the signal strength and could have a substantial loss of power when signals are weak or moderate, a situation we commonly encounter in contemporary applications. In this article, we propose a minimax optimal ridge-type set test (MORST), a simple and generic method for testing a global hypothesis. The power of MORST is robust and considerably higher than that of the classical tests when the strength of signals is weak or moderate. In the meantime, MORST only requires a slight increase in computation compared to these existing tests, making it applicable to the analysis of massive genome-wide data. We also provide the generalizations of MORST that are parallel to the traditional Wald test and Rao’s score test in asymptotic settings. Extensive simulations demonstrated the robust power of MORST and that the Type I error of MORST was well controlled. We applied MORST to the analysis of the whole-genome sequencing data from the Atherosclerosis Risk in Communities study, where MORST detected 20%–250% more signal regions than the classical tests. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16595972
title: Optimizing the JSM Program

description: Sometimes the Joint Statistical Meetings (JSM) is frustrating to attend, because multiple sessions on the same topic are scheduled at the same time. This article uses seeded latent Dirichlet allocation and a scheduling optimization algorithm to very significantly reduce overlapping content in the original schedule for the 2020 JSM program. Specifically, a measure based on total variation distance that ranges from 0 (random scheduling) to 1 (no overlapping content) finds that the original schedule had a score of 0.058, whereas our proposed schedule achieved a score of 0.371. This is a huge improvement that would (i) increase participant satisfaction as measured by the post-JSM satisfaction survey, and (ii) save the American Statistical Association significant money by obviating the need for the traditional in-person meeting of the 47 program chairs and other organizers. The methodology developed in this work immediately applies to future JSMs and is easily modified to improve scheduling for any other scientific conference that has parallel sessions.
DOI: 10.6084/m9.figshare.12951696
title: Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies

description: We consider in this article detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a prespecified region, for example, a gene. In view of massive intergenic regions in whole genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to detect the existence and the locations of signal regions by scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both causal and neutral variants, and the effects of signal variants to be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an empirical threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions. We perform simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results show that the proposed procedure outperforms the existing methods, especially when signal regions have causal variants whose effects are in different directions, or are contaminated with neutral variants. We illustrate Q-SCAN by analyzing the WGS data from the Atherosclerosis Risk in Communities study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13056173.v1
title: A Minimax Optimal Ridge-Type Set Test for Global Hypothesis with Applications in Whole Genome Sequencing Association Studies

description: Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling’s <i>T</i><sup>2</sup> test, the F test, and the empirical Bayes based score test. These classical tests, however, are not robust to the signal strength and could have a substantial loss of power when signals are weak or moderate, a situation we commonly encounter in contemporary applications. In this paper, we propose a Minimax Optimal Ridge-type Set Test (MORST), a simple and generic method for testing a global hypothesis. The power of MORST is robust and considerably higher than that of the classical tests when the strength of signals is weak or moderate. In the meantime, MORST only requires a slight increase in computation compared to these existing tests, making it applicable to the analysis of massive genome-wide data. We also provide the generalizations of MORST that are parallel to the traditional Wald test and Rao’s score test in asymptotic settings. Extensive simulations demonstrated the robust power of MORST and that the type I error of MORST was well controlled. We applied MORST to the analysis of the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, where MORST detected 20%–250% more signal regions than the classical tests.
DOI: 10.6084/m9.figshare.16595972.v2
title: Optimizing the JSM Program

description: Sometimes the Joint Statistical Meetings (JSM) is frustrating to attend, because multiple sessions on the same topic are scheduled at the same time. This article uses seeded latent Dirichlet allocation and a scheduling optimization algorithm to very significantly reduce overlapping content in the original schedule for the 2020 JSM program. Specifically, a measure based on total variation distance that ranges from 0 (random scheduling) to 1 (no overlapping content) finds that the original schedule had a score of 0.058, whereas our proposed schedule achieved a score of 0.371. This is a huge improvement that would (i) increase participant satisfaction as measured by the post-JSM satisfaction survey, and (ii) save the American Statistical Association significant money by obviating the need for the traditional in-person meeting of the 47 program chairs and other organizers. The methodology developed in this work immediately applies to future JSMs and is easily modified to improve scheduling for any other scientific conference that has parallel sessions.
DOI: 10.6084/m9.figshare.12951696.v1
title: Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies

description: We consider in this article detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a prespecified region, for example, a gene. In view of massive intergenic regions in whole genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to detect the existence and the locations of signal regions by scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both causal and neutral variants, and the effects of signal variants to be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an empirical threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions. We perform simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results show that the proposed procedure outperforms the existing methods, especially when signal regions have causal variants whose effects are in different directions, or are contaminated with neutral variants. We illustrate Q-SCAN by analyzing the WGS data from the Atherosclerosis Risk in Communities study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17137009.v1
title: Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia

description: How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates <i>X</i> on a real-valued response <i>Y</i>. We introduce a procedure for estimating causal effects and a non-parametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (<i>P</i> = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, <i>P</i> = 0.047) and negative effects (e.g., Magdalena, <i>P</i> = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online.
DOI: 10.6084/m9.figshare.13056170
title: Causal Bounds for Outcome-Dependent Sampling in Observational Studies

description: Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs often suffer from unmeasured confounding, which generally precludes the nonparametric identification of causal effects. Nonparametric bounds can provide a way to narrow the range of possible values for a nonidentifiable causal effect without making additional untestable assumptions. The nonparametric bounds literature has almost exclusively focused on settings with random sampling, and the bounds have often been derived with a particular linear programming method. We derive novel bounds for the causal risk difference, often referred to as the average treatment effect, in six settings with outcome-dependent sampling and unmeasured confounding for a binary outcome and exposure. Our derivations of the bounds illustrate two approaches that may be applicable in other settings where the bounding problem cannot be directly stated as a system of linear constraints. We illustrate our derived bounds in a real data example involving the effect of vitamin D concentration on mortality. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16595972.v1
title: Optimizing the JSM Program

description: Sometimes the Joint Statistical Meetings (JSM) is frustrating to attend, because multiple sessions on the same topic are scheduled at the same time. This paper uses seeded Latent Dirichlet Allocation and a scheduling optimization algorithm to very significantly reduce overlapping content in the original schedule for the 2020 JSM program. Specifically, a measure based on total variation distance that ranges from 0 (random scheduling) to 1 (no overlapping content) finds that the original schedule had a score of 0.058, whereas our proposed schedule achieved a score of 0.371. This is a huge improvement that would (1) increase participant satisfaction as measured by the post-JSM satisfaction survey, and (2) save the American Statistical Association significant money by obviating the need for the traditional in-person meeting of the 47 program chairs and other organizers. The methodology developed in this work immediately applies to future JSMs and is easily modified to improve scheduling for any other scientific conference that has parallel sessions.
DOI: 10.6084/m9.figshare.13056170.v2
title: Causal Bounds for Outcome-Dependent Sampling in Observational Studies

description: Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs often suffer from unmeasured confounding, which generally precludes the nonparametric identification of causal effects. Nonparametric bounds can provide a way to narrow the range of possible values for a nonidentifiable causal effect without making additional untestable assumptions. The nonparametric bounds literature has almost exclusively focused on settings with random sampling, and the bounds have often been derived with a particular linear programming method. We derive novel bounds for the causal risk difference, often referred to as the average treatment effect, in six settings with outcome-dependent sampling and unmeasured confounding for a binary outcome and exposure. Our derivations of the bounds illustrate two approaches that may be applicable in other settings where the bounding problem cannot be directly stated as a system of linear constraints. We illustrate our derived bounds in a real data example involving the effect of vitamin D concentration on mortality. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13056170.v1
title: Causal bounds for outcome-dependent sampling in observational studies

description: Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs often suffer from unmeasured confounding, which generally precludes the nonparametric identification of causal effects. Nonparametric bounds can provide a way to narrow the range of possible values for a nonidentifiable causal effect without making additional untestable assumptions. The nonparametric bounds literature has almost exclusively focused on settings with random sampling, and the bounds have often been derived with a particular linear programming method. We derive novel bounds for the causal risk difference, often referred to as the average treatment effect, in six settings with outcome-dependent sampling and unmeasured confounding for a binary outcome and exposure. Our derivations of the bounds illustrate two approaches that may be applicable in other settings where the bounding problem cannot be directly stated as a system of linear constraints. We illustrate our derived bounds in a real data example involving the effect of vitamin D concentration on mortality.
DOI: 10.6084/m9.figshare.5734479
title: Relationship between carotid intima-media thickness, physical activity, sleep quality, metabolic/inflamatory profile, body fatness, smoking and alcohol consumption in young adults

description: Abstract AIM The aim of this longitudinal study was to analyze the relationship between sleep disorder and intima-media thickness. METHOD Baseline measurements included carotid intima-media thickness, assessed by an ultrasound device; questionnaires about sleep and other behavioral variables; physical activity was measured by pedometer; body fatness was estimated by Dual-Energy X-ray Absorptiometry; fasting glucose, lipid profile and C-reactive protein were collected. RESULTS The occurrence rate of sleep-related disorders was 47% (95%CI= 37.2%-56.7%). Carotid intima-media thickness was related to symptoms of insomnia (r= 0.328 [0.141 to 0.493]) and, after adjustments for potential confounders, the relationship between carotid intima-media thickness and insomnia remained statistically significant (β= 0.121 [95%CI= 0.017; 0.225]). CONCLUSIONS In young adults, sleep disorder was significantly related to premature increase in carotid intima-media thickness.
DOI: 10.6084/m9.figshare.5734479.v1
title: Relationship between carotid intima-media thickness, physical activity, sleep quality, metabolic/inflamatory profile, body fatness, smoking and alcohol consumption in young adults

description: Abstract AIM The aim of this longitudinal study was to analyze the relationship between sleep disorder and intima-media thickness. METHOD Baseline measurements included carotid intima-media thickness, assessed by an ultrasound device; questionnaires about sleep and other behavioral variables; physical activity was measured by pedometer; body fatness was estimated by Dual-Energy X-ray Absorptiometry; fasting glucose, lipid profile and C-reactive protein were collected. RESULTS The occurrence rate of sleep-related disorders was 47% (95%CI= 37.2%-56.7%). Carotid intima-media thickness was related to symptoms of insomnia (r= 0.328 [0.141 to 0.493]) and, after adjustments for potential confounders, the relationship between carotid intima-media thickness and insomnia remained statistically significant (β= 0.121 [95%CI= 0.017; 0.225]). CONCLUSIONS In young adults, sleep disorder was significantly related to premature increase in carotid intima-media thickness.