cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7433192.v4
title: Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data

description: In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7063802.v3
title: Efficient Signal Inclusion With Genomic Applications

description: This article addresses the challenge of efficiently capturing a high proportion of true signals for subsequent data analyses when sample sizes are relatively limited with respect to data dimension. We propose the signal missing rate (SMR) as a new measure for false-negative control to account for the variability of false-negative proportion. Novel data-adaptive procedures are developed to control SMR without incurring many unnecessary false positives under dependence. We justify the efficiency and adaptivity of the proposed methods via theory and simulation. The proposed methods are applied to GWAS on human height to effectively remove irrelevant single nucleotide polymorphisms (SNPs) while retaining a high proportion of relevant SNPs for subsequent polygenic analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7075109
title: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases

description: The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the “terminal case,” in which the model cannot reproduce observations to within model discrepancy, and for which standard calibration methods in UQ fail to give sensible results. We show that even when there is no such issue with the model, the standard decomposition on the outputs can and usually does lead to a terminal case analysis. We present a simple test to allow a practitioner to establish whether their experiment will result in a terminal case analysis, and a methodology for defining calibration-optimal bases that avoid this whenever it is not inevitable. We present the optimal rotation algorithm for doing this, and demonstrate its efficacy for an idealized example for which the usual principal component methods fail. We apply these ideas to the CanAM4 model to demonstrate the terminal case issue arising for climate models. We discuss climate model tuning and the estimation of model discrepancy within this context, and show how the optimal rotation algorithm can be used in developing practical climate model tuning tools. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7433192.v3
title: Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data

description: In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7063802.v2
title: Efficient Signal Inclusion With Genomic Applications

description: This article addresses the challenge of efficiently capturing a high proportion of true signals for subsequent data analyses when sample sizes are relatively limited with respect to data dimension. We propose the signal missing rate (SMR) as a new measure for false-negative control to account for the variability of false-negative proportion. Novel data-adaptive procedures are developed to control SMR without incurring many unnecessary false positives under dependence. We justify the efficiency and adaptivity of the proposed methods via theory and simulation. The proposed methods are applied to GWAS on human height to effectively remove irrelevant single nucleotide polymorphisms (SNPs) while retaining a high proportion of relevant SNPs for subsequent polygenic analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7075109.v5
title: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases

description: The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the “terminal case,” in which the model cannot reproduce observations to within model discrepancy, and for which standard calibration methods in UQ fail to give sensible results. We show that even when there is no such issue with the model, the standard decomposition on the outputs can and usually does lead to a terminal case analysis. We present a simple test to allow a practitioner to establish whether their experiment will result in a terminal case analysis, and a methodology for defining calibration-optimal bases that avoid this whenever it is not inevitable. We present the optimal rotation algorithm for doing this, and demonstrate its efficacy for an idealized example for which the usual principal component methods fail. We apply these ideas to the CanAM4 model to demonstrate the terminal case issue arising for climate models. We discuss climate model tuning and the estimation of model discrepancy within this context, and show how the optimal rotation algorithm can be used in developing practical climate model tuning tools. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7075109.v4
title: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases

description: The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the “terminal case,” in which the model cannot reproduce observations to within model discrepancy, and for which standard calibration methods in UQ fail to give sensible results. We show that even when there is no such issue with the model, the standard decomposition on the outputs can and usually does lead to a terminal case analysis. We present a simple test to allow a practitioner to establish whether their experiment will result in a terminal case analysis, and a methodology for defining calibration-optimal bases that avoid this whenever it is not inevitable. We present the optimal rotation algorithm for doing this, and demonstrate its efficacy for an idealized example for which the usual principal component methods fail. We apply these ideas to the CanAM4 model to demonstrate the terminal case issue arising for climate models. We discuss climate model tuning and the estimation of model discrepancy within this context, and show how the optimal rotation algorithm can be used in developing practical climate model tuning tools. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7056404
title: Covariate Information Matrix for Sufficient Dimension Reduction

description: Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regression function. CIM is implemented via eigen-decomposition of a matrix estimated with a previously developed efficient nonparametric density estimation technique. We also propose a bootstrap-based diagnostic plot for estimating the dimension of the CS. Results of simulations and real data applications demonstrate superior or competitive performance of CIM compared to that of some other SDR methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7056404.v3
title: Covariate Information Matrix for Sufficient Dimension Reduction

description: Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regression function. CIM is implemented via eigen-decomposition of a matrix estimated with a previously developed efficient nonparametric density estimation technique. We also propose a bootstrap-based diagnostic plot for estimating the dimension of the CS. Results of simulations and real data applications demonstrate superior or competitive performance of CIM compared to that of some other SDR methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7056404.v2
title: Covariate Information Matrix for Sufficient Dimension Reduction

description: Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regression function. CIM is implemented via eigen-decomposition of a matrix estimated with a previously developed efficient nonparametric density estimation technique. We also propose a bootstrap-based diagnostic plot for estimating the dimension of the CS. Results of simulations and real data applications demonstrate superior or competitive performance of CIM compared to that of some other SDR methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7756817
title: Downstream Effects of Upstream Causes

description: The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States’ most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal <i>g</i>-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll <i>a</i>—a proxy for algal production. Publicly available data from North Carolina’s Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll <i>a</i> levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric <i>g</i>-formula, a marginal structural model, and a structural nested model indicate that chlorophyll <i>a</i> concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. 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.7756817.v3
title: Downstream Effects of Upstream Causes

description: The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States’ most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal <i>g</i>-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll <i>a</i>—a proxy for algal production. Publicly available data from North Carolina’s Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll <i>a</i> levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric <i>g</i>-formula, a marginal structural model, and a structural nested model indicate that chlorophyll <i>a</i> concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. 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.8059646
title: Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

description: Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. 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.7756817.v2
title: Downstream Effects of Upstream Causes

description: The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States’ most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal <i>g</i>-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll <i>a</i>—a proxy for algal production. Publicly available data from North Carolina’s Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll <i>a</i> levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric <i>g</i>-formula, a marginal structural model, and a structural nested model indicate that chlorophyll <i>a</i> concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. 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.8059646.v3
title: Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

description: Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. 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.8059646.v2
title: Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

description: Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.15468/dl.jbcweq
title: Occurrence Download

description: A dataset containing 124918 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON(((5.598907470703125 43.481822852999905,5.511016845703125 43.509717009288075,5.400552749633789 43.51127322324609,5.412139892578125 43.54257572246922,5.4547119140625 43.563476493226595,5.480804443359375 43.61122246320032,5.483551025390625 43.6499881760459,5.503549575805664 43.66644361490711,5.598907470703125 43.66985832954972,5.69091796875 43.70362249839005,5.763702392578124 43.661911057260674,5.79254150390625 43.625141236940564,5.804901123046875 43.601278509333646,5.799407958984375 43.5734266934657,5.76507568359375 43.52565074189047,5.71563720703125 43.52166770320007,5.598907470703125 43.481822852999905)))"
 ]
}

 The dataset includes 124918 records from 383 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0272198-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rp7vrg
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "Issue is Geodetic datum assumed WGS84",
 "Issue is Coordinate reprojection suspicious"
 ]
 },
 "OccurrenceStatus is Present",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059462-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yvsyu5
title: Occurrence Download

description: A dataset containing 14550 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulmus laevis Pall.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 14550 records from 232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0299261-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a8n2wt
title: Occurrence Download

description: A dataset containing 4494 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Disperis Sw."
 ]
}

 The dataset includes 4494 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0248776-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.