cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11396871.v2
title: Density Deconvolution With Additive Measurement Errors Using Quadratic Programming

description: Distribution estimation for noisy data via density deconvolution is a notoriously difficult problem, especially for typical noise distributions like Gaussian. We develop a density deconvolution estimator based on quadratic programming (QP) that can achieve better estimation than kernel density deconvolution methods. The QP approach appears to have a more favorable regularization tradeoff between oversmoothing versus oscillation, especially at the tails of the distribution. An additional advantage is that it is straightforward to incorporate a number of common density constraints such as nonnegativity, integration-to-one, unimodality, tail convexity, tail monotonicity, and support constraints. We demonstrate that the QP approach has favorable estimation performance relative to existing methods. Its performance is superior when only the universally applicable nonnegativity and integration-to-one constraints are incorporated, and incorporating additional common constraints when applicable (e.g., nonnegative support, unimodality, tail monotonicity or convexity, etc.) can further substantially improve the estimation. Supplementary materials for this article are available online and include R code, the R package QPdecon, a vignette for the QPdecon package, the sodium dataset that is used as an example, and appendices with a proof and additional figures.
DOI: 10.6084/m9.figshare.11232188.v2
title: First- and Second-Order Characteristics of Spatio-Temporal Point Processes on Linear Networks

description: We present several characteristics for spatio-temporal point patterns when the spatial locations are restricted to a linear network. A nonparametric kernel-based intensity estimator is proposed to highlight the concentration of events within the network and time, either jointly or separately. We also provide second-order characteristics for spatio-temporal point patterns on linear networks such as <i>K</i>-function and pair correlation function to analyze the type of interaction between points. They are independent of network’s geometry and have known values for Poisson point processes. Finally, we consider some applications to traffic accidents and demonstrate our findings by analyzing datasets of Houston (United States), Medellín (Colombia), and Eastbourne (United Kingdom). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11402247.v2
title: A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data

description: This technical note proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to develop a matrix-free framework for likelihood maximization. Simulation results show that our method is substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to fit factor models on data from suicide attempters, suicide ideators, and a control group. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11555739.v1
title: Bayesian Function-on-Scalars Regression for High-Dimensional Data

description: We develop a fully Bayesian framework for function-on-scalars regression with many predictors. The functional data response is modeled nonparametrically using unknown basis functions, which produces a flexible and data-adaptive functional basis. We incorporate shrinkage priors that effectively remove unimportant scalar covariates from the model and reduce sensitivity to the number of (unknown) basis functions. For variable selection in functional regression, we propose a decision theoretical posterior summarization technique, which identifies a subset of covariates that retains nearly the predictive accuracy of the full model. Our approach is broadly applicable for Bayesian functional regression models, and unlike existing methods provides joint rather than marginal selection of important predictor variables. Computationally scalable posterior inference is achieved using a Gibbs sampler with linear time complexity in the number of predictors. The resulting algorithm is empirically faster than existing frequentist and Bayesian techniques, and provides joint estimation of model parameters, prediction and imputation of functional trajectories, and uncertainty quantification via the posterior distribution. A simulation study demonstrates improvements in estimation accuracy, uncertainty quantification, and variable selection relative to existing alternatives. The methodology is applied to actigraphy data to investigate the association between intraday physical activity and responses to a sleep questionnaire. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11410047.v1
title: A Random Forest Approach for Bounded Outcome Variables

description: Random forests have become an established tool for classification and regression, in particular in high-dimensional settings and in the presence of nonadditive predictor-response relationships. For bounded outcome variables restricted to the unit interval, however, classical modeling approaches based on mean squared error loss may severely suffer as they do not account for heteroscedasticity in the data. To address this issue, we propose a random forest approach for relating a beta distributed outcome to a set of explanatory variables. Our approach explicitly makes use of the likelihood function of the beta distribution for the selection of splits during the tree-building procedure. In each iteration of the tree-building algorithm it chooses one explanatory variable in combination with a split point that maximizes the log-likelihood function of the beta distribution with the parameter estimates derived from the nodes of the currently built tree. Results of several simulation studies and an application using data from the U.S.A. National Lakes Assessment Survey demonstrate the properties and usefulness of the method, in particular when compared to random forest approaches based on mean squared error loss and parametric regression models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11607294.v2
title: The Automatic Construction of Bootstrap Confidence Intervals

description: The standard intervals, for example, θ̂±1.96σ̂ for nominal 95% two-sided coverage, are familiar and easy to use, but can be of dubious accuracy in regular practice. Bootstrap confidence intervals offer an order of magnitude improvement—from first order to second order accuracy. This article introduces a new set of algorithms that automate the construction of bootstrap intervals, substituting computer power for the need to individually program particular applications. The algorithms are described in terms of the underlying theory that motivates them, along with examples of their application. They are implemented in the R package bcaboot. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11973456.v1
title: Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

description: Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to the high number of parameters. Therefore, maximum likelihood methods are often infeasible and researchers rely on alternative techniques, such as Gibbs sampling. But conventional Gibbs samplers are often tailored to specific situations and suffer from high autocorrelation among repeated draws. We present a Gibbs sampler for general nonlinear state space models with an univariate autoregressive state equation. For this we employ an interweaving strategy and elliptical slice sampling to exploit the dependence implied by the autoregressive process. Within a simulation study we demonstrate the efficiency of the proposed sampler for bivariate dynamic copula models. Further we are interested in modeling the volatility return relationship. Therefore, we use the proposed sampler to estimate the parameters of stochastic volatility models with skew Student’s <i>t</i> errors and the parameters of a novel bivariate dynamic mixture copula model. This model allows for dynamic asymmetric tail dependence. Comparison to relevant benchmark models, such as the DCC-GARCH or a Student’s <i>t</i> copula model, with respect to predictive accuracy shows the superior performance of the proposed approach. R scripts, an R package, data and additional results are provided as supplementary materials. Supplementary materials for this article are available online.
DOI: 10.3886/e149061v1
title: Data and Code for: College Majors, Occupations, and the Gender Wage Gap

description: The paper assesses gender differences in pre-labor market specialization among the college-educated and highlights how those differences have evolved over time. Women choose majors with lower potential earnings (based on male wages associated with those majors) and subsequently sort into occupations with lower potential earnings given their major choice. These differences have narrowed over time, but recent cohorts of women still choose majors and occupations with lower potential earnings. Differences in undergraduate major choice explain a substantive portion of gender wage gaps for the college-educated above and beyond simply controlling for occupation. Collectively, our results highlight the importance of understanding gender differences in the mapping between college major and occupational sorting when studying the evolution of gender differences in labor market outcomes over time.
DOI: 10.5517/ccdc.csd.cc28dkr0
title: CCDC 2097452: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dks1
title: CCDC 2097453: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dkt2
title: CCDC 2097454: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dkv3
title: CCDC 2097455: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dkw4
title: CCDC 2097456: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dkx5
title: CCDC 2097457: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dky6
title: CCDC 2097458: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dkz7
title: CCDC 2097459: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dl09
title: CCDC 2097460: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dl1b
title: CCDC 2097461: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.5517/ccdc.csd.cc28dl2c
title: CCDC 2097462: Experimental Crystal Structure Determination

description: Related Article: G. Novelli, C.J. McMonagle, F. Kleemiss, M. Probert, H. Puschmann, S. Grabowsky, H.E. Maynard-Casely, G.J. McIntyre, S. Parsons|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|785|doi:10.1107/S205252062100740X
DOI: 10.6084/m9.figshare.11619867
title: A Fused Gaussian Process Model for Very Large Spatial Data

description: With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model large or massive spatial datasets. In particular, a Gaussian process with additive components is proposed, with its covariance structure consisting of two components: one component is flexible without assuming a specific parametric covariance function but is able to achieve dimension reduction; the other is parametric and simultaneously induces sparsity. The inference algorithm for parameter estimation and spatial prediction is devised. The resulting spatial prediction methodology that we call fused Gaussian process (FGP), is applied to simulated data and a massive satellite dataset. The results demonstrate the computational and inferential benefits of FGP over competing methods and show that FGP is robust against model misspecification and captures spatial nonstationarity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11619867.v3
title: A Fused Gaussian Process Model for Very Large Spatial Data

description: With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model large or massive spatial datasets. In particular, a Gaussian process with additive components is proposed, with its covariance structure consisting of two components: one component is flexible without assuming a specific parametric covariance function but is able to achieve dimension reduction; the other is parametric and simultaneously induces sparsity. The inference algorithm for parameter estimation and spatial prediction is devised. The resulting spatial prediction methodology that we call fused Gaussian process (FGP), is applied to simulated data and a massive satellite dataset. The results demonstrate the computational and inferential benefits of FGP over competing methods and show that FGP is robust against model misspecification and captures spatial nonstationarity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11932950.v3
title: Probabilistic Detection and Estimation of Conic Sections From Noisy Data

description: Inferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for conic sections is that estimation methods rely on the underlying shape of the conics (being known to be ellipse, parabola, or hyperbola). A general purpose Bayesian hierarchical model is proposed for conic sections and corresponding estimation method based on noisy data is shown to work even when the specific nature of the conic section is unknown. The model, thus, provides probabilistic detection of the underlying conic section and inference about the associated parameters of the conic section. Through extensive simulation studies where the true conics may not be known, the methodology is demonstrated to have practical and methodological advantages relative to many existing techniques. In addition, the proposed method provides probabilistic measures of uncertainty of the estimated parameters. Furthermore, we observe high fidelity to the true conics even in challenging situations, such as data arising from partial conics in arbitrarily rotated and nonstandard form, and where a visual inspection is unable to correctly identify the type of conic section underlying the data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11366066.v2
title: Data transforming augmentation for heteroscedastic models

description: Data augmentation (DA) turns seemingly intractable computational problems into simple ones by augmenting latent missing data. In addition to computational simplicity, it is now well-established that DA equipped with a deterministic transformation can improve the convergence speed of iterative algorithms such as an EM algorithm or Gibbs sampler. In this article, we outline a framework for the transformation-based DA, which we call data transforming augmentation (DTA), allowing augmented data to be a deterministic function of latent and observed data, and unknown parameters. Under this framework, we investigate a novel DTA scheme that turns heteroscedastic models into homoscedastic ones to take advantage of simpler computations typically available in homoscedastic cases. Applying this DTA scheme to fitting linear mixed models, we demonstrate simpler computations and faster convergence rates of resulting iterative algorithms, compared with those under a non-transformation-based DA scheme. We also fit a Beta-Binomial model using the proposed DTA scheme, which enables sampling approximate marginal posterior distributions that are available only under homoscedasticity. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.11932950.v2
title: Probabilistic Detection and Estimation of Conic Sections From Noisy Data

description: Inferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for conic sections is that estimation methods rely on the underlying shape of the conics (being known to be ellipse, parabola, or hyperbola). A general purpose Bayesian hierarchical model is proposed for conic sections and corresponding estimation method based on noisy data is shown to work even when the specific nature of the conic section is unknown. The model, thus, provides probabilistic detection of the underlying conic section and inference about the associated parameters of the conic section. Through extensive simulation studies where the true conics may not be known, the methodology is demonstrated to have practical and methodological advantages relative to many existing techniques. In addition, the proposed method provides probabilistic measures of uncertainty of the estimated parameters. Furthermore, we observe high fidelity to the true conics even in challenging situations, such as data arising from partial conics in arbitrarily rotated and nonstandard form, and where a visual inspection is unable to correctly identify the type of conic section underlying the data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11366066.v1
title: Data transforming augmentation for heteroscedastic models

description: Data augmentation (DA) turns seemingly intractable computational problems into simple ones by augmenting latent missing data. In addition to computational simplicity, it is now well-established that DA equipped with a deterministic transformation can improve the convergence speed of iterative algorithms such as an EM algorithm or Gibbs sampler. In this article, we outline a framework for the transformation-based DA, which we call data transforming augmentation (DTA), allowing augmented data to be a deterministic function of latent and observed data, and unknown parameters. Under this framework, we investigate a novel DTA scheme that turns heteroscedastic models into homoscedastic ones to take advantage of simpler computations typically available in homoscedastic cases. Applying this DTA scheme to fitting linear mixed models, we demonstrate simpler computations and faster convergence rates of resulting iterative algorithms, compared with those under a non-transformation-based DA scheme. We also fit a Beta-Binomial model using the proposed DTA scheme, which enables sampling approximate marginal posterior distributions that are available only under homoscedasticity. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.11932950
title: Probabilistic Detection and Estimation of Conic Sections From Noisy Data

description: Inferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for conic sections is that estimation methods rely on the underlying shape of the conics (being known to be ellipse, parabola, or hyperbola). A general purpose Bayesian hierarchical model is proposed for conic sections and corresponding estimation method based on noisy data is shown to work even when the specific nature of the conic section is unknown. The model, thus, provides probabilistic detection of the underlying conic section and inference about the associated parameters of the conic section. Through extensive simulation studies where the true conics may not be known, the methodology is demonstrated to have practical and methodological advantages relative to many existing techniques. In addition, the proposed method provides probabilistic measures of uncertainty of the estimated parameters. Furthermore, we observe high fidelity to the true conics even in challenging situations, such as data arising from partial conics in arbitrarily rotated and nonstandard form, and where a visual inspection is unable to correctly identify the type of conic section underlying the data. Supplementary materials for this article are available online.