cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12851298.v2
title: A Two-Part Framework for Estimating Individualized Treatment Rules From Semicontinuous Outcomes

description: Health care payments are an important component of health care utilization and are thus a major focus in health services and health policy applications. However, payment outcomes are semicontinuous in that over a given period of time some patients incur no payments and some patients incur large costs. Individualized treatment rules (ITRs) are a major part of the push for tailoring treatments and interventions to patients, yet there is a little work focused on estimating ITRs from semicontinuous outcomes. In this article, we develop a framework for estimation of ITRs based on two-part modeling, wherein the ITR is estimated by separately targeting the zero part of the outcome and the strictly positive part. To improve performance when high-dimensional covariates are available, we leverage a scientifically plausible penalty that simultaneously selects variables and encourages the signs of coefficients for each variable to agree between the two components of the ITR. We develop an efficient algorithm for computation and prove oracle inequalities for the resulting estimation and prediction errors. We demonstrate the effectiveness of our approach in simulated examples and in a study of a health system intervention. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12156909.v1
title: Reinforced Designs: Multiple Instruments Plus Control Groups as Evidence Factors in an Observational Study of the Effectiveness of Catholic Schools

description: Absent randomization, causal conclusions gain strength if several independent evidence factors concur. We develop a method for constructing evidence factors from several instruments plus a direct comparison of treated and control groups, and we evaluate the methods performance in terms of design sensitivity and simulation. In the application, we consider the effectiveness of Catholic versus public high schools, constructing three evidence factors from three past strategies for studying this question, namely: (i) having nearby access to a Catholic school as an instrument, (ii) being Catholic as an instrument for attending Catholic school, and (iii) a direct comparison of students in Catholic and public high schools. Although these three analyses use the same data, we: (i) construct three essentially independent statistical tests of no effect that require very different assumptions, (ii) study the sensitivity of each test to the assumptions underlying that test, (iii) examine the degree to which independent tests dependent upon different assumptions concur, (iv) pool evidence across independent factors. In the application, we conclude that the ostensible benefit of Catholic education depends critically on the validity of one instrument, and is therefore quite fragile. 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.14445354
title: A survey of image labelling for computer vision applications

description: Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.
DOI: 10.6084/m9.figshare.11603553
title: Nonlinear separation methods and applications for vector equilibrium problems using improvement sets

description: In this paper, the image space analysis is applied to investigate a vector equilibrium problem using improvement sets and with matrix inequality constraints. First, a nonlinear scalar regular weak separation function is constructed by using the oriented distance function and the norm function. Then, a global saddle-point condition for a generalized Lagrange function is investigated. It is shown that the existence of a saddle point is equivalent to a nonlinear separation of two suitable subsets in the image space. Furthermore, a gap function and an error bound are obtained in terms of the nonlinear scalar regular weak separation function under suitable assumptions. As applications, an optimality condition, a gap function and an error bound for a strategic game with vector payoffs are also given.
DOI: 10.6084/m9.figshare.11603553.v1
title: Nonlinear separation methods and applications for vector equilibrium problems using improvement sets

description: In this paper, the image space analysis is applied to investigate a vector equilibrium problem using improvement sets and with matrix inequality constraints. First, a nonlinear scalar regular weak separation function is constructed by using the oriented distance function and the norm function. Then, a global saddle-point condition for a generalized Lagrange function is investigated. It is shown that the existence of a saddle point is equivalent to a nonlinear separation of two suitable subsets in the image space. Furthermore, a gap function and an error bound are obtained in terms of the nonlinear scalar regular weak separation function under suitable assumptions. As applications, an optimality condition, a gap function and an error bound for a strategic game with vector payoffs are also given.
DOI: 10.15151/esrf-es-562173519
title: The SAXS-SANS platform within the Partnership for Structural Biology (PSB)

description: This proposal seeks time on BM29 to allow joint SAXS and SANS measurements to be carried out as part of the PSB SAXS/SANS platform. The BAG will service this user platform by allowing the allocation of SAXS beamtime directly after SANS measurements so that full advantange of co-location of SAXS and SANS facilities of the PSB can be provided to users. The scientific case for each joint request is evaluated by the ILL review committees of its Scientific Council and the allocation of time for successful proposals coordinated between the ILL and ESRF user offices.
DOI: 10.5281/zenodo.5534209
title: Self-Strain-Sensing: Comparison of Contact Materials

description: These Datasets were used to compare different contacting methods for the development of Self-Strain-Sensing CFRP parts.
DOI: 10.5281/zenodo.5534208
title: Self-Strain-Sensing: Comparison of Contact Materials

description: These Datasets were used to compare different contacting methods for the development of Self-Strain-Sensing CFRP parts.
DOI: 10.6084/m9.figshare.11704236.v9
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14825352
title: Aldosterone and corticosterone of a larval amphibian exposed to increased salinity

description: We exposed larval leopard frogs to increased salinity and RU486 (a glucocorticoid receptor antagonist) and tested their influence on waterborne corticosterone (baseline and stress-induced) and aldosterone.
DOI: 10.6084/m9.figshare.14039385.v1
title: Estimation of Impulse Response Functions When Shocks are Observed at a Higher Frequency than Outcome Variables*

description: This paper proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions (IRFs) in a setup where (<i>i</i>) the shock of interest is observed, (<i>ii</i>) the impact variable of interest is observed at a <i>lower</i> frequency (as a temporally aggregated or sequentially sampled variable), (<i>iii</i>) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (<i>iv</i>) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The usefulness of MFDL estimator is then illustrated in three empirical applications: (<i>i</i>) the daily pass-through of shocks to crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency, (<i>ii</i>) the impact of shocks to global investors’ risk appetite on global capital flows, and (<i>iii</i>) the impact of monetary policy shocks on real activity.
DOI: 10.6084/m9.figshare.11704236.v10
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11704236.v11
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14394343.v1
title: The PCDID Approach: Difference-in-Differences when Trends are Potentially Unparallel and Stochastic

description: We develop a class of regression-based estimators, called Principal Components Differ-ence-in-Differences estimators (PCDID), for treatment effect estimation. Analogous to a control function approach, PCDID uses factor proxies constructed from control units to control for unobserved trends, assuming that the unobservables follow an interactive effects structure. We clarify the conditions under which the estimands in this regression-based approach represent useful causal parameters of interest. We establish consistency and asymptotic normality results of PCDID estimators under minimal assumptions on the specification of time trends. The PCDID approach is illustrated in an empirical exercise that examines the effects of welfare waiver programs on welfare caseloads in the US.
DOI: 10.6084/m9.figshare.14484571.v1
title: Binary Conditional Forecasts*

description: While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this paper, we merge elements from the literature on the construction and implementation of conditional forecasts with the literature on forecasting binary variables. We use the Qual-VAR [Dueker (2005)], whose joint VAR-probit structure allows us to form conditional forecasts of the latent variable which can then be used to form probabilistic forecasts of the binary variable. We apply the model to forecasting recessions in real-time and investigate the role of monetary and oil shocks on the likelihood of two U.S. recessions.
DOI: 10.6084/m9.figshare.14604871.v1
title: A Structural Model of Homophily and Clustering in Social Networks

description: I develop and estimate a structural model of network formation with heterogeneous players and latent community structure, whose equilibrium homophily and clustering levels match those usually observed in real-world social networks. Players belong to communities unobserved by the econometrician and have community-specific payoffs, allowing preferences to have a bias for similar people. Players meet sequentially and decide whether to form bilateral links, after receiving a random matching shock. The model converges to a hierarchical exponential family random graph. Using school friendship network data from Add Health, I estimate the posterior distribution of parameters and unobserved heterogeneity, detecting high levels of racial homophily and payoff heterogeneity across communities. The posterior predictions of sufficient statistics show that the model is able to replicate the homophily levels and the aggregate clustering of the observed network, in contrast with standard exponential family network models without community structure.
DOI: 10.6084/m9.figshare.14664934
title: Bayesian Inference in Common Microeconometric Models With Massive Datasets by Double Marginalized Subsampling

description: Bayesian inference with a large dataset is computationally intensive, as Markov chain Monte Carlo simulation requires a complete scan of the dataset for each proposed parameter update. To reduce the number of data points evaluated at each iteration of posterior simulation, we develop a double marginalized subsampling method, which is applicable to a wide array of microeconometric models including Tobit, Probit, regressions with non-Gaussian errors, heteroscedasticity and stochastic volatility, hierarchical longitudinal models, time-varying-parameter regressions, Gaussian mixtures, etc. We also provide an extension to double pseudo-marginalized subsampling, which has more applications beyond conditionally conjugate models. With rank-one update of the cumulative statistics, both methods target the exact posterior distribution, from which a parameter draw can be obtained with every single observation. Simulation studies demonstrate the statistical and computational efficiency of the marginalized sampler. The methods are also applied to a real-world massive dataset on the incidentally truncated mortgage rates.
DOI: 10.6084/m9.figshare.14664934.v2
title: Bayesian Inference in Common Microeconometric Models With Massive Datasets by Double Marginalized Subsampling

description: Bayesian inference with a large dataset is computationally intensive, as Markov chain Monte Carlo simulation requires a complete scan of the dataset for each proposed parameter update. To reduce the number of data points evaluated at each iteration of posterior simulation, we develop a double marginalized subsampling method, which is applicable to a wide array of microeconometric models including Tobit, Probit, regressions with non-Gaussian errors, heteroscedasticity and stochastic volatility, hierarchical longitudinal models, time-varying-parameter regressions, Gaussian mixtures, etc. We also provide an extension to double pseudo-marginalized subsampling, which has more applications beyond conditionally conjugate models. With rank-one update of the cumulative statistics, both methods target the exact posterior distribution, from which a parameter draw can be obtained with every single observation. Simulation studies demonstrate the statistical and computational efficiency of the marginalized sampler. The methods are also applied to a real-world massive dataset on the incidentally truncated mortgage rates.
DOI: 10.6084/m9.figshare.14664934.v1
title: Bayesian Inference in Common Microeconometric Models with Massive Datasets by Double Marginalized Subsampling

description: Bayesian inference with a large dataset is computationally intensive, as Markov chain Monte Carlo simulation requires a complete scan of the dataset for each proposed parameter update. To reduce the number of data points evaluated at each iteration of posterior simulation, we develop a double marginalized subsampling method, which is applicable to a wide array of microeconometric models including Tobit, Probit, regressions with non-Gaussian errors, heteroscedasticity and stochastic volatility, hierarchical longitudinal models, time-varying-parameter regressions, Gaussian mixtures, etc. We also provide an extension to double pseudo-marginalized subsampling, which has more applications beyond conditionally conjugate models. With rank-one update of the cumulative statistics, both methods target the exact posterior distribution, from which a parameter draw can be obtained with every single observation. Simulation studies demonstrate the statistical and computational efficiency of the marginalized sampler. The methods are also applied to a real-world massive dataset on the incidentally truncated mortgage rates.
DOI: 10.6084/m9.figshare.12849704
title: Mean-Structure and Autocorrelation Consistent Covariance Matrix Estimation

description: We consider estimation of the asymptotic covariance matrix in nonstationary time series. A nonparametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points (CPs) is proposed. It is algorithmically fast because neither a search for CPs, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally efficient. It is, therefore, useful in many statistical procedures, for example, CPs detection and construction of simultaneous confidence bands of trends. Empirical studies on four stock market indices are also discussed.
DOI: 10.6084/m9.figshare.12849704.v2
title: Mean-Structure and Autocorrelation Consistent Covariance Matrix Estimation

description: We consider estimation of the asymptotic covariance matrix in nonstationary time series. A nonparametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points (CPs) is proposed. It is algorithmically fast because neither a search for CPs, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally efficient. It is, therefore, useful in many statistical procedures, for example, CPs detection and construction of simultaneous confidence bands of trends. Empirical studies on four stock market indices are also discussed.
DOI: 10.6084/m9.figshare.12849704.v1
title: Mean-structure and Autocorrelation Consistent Covariance Matrix Estimation

description: We consider estimation of the asymptotic covariance matrix in non-stationary time series. A non-parametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points is proposed. It is algorithmically fast because neither a search for change points, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally efficient. It is, therefore, useful in many statistical procedures, e.g., change points detection and construction of simultaneous confidence bands of trends. Empirical studies on four stock market indices are also discussed.
DOI: 10.5517/ccdc.csd.cc242xy3
title: CCDC 1969025: Experimental Crystal Structure Determination

description: Related Article: Ye Bai, Meili Zhang, Botao Wang, Yixia Ren, JiJiang Wang, Huali Cui, Xiaogang Yang|2021|Cleaner Engineering and Technology|5|100276|doi:10.1016/j.clet.2021.100276
DOI: 10.5517/ccdc.csd.cc26n89g
title: CCDC 2045265: Experimental Crystal Structure Determination

description: Related Article: Tanner C. Jankins, Raul Martin-Montero, Phillippa Cooper, Ruben Martin, Keary M. Engle|2021|J.Am.Chem.Soc.|143|14981|doi:10.1021/jacs.1c07162
DOI: 10.6084/m9.figshare.16699717
title: Title

description: Description
DOI: 10.6084/m9.figshare.16699717.v1
title: Title

description: Description
DOI: 10.5517/ccdc.csd.cc27r5z5
title: CCDC 2077867: Experimental Crystal Structure Determination

description: Related Article: Aihui Liang, Yao Gao, Reza Asadpour, Zitang Wei, Blake P. Finkenauer, Linrui Jin, Jiaqi Yang, Kang Wang, Ke Chen, Peilin Liao, Chenhui Zhu, Libai Huang, Bryan W. Boudouris, Muhammad Ashraf Alam, Letian Dou|2021|J.Am.Chem.Soc.|143|15215|doi:10.1021/jacs.1c06337
DOI: 10.5517/ccdc.csd.cc27r629
title: CCDC 2077870: Experimental Crystal Structure Determination

description: Related Article: Aihui Liang, Yao Gao, Reza Asadpour, Zitang Wei, Blake P. Finkenauer, Linrui Jin, Jiaqi Yang, Kang Wang, Ke Chen, Peilin Liao, Chenhui Zhu, Libai Huang, Bryan W. Boudouris, Muhammad Ashraf Alam, Letian Dou|2021|J.Am.Chem.Soc.|143|15215|doi:10.1021/jacs.1c06337