cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12312242.v4
title: Brain Regions Identified as Being Associated With Verbal Reasoning Through the Use of Imaging Regression via Internal Variation

description: <i>Abstract–</i>Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of an individual’s general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called internal variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for total variation, which has been considered suitable to deal with similar problems but may not be scalable to high-order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Next, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including reorienting, bias-field correcting, extracting, normalizing, and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals’ verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechanisms for verbal reasoning. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12312242
title: Brain Regions Identified as Being Associated With Verbal Reasoning Through the Use of Imaging Regression via Internal Variation

description: <i>Abstract–</i>Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of an individual’s general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called internal variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for total variation, which has been considered suitable to deal with similar problems but may not be scalable to high-order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Next, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including reorienting, bias-field correcting, extracting, normalizing, and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals’ verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechanisms for verbal reasoning. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12312242.v3
title: Brain Regions Identified as Being Associated With Verbal Reasoning Through the Use of Imaging Regression via Internal Variation

description: <i>Abstract–</i>Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of an individual’s general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called internal variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for total variation, which has been considered suitable to deal with similar problems but may not be scalable to high-order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Next, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including reorienting, bias-field correcting, extracting, normalizing, and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals’ verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechanisms for verbal reasoning. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12292541
title: Optimal Individualized Decision Rules Using Instrumental Variable Methods

description: There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where it is feasible to intervene directly on treatment. We then consider a setting where intervening on treatment is infeasible, but intervening to encourage treatment is feasible. In both of these settings, we also handle the case that the treatment is a limited resource so that optimal interventions focus the available resources on those individuals who will benefit most from treatment. Given a reference rule, we evaluate an optimal individualized rule by its average causal effect relative to a prespecified reference rule. We develop methods to estimate optimal individualized rules and construct asymptotically efficient plug-in estimators of the corresponding average causal effect relative to a prespecified reference rule. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12292541.v3
title: Optimal Individualized Decision Rules Using Instrumental Variable Methods

description: There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where it is feasible to intervene directly on treatment. We then consider a setting where intervening on treatment is infeasible, but intervening to encourage treatment is feasible. In both of these settings, we also handle the case that the treatment is a limited resource so that optimal interventions focus the available resources on those individuals who will benefit most from treatment. Given a reference rule, we evaluate an optimal individualized rule by its average causal effect relative to a prespecified reference rule. We develop methods to estimate optimal individualized rules and construct asymptotically efficient plug-in estimators of the corresponding average causal effect relative to a prespecified reference rule. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11915016
title: Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data

description: Decisions regarding the supply of electricity across a power grid must take into consideration the inherent uncertainty in demand. Optimal decision-making requires probabilistic forecasts for demand in a hierarchy with various levels of aggregation, such as substations, cities, and regions. The forecasts should be coherent in the sense that the forecast of the aggregated series should equal the sum of the forecasts of the corresponding disaggregated series. Coherency is essential, since the allocation of electricity at one level of the hierarchy relies on the appropriate amount being provided from the previous level. We introduce a new probabilistic forecasting method for a large hierarchy based on UK residential smart meter data. We find our method provides coherent and accurate probabilistic forecasts, as a result of an effective forecast combination. Furthermore, by avoiding distributional assumptions, we find that our method captures the variety of distributions in the smart meter hierarchy. Finally, the results confirm that, to ensure coherency in our large-scale hierarchy, it is sufficient to model a set of lower-dimension dependencies, rather than modeling the entire joint distribution of all series in the hierarchy. In achieving coherent and accurate hierarchical probabilistic forecasts, this work contributes to improved decision-making for smart grids. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.11915016.v3
title: Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data

description: Decisions regarding the supply of electricity across a power grid must take into consideration the inherent uncertainty in demand. Optimal decision-making requires probabilistic forecasts for demand in a hierarchy with various levels of aggregation, such as substations, cities, and regions. The forecasts should be coherent in the sense that the forecast of the aggregated series should equal the sum of the forecasts of the corresponding disaggregated series. Coherency is essential, since the allocation of electricity at one level of the hierarchy relies on the appropriate amount being provided from the previous level. We introduce a new probabilistic forecasting method for a large hierarchy based on UK residential smart meter data. We find our method provides coherent and accurate probabilistic forecasts, as a result of an effective forecast combination. Furthermore, by avoiding distributional assumptions, we find that our method captures the variety of distributions in the smart meter hierarchy. Finally, the results confirm that, to ensure coherency in our large-scale hierarchy, it is sufficient to model a set of lower-dimension dependencies, rather than modeling the entire joint distribution of all series in the hierarchy. In achieving coherent and accurate hierarchical probabilistic forecasts, this work contributes to improved decision-making for smart grids. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.11915016.v2
title: Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data

description: Decisions regarding the supply of electricity across a power grid must take into consideration the inherent uncertainty in demand. Optimal decision-making requires probabilistic forecasts for demand in a hierarchy with various levels of aggregation, such as substations, cities, and regions. The forecasts should be coherent in the sense that the forecast of the aggregated series should equal the sum of the forecasts of the corresponding disaggregated series. Coherency is essential, since the allocation of electricity at one level of the hierarchy relies on the appropriate amount being provided from the previous level. We introduce a new probabilistic forecasting method for a large hierarchy based on UK residential smart meter data. We find our method provides coherent and accurate probabilistic forecasts, as a result of an effective forecast combination. Furthermore, by avoiding distributional assumptions, we find that our method captures the variety of distributions in the smart meter hierarchy. Finally, the results confirm that, to ensure coherency in our large-scale hierarchy, it is sufficient to model a set of lower-dimension dependencies, rather than modeling the entire joint distribution of all series in the hierarchy. In achieving coherent and accurate hierarchical probabilistic forecasts, this work contributes to improved decision-making for smart grids. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12254951
title: Incorporating Animal Movement Into Distance Sampling

description: Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (&lt;5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999–2006) of spotted dolphins (<i>Stenella attenuata</i>) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveys. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12254951.v4
title: Incorporating Animal Movement Into Distance Sampling

description: Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (&lt;5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999–2006) of spotted dolphins (<i>Stenella attenuata</i>) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveys. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.13056143
title: Learning When-to-Treat Policies

description: Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an “advantage doubly robust” estimator for learning such dynamic treatment rules using observational data under the assumption of sequential ignorability. We prove welfare regret bounds that generalize results for doubly robust learning in the single-step setting, and show promising empirical performance in several different contexts. Our approach is practical for policy optimization, and does not need any structural (e.g., Markovian) assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13056143.v3
title: Learning When-to-Treat Policies

description: Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an “advantage doubly robust” estimator for learning such dynamic treatment rules using observational data under the assumption of sequential ignorability. We prove welfare regret bounds that generalize results for doubly robust learning in the single-step setting, and show promising empirical performance in several different contexts. Our approach is practical for policy optimization, and does not need any structural (e.g., Markovian) assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12118581.v4
title: Robust Q-Learning

description: <i>Abstract–</i>Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the “Extending Treatment Effectiveness of Naltrexone” multistage randomized trial to illustrate our proposed methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12118581
title: Robust Q-Learning

description: <i>Abstract–</i>Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the “Extending Treatment Effectiveness of Naltrexone” multistage randomized trial to illustrate our proposed methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12254951.v3
title: Incorporating Animal Movement Into Distance Sampling

description: Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (&lt;5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999–2006) of spotted dolphins (<i>Stenella attenuata</i>) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveys. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.