cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7590851.v2
title: Nonparametric Imputation by Data Depth

description: We present single imputation method for missing values which borrows the idea of data depth—a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly defined statistical depth function. For each single iteration, imputation reverts to optimization of quadratic, linear, or quasiconcave functions that are solved analytically by linear programming or the Nelder–Mead method. As it accounts for the underlying data topology, the procedure is distribution free, allows imputation close to the data geometry, can make prediction in situations where local imputation (<i>k</i>-nearest neighbors, random forest) cannot, and has attractive robustness and asymptotic properties under elliptical symmetry. It is shown that a special case—when using the Mahalanobis depth—has direct connection to well-known methods for the multivariate normal model, such as iterated regression and regularized PCA. The methodology is extended to multiple imputation for data stemming from an elliptically symmetric distribution. Simulation and real data studies show good results compared with existing popular alternatives. The method has been implemented as an R-package. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.7458776
title: Bayesian Repulsive Gaussian Mixture Model

description: We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the independent prior on the component centers, the repulsive prior introduces additional shrinkage effect on the tail probability of the posterior number of components, which serves as a measurement of the model complexity. In addition, a generalized urn model that allows a random number of components and correlated component centers is developed based on the exchangeable partition distribution, which gives rise to the corresponding blocked-collapsed Gibbs sampler for posterior inference. We evaluate the performance and demonstrate the advantages of the proposed methodology through extensive simulation studies and real data analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7458776.v3
title: Bayesian Repulsive Gaussian Mixture Model

description: We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the independent prior on the component centers, the repulsive prior introduces additional shrinkage effect on the tail probability of the posterior number of components, which serves as a measurement of the model complexity. In addition, a generalized urn model that allows a random number of components and correlated component centers is developed based on the exchangeable partition distribution, which gives rise to the corresponding blocked-collapsed Gibbs sampler for posterior inference. We evaluate the performance and demonstrate the advantages of the proposed methodology through extensive simulation studies and real data analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7862645
title: Hierarchical Normalized Completely Random Measures to Cluster Grouped Data

description: In this article, we propose a Bayesian nonparametric model for clustering grouped data. We adopt a hierarchical approach: at the highest level, each group of data is modeled according to a mixture, where the mixing distributions are conditionally independent normalized completely random measures (NormCRMs) centered on the same base measure, which is itself a NormCRM. The discreteness of the shared base measure implies that the processes at the data level share the same atoms. This desired feature allows to cluster together observations of different groups. We obtain a representation of the hierarchical clustering model by marginalizing with respect to the infinite dimensional NormCRMs. We investigate the properties of the clustering structure induced by the proposed model and provide theoretical results concerning the distribution of the number of clusters, within and between groups. Furthermore, we offer an interpretation in terms of generalized Chinese restaurant franchise process, which allows for posterior inference under both conjugate and nonconjugate models. We develop algorithms for fully Bayesian inference and assess performances by means of a simulation study and a real-data illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7862645.v2
title: Hierarchical Normalized Completely Random Measures to Cluster Grouped Data

description: In this article, we propose a Bayesian nonparametric model for clustering grouped data. We adopt a hierarchical approach: at the highest level, each group of data is modeled according to a mixture, where the mixing distributions are conditionally independent normalized completely random measures (NormCRMs) centered on the same base measure, which is itself a NormCRM. The discreteness of the shared base measure implies that the processes at the data level share the same atoms. This desired feature allows to cluster together observations of different groups. We obtain a representation of the hierarchical clustering model by marginalizing with respect to the infinite dimensional NormCRMs. We investigate the properties of the clustering structure induced by the proposed model and provide theoretical results concerning the distribution of the number of clusters, within and between groups. Furthermore, we offer an interpretation in terms of generalized Chinese restaurant franchise process, which allows for posterior inference under both conjugate and nonconjugate models. We develop algorithms for fully Bayesian inference and assess performances by means of a simulation study and a real-data illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7458776.v2
title: Bayesian Repulsive Gaussian Mixture Model

description: We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the independent prior on the component centers, the repulsive prior introduces additional shrinkage effect on the tail probability of the posterior number of components, which serves as a measurement of the model complexity. In addition, a generalized urn model that allows a random number of components and correlated component centers is developed based on the exchangeable partition distribution, which gives rise to the corresponding blocked-collapsed Gibbs sampler for posterior inference. We evaluate the performance and demonstrate the advantages of the proposed methodology through extensive simulation studies and real data analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874162.v4
title: A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

description: People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This article is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.7874162
title: A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

description: People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This article is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.7862645.v1
title: Hierarchical Normalized Completely Random Measures to Cluster Grouped Data

description: In this article, we propose a Bayesian nonparametric model for clustering grouped data. We adopt a hierarchical approach: at the highest level, each group of data is modeled according to a mixture, where the mixing distributions are conditionally independent normalized completely random measures (NormCRMs) centered on the same base measure, which is itself a NormCRM. The discreteness of the shared base measure implies that the processes at the data level share the same atoms. This desired feature allows to cluster together observations of different groups. We obtain a representation of the hierarchical clustering model by marginalizing with respect to the infinite dimensional NormCRMs. We investigate the properties of the clustering structure induced by the proposed model and provide theoretical results concerning the distribution of the number of clusters, within and between groups. Furthermore, we offer an interpretation in terms of generalized Chinese restaurant franchise process, which allows for posterior inference under both conjugate and nonconjugate models. We develop algorithms for fully Bayesian inference and assess performances by means of a simulation study and a real-data illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8069312
title: Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes

description: Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This article offers a simple, practical construction of simultaneous confidence bands for quantile and QE functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for distribution functions, which are readily available for many problems. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling methods for observed and counterfactual distributions, and does not depend on the sampling scheme. We apply our method to characterize the distributional impact of insurance coverage on health care utilization and obtain the distributional decomposition of the racial test score gap. We find that universal insurance coverage increases the number of doctor visits across the entire distribution, and that the racial test score gap is small at early ages but grows with age due to socio-economic factors especially at the top of the distribution. Supplementary materials (additional results, R package, replication files) for this article are available online.
DOI: 10.6084/m9.figshare.7874162.v3
title: A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

description: People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This article is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.8069312.v4
title: Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes

description: Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This article offers a simple, practical construction of simultaneous confidence bands for quantile and QE functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for distribution functions, which are readily available for many problems. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling methods for observed and counterfactual distributions, and does not depend on the sampling scheme. We apply our method to characterize the distributional impact of insurance coverage on health care utilization and obtain the distributional decomposition of the racial test score gap. We find that universal insurance coverage increases the number of doctor visits across the entire distribution, and that the racial test score gap is small at early ages but grows with age due to socio-economic factors especially at the top of the distribution. Supplementary materials (additional results, R package, replication files) for this article are available online.
DOI: 10.6084/m9.figshare.8069312.v3
title: Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes

description: Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This article offers a simple, practical construction of simultaneous confidence bands for quantile and QE functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for distribution functions, which are readily available for many problems. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling methods for observed and counterfactual distributions, and does not depend on the sampling scheme. We apply our method to characterize the distributional impact of insurance coverage on health care utilization and obtain the distributional decomposition of the racial test score gap. We find that universal insurance coverage increases the number of doctor visits across the entire distribution, and that the racial test score gap is small at early ages but grows with age due to socio-economic factors especially at the top of the distribution. Supplementary materials (additional results, R package, replication files) for this article are available online.
DOI: 10.5281/zenodo.5363311
title: Supplementary Information for "Semi-automated high-throughput substrate screening assay for nucleoside kinases

description: This is the external Supplementary Information for our publication "Semi-automated high-throughput substrate screening assay for nucleoside kinases". Files are to follow soon. We apologize for the delay. Thank you for your patience. The preprint and the Supporting Information are available at ChemRxiv (https://doi.org/10.33774/chemrxiv-2021-k0w7q).
DOI: 10.5281/zenodo.5363310
title: Supplementary Information for "Semi-automated high-throughput substrate screening assay for nucleoside kinases

description: This is the external Supplementary Information for our publication "Semi-automated high-throughput substrate screening assay for nucleoside kinases". Files are to follow soon. We apologize for the delay. Thank you for your patience. The preprint and the Supporting Information are available at ChemRxiv (https://doi.org/10.33774/chemrxiv-2021-k0w7q).
DOI: 10.5281/zenodo.5537057
title: Microeletrônica Orgânica

description: Repositorio de dados para caracterização morfológica de filmes finos para transistores orgânicos
DOI: 10.5281/zenodo.5537056
title: Microeletrônica Orgânica

description: Repositorio de dados para caracterização morfológica de filmes finos para transistores orgânicos
DOI: 10.6084/m9.figshare.16699066
title: Characteristics of Vertical Distributions of Methane and Dimethylsulphoniopropionate in the southern Yap Trench

description: samples were collected in the southern Yap Trench for measuring concentrations of CH<sub>4</sub>, DMSPd, DMSPp, and DMSPt
DOI: 10.6084/m9.figshare.16699066.v1
title: Characteristics of Vertical Distributions of Methane and Dimethylsulphoniopropionate in the southern Yap Trench

description: samples were collected in the southern Yap Trench for measuring concentrations of CH<sub>4</sub>, DMSPd, DMSPp, and DMSPt
DOI: 10.5281/zenodo.5525290
title: mdsine2_forward_sims

description: Forward sims from MDSINE2 and other comparator methods
DOI: 10.5281/zenodo.5525289
title: mdsine2_forward_sims

description: Forward sims from MDSINE2 and other comparator methods
DOI: 10.3886/e151141
title: Wages, Human Capital, and Barriers to Structural Transformation

description: We document for 13 countries ranging from rich (Canada, U.S.) to poor (India, Indonesia) that average wages are considerably lower in agriculture than in the other sectors. Moreover, agriculture has less educated workers and lower Mincer returns.We view these findings through the lens of a multi-sector model in which workers differin observed and unobserved characteristics and sectors differ in their human–capitalintensities. We derive expressions for the implied barriers to the reallocation of laborout of agriculture. We find that in our sample these barriers are considerably smallerthan what the macro–development literature has argued.
DOI: 10.3886/e151141v1
title: Wages, Human Capital, and Barriers to Structural Transformation

description: We document for 13 countries ranging from rich (Canada, U.S.) to poor (India, Indonesia) that average wages are considerably lower in agriculture than in the other sectors. Moreover, agriculture has less educated workers and lower Mincer returns.We view these findings through the lens of a multi-sector model in which workers differin observed and unobserved characteristics and sectors differ in their human–capitalintensities. We derive expressions for the implied barriers to the reallocation of laborout of agriculture. We find that in our sample these barriers are considerably smallerthan what the macro–development literature has argued.
DOI: 10.6084/m9.figshare.15196465.v1
title: Functional estimation and change detection for nonstationary time series

description: Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest, while being robust to nuisance changes. Statistical analysis thus needs to allow for some form of nonstationarity under the null hypothesis of no change. In this paper, estimators for integrated parameters of locally stationary time series are constructed and a corresponding functional central limit theorem is established, enabling change-point inference for a broad class of parameters under mild assumptions. The proposed framework covers all parameters which may be expressed as nonlinear functions of moments, for example kurtosis, autocorrelation, and coefficients in a linear regression model. To perform feasible inference based on the derived limit distribution, a bootstrap variant is proposed and its consistency is established. The methodology is illustrated by means of a simulation study and by an application to high-frequency asset prices.