cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7895756.v3
title: On Prediction Properties of Kriging: Uniform Error Bounds and Robustness

description: Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work, we obtain some error bounds for the simple and universal kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Matérn correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Matérn correlation functions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7999331.v4
title: A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

description: Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8145704.v5
title: Bayesian Optimal Design for Ordinary Differential Equation Models With Application in Biological Science

description: Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of nonlinear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing the transport of amino acids through cell membranes in human placentas. Decision-theoretic Bayesian design of experiments for such nonlinear models is conceptually very attractive, allowing the formal incorporation of prior knowledge to overcome the parameter dependence of frequentist design and being less reliant on asymptotic approximations. However, the necessary approximation and maximization of the, typically analytically intractable, expected utility results in a computationally challenging problem. These issues are further exacerbated if the solution to the differential equations is not available in closed-form. This article proposes a new combination of a probabilistic solution to the equations embedded within a Monte Carlo approximation to the expected utility with cyclic descent of a smooth approximation to find the optimal design. A novel precomputation algorithm reduces the computational burden, making the search for an optimal design feasible for bigger problems. The methods are demonstrated by finding new designs for a number of common models derived from differential equations, and by providing optimal designs for the placenta experiment. 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.9103811
title: Statistical Topology and the Random Interstellar Medium

description: We use topological methods to investigate the small-scale variation and local spatial characteristics of the interstellar medium (ISM) in three regions of the southern sky. We demonstrate that there are circumstances where topological methods can identify differences in distributions when conventional marginal or correlation analyses may not. We propose a nonparametric method for comparing two fields based on the counts of topological features and the geometry of the associated persistence diagrams. We investigate the expected distribution of topological structures quantified through Betti numbers under Gaussian random field (GRF) assumptions, which underlie many astrophysical models of the ISM. When we apply the methods to the astrophysical data, we find strong evidence that one of the three regions is both topologically dissimilar to the other two and not consistent with an underlying GRF model. This region is proximal to a region of recent star formation whereas the others are more distant. 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.9103811.v5
title: Statistical Topology and the Random Interstellar Medium

description: We use topological methods to investigate the small-scale variation and local spatial characteristics of the interstellar medium (ISM) in three regions of the southern sky. We demonstrate that there are circumstances where topological methods can identify differences in distributions when conventional marginal or correlation analyses may not. We propose a nonparametric method for comparing two fields based on the counts of topological features and the geometry of the associated persistence diagrams. We investigate the expected distribution of topological structures quantified through Betti numbers under Gaussian random field (GRF) assumptions, which underlie many astrophysical models of the ISM. When we apply the methods to the astrophysical data, we find strong evidence that one of the three regions is both topologically dissimilar to the other two and not consistent with an underlying GRF model. This region is proximal to a region of recent star formation whereas the others are more distant. 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.7792106
title: Informed Proposals for Local MCMC in Discrete Spaces

description: <b>There is a lack of methodological results to design efficient Markov chain Monte Carlo (</b><b>MCMC</b><b>) algorithms for statistical models with discrete-valued high-dimensional parameters. Motivated by this consideration, we propose a simple framework for the design of informed</b><b>MCMC</b><b>proposals (i.e., Metropolis–Hastings proposal distributions that appropriately incorporate local information about the target) which is naturally applicable to discrete spaces. Using Peskun-type comparisons of Markov kernels, we explicitly characterize the class of asymptotically optimal proposal distributions under this framework, which we refer to as <i>locally balanced</i> proposals. The resulting algorithms are straightforward to implement in discrete spaces and provide orders of magnitude improvements in efficiency compared to alternative</b><b>MCMC</b><b>schemes, including discrete versions of Hamiltonian Monte Carlo. Simulations are performed with both simulated and real datasets, including a detailed application to Bayesian record linkage. A direct connection with gradient-based</b><b>MCMC</b><b>suggests that locally balanced proposals can be seen as a natural way to extend the latter to discrete spaces. Supplementary materials for this article are available online.</b>
DOI: 10.6084/m9.figshare.8069309.v4
title: Robust Clustering With Subpopulation-Specific Deviations

description: The National Birth Defects Prevention Study (NBDPS) is a case-control study of birth defects conducted across 10 U.S. states. Researchers are interested in characterizing the etiologic role of maternal diet, collected using a food frequency questionnaire. Because diet is multidimensional, dimension reduction methods such as cluster analysis are often used to summarize dietary patterns. In a large, heterogeneous population, traditional clustering methods, such as latent class analysis, used to estimate dietary patterns can produce a large number of clusters due to a variety of factors, including study size and regional diversity. These factors result in a loss of interpretability of patterns that may differ due to minor consumption changes. Based on adaptation of the local partition process, we propose a new method, robust profile clustering, to handle these data complexities. Here, participants may be clustered at two levels: (1) globally, where women are assigned to an overall population-level cluster via an overfitted finite mixture model, and (2) locally, where regional variations in diet are accommodated via a beta-Bernoulli process dependent on subpopulation differences. We use our method to analyze the NBDPS data, deriving prepregnancy dietary patterns for women in the NBDPS while accounting for regional variability. 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.8145704.v4
title: Bayesian Optimal Design for Ordinary Differential Equation Models With Application in Biological Science

description: Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of nonlinear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing the transport of amino acids through cell membranes in human placentas. Decision-theoretic Bayesian design of experiments for such nonlinear models is conceptually very attractive, allowing the formal incorporation of prior knowledge to overcome the parameter dependence of frequentist design and being less reliant on asymptotic approximations. However, the necessary approximation and maximization of the, typically analytically intractable, expected utility results in a computationally challenging problem. These issues are further exacerbated if the solution to the differential equations is not available in closed-form. This article proposes a new combination of a probabilistic solution to the equations embedded within a Monte Carlo approximation to the expected utility with cyclic descent of a smooth approximation to find the optimal design. A novel precomputation algorithm reduces the computational burden, making the search for an optimal design feasible for bigger problems. The methods are demonstrated by finding new designs for a number of common models derived from differential equations, and by providing optimal designs for the placenta experiment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.17876/plate/dr.3/plates/102_13645
title: Digital object identifier (DOI) for the plate 13645 in archive 102 from the APPLAUSE DR3
DOI: 10.6084/m9.figshare.7792106.v3
title: Informed Proposals for Local MCMC in Discrete Spaces

description: <b>There is a lack of methodological results to design efficient Markov chain Monte Carlo (</b><b>MCMC</b><b>) algorithms for statistical models with discrete-valued high-dimensional parameters. Motivated by this consideration, we propose a simple framework for the design of informed</b><b>MCMC</b><b>proposals (i.e., Metropolis–Hastings proposal distributions that appropriately incorporate local information about the target) which is naturally applicable to discrete spaces. Using Peskun-type comparisons of Markov kernels, we explicitly characterize the class of asymptotically optimal proposal distributions under this framework, which we refer to as <i>locally balanced</i> proposals. The resulting algorithms are straightforward to implement in discrete spaces and provide orders of magnitude improvements in efficiency compared to alternative</b><b>MCMC</b><b>schemes, including discrete versions of Hamiltonian Monte Carlo. Simulations are performed with both simulated and real datasets, including a detailed application to Bayesian record linkage. A direct connection with gradient-based</b><b>MCMC</b><b>suggests that locally balanced proposals can be seen as a natural way to extend the latter to discrete spaces. Supplementary materials for this article are available online.</b>
DOI: 10.6084/m9.figshare.9103811.v4
title: Statistical Topology and the Random Interstellar Medium

description: We use topological methods to investigate the small-scale variation and local spatial characteristics of the interstellar medium (ISM) in three regions of the southern sky. We demonstrate that there are circumstances where topological methods can identify differences in distributions when conventional marginal or correlation analyses may not. We propose a nonparametric method for comparing two fields based on the counts of topological features and the geometry of the associated persistence diagrams. We investigate the expected distribution of topological structures quantified through Betti numbers under Gaussian random field (GRF) assumptions, which underlie many astrophysical models of the ISM. When we apply the methods to the astrophysical data, we find strong evidence that one of the three regions is both topologically dissimilar to the other two and not consistent with an underlying GRF model. This region is proximal to a region of recent star formation whereas the others are more distant. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.17876/plate/dr.3/plates/102_13646
title: Digital object identifier (DOI) for the plate 13646 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13647
title: Digital object identifier (DOI) for the plate 13647 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13648
title: Digital object identifier (DOI) for the plate 13648 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13649
title: Digital object identifier (DOI) for the plate 13649 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13650
title: Digital object identifier (DOI) for the plate 13650 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13651
title: Digital object identifier (DOI) for the plate 13651 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13652
title: Digital object identifier (DOI) for the plate 13652 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13653
title: Digital object identifier (DOI) for the plate 13653 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13654
title: Digital object identifier (DOI) for the plate 13654 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13655
title: Digital object identifier (DOI) for the plate 13655 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13656
title: Digital object identifier (DOI) for the plate 13656 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13657
title: Digital object identifier (DOI) for the plate 13657 in archive 102 from the APPLAUSE DR3
DOI: 10.5517/ccdc.csd.cc2f1l9d
title: CCDC 2235853: Experimental Crystal Structure Determination

description: Related Article: Anshul Jain, Akanksha Kumari, Suman K. Saha, Khyati Shukla, Ashwani Chauhan, Ramesh K. Metre, Nirmal K. Rana|2023|J.Org.Chem.|88|11346|doi:10.1021/acs.joc.3c01052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fg78f
title: CCDC 2248004: Experimental Crystal Structure Determination

description: Related Article: Zhenyu Zhang, Wei Zhang, Jun−Chen Tang, Jin−Teng Che, Zhongchao Zhang, Jia−Hua Chen, Zhen Yang|2023|J.Org.Chem.|88|10539|doi:10.1021/acs.joc.3c00542
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17876/plate/dr.3/plates/102_13658
title: Digital object identifier (DOI) for the plate 13658 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13659
title: Digital object identifier (DOI) for the plate 13659 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13660
title: Digital object identifier (DOI) for the plate 13660 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13661
title: Digital object identifier (DOI) for the plate 13661 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13662
title: Digital object identifier (DOI) for the plate 13662 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13663
title: Digital object identifier (DOI) for the plate 13663 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13664
title: Digital object identifier (DOI) for the plate 13664 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13665
title: Digital object identifier (DOI) for the plate 13665 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13666
title: Digital object identifier (DOI) for the plate 13666 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13667
title: Digital object identifier (DOI) for the plate 13667 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13668
title: Digital object identifier (DOI) for the plate 13668 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13669
title: Digital object identifier (DOI) for the plate 13669 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13670
title: Digital object identifier (DOI) for the plate 13670 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13671
title: Digital object identifier (DOI) for the plate 13671 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13672
title: Digital object identifier (DOI) for the plate 13672 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13673
title: Digital object identifier (DOI) for the plate 13673 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13674
title: Digital object identifier (DOI) for the plate 13674 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13675
title: Digital object identifier (DOI) for the plate 13675 in archive 102 from the APPLAUSE DR3
DOI: 10.18112/openneuro.ds004697.v1.0.1
title: Food and Brain Study
DOI: 10.17876/plate/dr.3/plates/102_13676
title: Digital object identifier (DOI) for the plate 13676 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13677
title: Digital object identifier (DOI) for the plate 13677 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13678
title: Digital object identifier (DOI) for the plate 13678 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13679
title: Digital object identifier (DOI) for the plate 13679 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13680
title: Digital object identifier (DOI) for the plate 13680 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13681
title: Digital object identifier (DOI) for the plate 13681 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13682
title: Digital object identifier (DOI) for the plate 13682 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13683
title: Digital object identifier (DOI) for the plate 13683 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13684
title: Digital object identifier (DOI) for the plate 13684 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13685
title: Digital object identifier (DOI) for the plate 13685 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13686
title: Digital object identifier (DOI) for the plate 13686 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13687
title: Digital object identifier (DOI) for the plate 13687 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13688
title: Digital object identifier (DOI) for the plate 13688 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13689
title: Digital object identifier (DOI) for the plate 13689 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13690
title: Digital object identifier (DOI) for the plate 13690 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13691
title: Digital object identifier (DOI) for the plate 13691 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13692
title: Digital object identifier (DOI) for the plate 13692 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13693
title: Digital object identifier (DOI) for the plate 13693 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13694
title: Digital object identifier (DOI) for the plate 13694 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13695
title: Digital object identifier (DOI) for the plate 13695 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13696
title: Digital object identifier (DOI) for the plate 13696 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13697
title: Digital object identifier (DOI) for the plate 13697 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13698
title: Digital object identifier (DOI) for the plate 13698 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13699
title: Digital object identifier (DOI) for the plate 13699 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13700
title: Digital object identifier (DOI) for the plate 13700 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13701
title: Digital object identifier (DOI) for the plate 13701 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13702
title: Digital object identifier (DOI) for the plate 13702 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13703
title: Digital object identifier (DOI) for the plate 13703 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13704
title: Digital object identifier (DOI) for the plate 13704 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13705
title: Digital object identifier (DOI) for the plate 13705 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13706
title: Digital object identifier (DOI) for the plate 13706 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13707
title: Digital object identifier (DOI) for the plate 13707 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13708
title: Digital object identifier (DOI) for the plate 13708 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13709
title: Digital object identifier (DOI) for the plate 13709 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13710
title: Digital object identifier (DOI) for the plate 13710 in archive 102 from the APPLAUSE DR3