cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7393229.v1
title: Fast and Accurate Approximation of the Full Conditional for Gamma Shape Parameters

description: The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt with by using either Metropolis–Hastings moves, rejection sampling methods, or numerical integration. However, in models with a large number of shape parameters, these existing methods are slower or more complicated than one would like, making them burdensome in practice. It turns out that the full conditional distribution of the gamma shape parameter is well approximated by a gamma distribution, even for small sample sizes, when the prior on the shape parameter is also a gamma distribution. This article introduces a quick and easy algorithm for finding a gamma distribution that approximates the full conditional distribution of the shape parameter. We empirically demonstrate the speed and accuracy of the approximation across a wide range of conditions. If exactness is required, the approximation can be used as a proposal distribution for Metropolis–Hastings. Supplementary material for this article is available online.
DOI: 10.7284/120113
title: Singlebeam Echosounder data collected during research cruise OC1409B using a Knudsen 3260 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393217
title: Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

description: We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging data collected over the U.S. Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7393304.v3
title: Global 'water table ratio' and 'groundwater response time' raster data

description: Data supporting the publication:<br>Cuthbert et al (2019) "Global patterns and dynamics of climate-groundwater interactions" Nature Climate Change https://www.nature.com/articles/s41558-018-0386-4<br><br>See the ReadMe file uploaded with the data and the Methods section of the paper for details of the derivation of each dataset.<br>
DOI: 10.7284/120120
title: Speedlog data collected during research cruise OC1409B using a Furuno DS-50 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393223.v3
title: Intrinsic Data Depth for Hermitian Positive Definite Matrices

description: Nondegenerate covariance, correlation, and spectral density matrices are necessarily symmetric or Hermitian and positive definite. This article develops statistical data depths for collections of Hermitian positive definite matrices by exploiting the geometric structure of the space as a Riemannian manifold. The depth functions allow one to naturally characterize most central or outlying matrices, but also provide a practical framework for inference in the context of samples of positive definite matrices. First, the desired properties of an intrinsic data depth function acting on the space of Hermitian positive definite matrices are presented. Second, we propose two pointwise and integrated data depth functions that satisfy each of these requirements and investigate several robustness and efficiency aspects. As an application, we construct depth-based confidence regions for the intrinsic mean of a sample of positive definite matrices, which is applied to the exploratory analysis of a collection of covariance matrices in a multicenter clinical trial. Supplementary materials and an accompanying R-package are available online.
DOI: 10.6084/m9.figshare.7393304.v4
title: Global 'water table ratio' and 'groundwater response time' raster data

description: Data supporting the publication:<br>Cuthbert et al (2019) "Global patterns and dynamics of climate-groundwater interactions" Nature Climate Change https://www.nature.com/articles/s41558-018-0386-4<br><br>See the ReadMe file uploaded with the data and the Methods section of the paper for details of the derivation of each dataset.<br>
DOI: 10.7284/120121
title: Water Temperature data collected during research cruise OC1409B using a Sea-Bird SBE-38 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393148
title: Classical Backfitting for Smooth-Backfitting Additive Models

description: Smooth backfitting has been shown to have better theoretical properties than classical backfitting for fitting additive models based on local linear regression. In this article, we show that the smooth backfitting procedure in the local linear case can be alternatively performed as a classical backfitting procedure with a different type of smoother matrices. These smoother matrices are symmetric and shrinking and some established results in the literature are readily applicable. The connections allow the smooth backfitting algorithm to be implemented in a much simplified way, give new insights on the differences between the two approaches in the literature, and provide an extension to local polynomial regression. The connections also give rise to a new estimator at data points. Asymptotic properties of general local polynomial smooth backfitting estimates are investigated, allowing for different orders of local polynomials and different bandwidths. Cases of oracle efficiency are discussed. Computer-generated simulations are conducted to demonstrate finite sample behaviors of the methodology and a real data example is given for illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7393175
title: Identification of log-units in clay-rock formations based on local and spatial statistics of well-log properties: application to the Opalinus claystone in the Benken borehole

description: Spreadsheet summary with the determination of the clustering parameters for a kernel size of 3 m
DOI: 10.7284/120122
title: Transmissometer data collected during research cruise OC1409B using a WET Labs C-Star instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393175.v1
title: Identification of log-units in clay-rock formations based on local and spatial statistics of well-log properties: application to the Opalinus claystone in the Benken borehole

description: Spreadsheet summary with the determination of the clustering parameters for a kernel size of 3 m
DOI: 10.6084/m9.figshare.7393217.v1
title: Efficient Algorithms for Bayesian Nearest-Neighbor Gaussian Processes

description: We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU.
DOI: 10.6084/m9.figshare.7393145
title: The Offset Normal Shape Distribution for Dynamic Shape Analysis

description: This article deals with the statistical analysis of landmark data observed at different temporal instants. Statistical analysis of dynamic shapes is a problem with significant challenges due to the difficulty in providing a description of the shape changes over time, across subjects and over groups of subjects. There are several modeling strategies, which can be used for dynamic shape analysis. Here, we use the exact distribution theory for the shape of planar correlated Gaussian configurations and derive the induced <i>offset-normal</i> shape distribution. Various properties of this distribution are investigated, and some special cases discussed. This work is a natural progression of what has been proposed in Mardia and Dryden, Dryden and Mardia, Mardia and Walder, and Kume and Welling. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7393148.v1
title: Classical Backfitting for Smooth-backfitting Additive Models

description: Smooth backfitting has been shown to have better theoretical properties than classical backfitting for fitting additive models based on local linear regression (Mammen et al., 1999). In this paper we show that the smooth backfitting procedure in the local linear case can be alternatively performed as a classical backfitting procedure with a different type of smoother matrices. These smoother matrices first given in Huang and Chen (2008) are symmetric and shrinking and some established results in Buja et al. (1989) are readily applicable. The connections allow the smooth backfitting algorithm to be implemented in a much simplified way, give new insights on the differences between the two approaches in the literature, and provide an extension to local polynomial regression. The connections also give rise to a new estimator at data points. Asymptotic properties of general local polynomial smooth backfitting estimates are investigated, allowing for different orders of local polynomials and different bandwidths. Cases of oracle efficiency are discussed. Computer-generated simulations are conducted to demonstrate finite sample behaviors of the methodology and a real data example is given for illustration. Supplemental materials for the article are available online.
DOI: 10.6084/m9.figshare.7393217.v3
title: Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

description: We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging data collected over the U.S. Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU. Supplemental materials for this article are available online.
DOI: 10.7284/120123
title: TSG (Thermosalinograph) data collected during research cruise OC1409B using a Sea-Bird SBE-45 instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393148.v3
title: Classical Backfitting for Smooth-Backfitting Additive Models

description: Smooth backfitting has been shown to have better theoretical properties than classical backfitting for fitting additive models based on local linear regression. In this article, we show that the smooth backfitting procedure in the local linear case can be alternatively performed as a classical backfitting procedure with a different type of smoother matrices. These smoother matrices are symmetric and shrinking and some established results in the literature are readily applicable. The connections allow the smooth backfitting algorithm to be implemented in a much simplified way, give new insights on the differences between the two approaches in the literature, and provide an extension to local polynomial regression. The connections also give rise to a new estimator at data points. Asymptotic properties of general local polynomial smooth backfitting estimates are investigated, allowing for different orders of local polynomials and different bandwidths. Cases of oracle efficiency are discussed. Computer-generated simulations are conducted to demonstrate finite sample behaviors of the methodology and a real data example is given for illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7393217.v2
title: Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

description: We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging data collected over the U.S. Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7393142
title: Robust and Accurate Inference via a Mixture of Gaussian and Student’s <i>t</i> Errors

description: A Gaussian measurement error assumption, that is, an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student’s <i>t</i> distribution helps reduce the bias. However, it may be less efficient in estimating parameters if the heavy tailed assumption is uniformly applied to all of the data when most of them are normally observed. We propose a mixture error assumption that selectively converts Gaussian errors into Student’s <i>t</i> errors according to latent outlier indicators, leveraging the best of the Gaussian and Student’s <i>t</i> errors; a parameter estimation can be not only robust but also accurate. Using simulated hospital profiling data and astronomical time series of brightness data, we demonstrate the potential for the proposed mixture error assumption to estimate parameters accurately in the presence of outliers. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7393148.v2
title: Classical Backfitting for Smooth-Backfitting Additive Models

description: Smooth backfitting has been shown to have better theoretical properties than classical backfitting for fitting additive models based on local linear regression. In this article, we show that the smooth backfitting procedure in the local linear case can be alternatively performed as a classical backfitting procedure with a different type of smoother matrices. These smoother matrices are symmetric and shrinking and some established results in the literature are readily applicable. The connections allow the smooth backfitting algorithm to be implemented in a much simplified way, give new insights on the differences between the two approaches in the literature, and provide an extension to local polynomial regression. The connections also give rise to a new estimator at data points. Asymptotic properties of general local polynomial smooth backfitting estimates are investigated, allowing for different orders of local polynomials and different bandwidths. Cases of oracle efficiency are discussed. Computer-generated simulations are conducted to demonstrate finite sample behaviors of the methodology and a real data example is given for illustration. Supplementary materials for this article are available online.
DOI: 10.7284/120140
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise OC1409C using a Hawaii UHDAS instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393139
title: Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

description: Simulation-based Bayesian inference methods are useful when the statistical model of interest does not possess a computationally tractable likelihood function. One such likelihood-free method is approximate Bayesian computation (ABC), which approximates the likelihood of a carefully chosen summary statistic via model simulation and nonparametric density estimation. ABC is known to suffer a curse of dimensionality with respect to the size of the summary statistic. When the model summary statistic is roughly normally distributed in regions of the parameter space of interest, Bayesian synthetic likelihood (BSL), which uses a normal likelihood approximation for a summary statistic, is a useful method that can be more computationally efficient than ABC. However, BSL requires estimation of the covariance matrix of the summary statistic for each proposed parameter, which requires a large number of simulations to estimate precisely using the sample covariance matrix when the summary statistic is high dimensional. In this article, we propose to use the graphical lasso to provide a sparse estimate of the precision matrix. This approach can estimate the covariance matrix accurately with significantly fewer model simulations. We discuss the nontrivial issue of tuning parameter choice in the context of BSL and demonstrate on several complex applications that our method, which we call BSLasso, provides significant improvements in computational efficiency whilst maintaining the ability to produce similar posterior distributions to BSL. The BSL and BSLasso methods applied to the examples of this article are implemented in the BSL package in R, which is available on the Comprehensive R Archive Network. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7393142.v1
title: Robust and Accurate Inference via a Mixture of Gaussian and Student's <i>t</i> Errors

description: A Gaussian measurement error assumption, i.e., an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student's <i>t</i> distribution helps reduce the bias. However, it may be less efficient in estimating parameters if the heavy tailed assumption is uniformly applied to all of the data when most of them are normally observed. We propose a mixture error assumption that selectively converts Gaussian errors into Student's <i>t</i> errors according to latent outlier indicators, leveraging the best of the Gaussian and Student's <i>t</i> errors; a parameter estimation can be not only robust but also accurate. Using simulated hospital profiling data and astronomical time series of brightness data, we demonstrate the potential for the proposed mixture error assumption to estimate parameters accurately in the presence of outliers. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.7393145.v1
title: The Offset Normal Shape Distribution for Dynamic Shape Analysis

description: This paper deals with the statistical analysis of landmark data observed at different temporal instants. Statistical analysis of dynamic shapes is a problem with significant challenges due to the difficulty in providing a description of the shape changes over time, across subjects and over groups of subjects. There are several modelling strategies which can be used for dynamic shape analysis. Here, we use the exact distribution theory for the shape of planar correlated Gaussian configurations and derive the induced <i>offset-normal</i> shape distribution. Various properties of this distribution are investigated, and some special cases discussed. This work is a natural progression of what has been proposed in Mardia and Dryden (1989), Dryden and Mardia (1991), Mardia and Walder (1994) and Kume and Welling (2010).
DOI: 10.7284/120128
title: Anemometer data collected during research cruise OC1409C using a Gill WindObserver II instrument system onboard the platform RV Oceanus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7393142.v3
title: Robust and Accurate Inference via a Mixture of Gaussian and Student’s <i>t</i> Errors

description: A Gaussian measurement error assumption, that is, an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student’s <i>t</i> distribution helps reduce the bias. However, it may be less efficient in estimating parameters if the heavy tailed assumption is uniformly applied to all of the data when most of them are normally observed. We propose a mixture error assumption that selectively converts Gaussian errors into Student’s <i>t</i> errors according to latent outlier indicators, leveraging the best of the Gaussian and Student’s <i>t</i> errors; a parameter estimation can be not only robust but also accurate. Using simulated hospital profiling data and astronomical time series of brightness data, we demonstrate the potential for the proposed mixture error assumption to estimate parameters accurately in the presence of outliers. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7393145.v2
title: The Offset Normal Shape Distribution for Dynamic Shape Analysis

description: This article deals with the statistical analysis of landmark data observed at different temporal instants. Statistical analysis of dynamic shapes is a problem with significant challenges due to the difficulty in providing a description of the shape changes over time, across subjects and over groups of subjects. There are several modeling strategies, which can be used for dynamic shape analysis. Here, we use the exact distribution theory for the shape of planar correlated Gaussian configurations and derive the induced <i>offset-normal</i> shape distribution. Various properties of this distribution are investigated, and some special cases discussed. This work is a natural progression of what has been proposed in Mardia and Dryden, Dryden and Mardia, Mardia and Walder, and Kume and Welling. Supplemental materials for this article are available online.