cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3798153.v1
title: A Semiparametric Single-Index Risk Score Across Populations

description: We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-index model is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and crucially, the single-index itself, having to do with the diet score, is common to all diseases and populations, and the nonparametrically estimated functions of the single-index are the same up to a scale parameter. Using B-splines with an increasing number of knots, we develop a method to solve the problem, and display its asymptotic theory. An application to the NIH-AARP Study of Diet and Health is described, where we show the advantages of using multiple diseases and populations simultaneously rather than one at a time in understanding the effect of increased Milk consumption. Simulations illustrate the properties of the methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14672019
title: cdg2018-bip-scz

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.21227/7hbg-kd47
title: Urb3DCD : Urban Point Clouds Simulated Dataset for 3D Change Detection

description: In a context of rapid urban evolution, there is a need of surveying cities. Nowadays methodologies such as machine learning require large amount of data to train, hence the necessity of providing public dataset allowing to follow up urban evolution. While most of changes occurs in the vertical axis, there is no public change detection dataset composed of 3D point clouds and directly annotated according to the change at point level. With this dataset we aim to fill this gap because we believe that 3D point clouds bring some supplementary information on height that seems useful in the context of building change extraction as main modifications occur on the vertical axis. Furthermore, spectral variability of a same object over time, difference of viewing angles between acquisition of 2D images, perspective and distortion effects could complicate change retrieval based on 2D data. Thus, this dataset is composed of bi-temporal pairs of point clouds annotated according to the change. Point clouds are acquired via a simulator of aerial LiDAR Survey over dense urban areas. Changes are also introduced by the simulator. The dataset is composed of tricky low resolution point clouds. A training, validation and testing set is furnished. Notice that this dataset will comport several versions, this one is the first one.
DOI: 10.5523/bris.260f3wwcet7lc22ztrzkptmi55
title: Supporting data for A nonlinear frequency-dependent spring-mass model for estimating loading caused by rhythmic human jumping

description: This data in this repository corresponds to experimental data collected for each test subject (8 total) : ground reaction force, centre-of-mass displacement and time for White, R.E., Macdonald, J.H.G., Alexander, N.A. (2021) A nonlinear frequency-dependent spring-mass model for estimating loading caused by rhythmic human jumping, Engineering Structures, https://doi.org/10.1016/j.engstruct.2021.112229, The csv and excel files corresponds to each test subject's acquired dynamics
DOI: 10.6084/m9.figshare.14672034
title: cdg2019

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.6084/m9.figshare.6220361.v3
title: Factor and Idiosyncratic Empirical Processes

description: The distributions of the common and idiosyncratic components for an individual variable are important in forecasting and applications. However, they are not identified with low-dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the common and idiosyncratic components can be estimated consistently. Based on the estimated common and idiosyncratic components, we construct the empirical processes for estimation of the distribution functions of the common and idiosyncratic components. We prove that the two empirical processes are oracle efficient when <i>T</i> = <i>o</i>(<i>p</i>) where <i>p</i> and <i>T</i> are the dimension and sample size, respectively. This demonstrates that the factor and idiosyncratic empirical processes behave as well as the empirical processes pretending that the common and idiosyncratic components for an individual variable are directly observable. Based on this oracle property, we construct simultaneous confidence bands (SCBs) for the distributions of the common and idiosyncratic components. For the first-order consistency of the estimated distribution functions, T=o(p) suffices. Extensive simulation studies check that the estimated bands have good coverage frequencies. Our real data analysis shows that the common-component distribution has a structural change during the crisis in 2008, while the idiosyncratic-component distribution does not change much. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6406094.v3
title: Toward Computerized Efficient Estimation in Infinite-Dimensional Models

description: Despite the risk of misspecification they are tied to, parametric models continue to be used in statistical practice because they are simple and convenient to use. In particular, efficient estimation procedures in parametric models are easy to describe and implement. Unfortunately, the same cannot be said of semiparametric and nonparametric models. While the latter often reflect the level of available scientific knowledge more appropriately, performing efficient inference in these models is generally challenging. The efficient influence function is a key analytic object from which the construction of asymptotically efficient estimators can potentially be streamlined. However, the theoretical derivation of the efficient influence function requires specialized knowledge and is often a difficult task, even for experts. In this article, we present a novel representation of the efficient influence function and describe a numerical procedure for approximating its evaluation. The approach generalizes the nonparametric procedures of Frangakis et al. and Luedtke, Carone, and van der Laan to arbitrary models. We present theoretical results to support our proposal and illustrate the method in the context of several semiparametric problems. The proposed approach is an important step toward automating efficient estimation in general statistical models, thereby rendering more accessible the use of realistic models in statistical analyses. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6834872.v4
title: Extremiles: A New Perspective on Asymmetric Least Squares

description: Quantiles and expectiles of a distribution are found to be useful descriptors of its tail in the same way as the median and mean are related to its central behavior. This article considers a valuable alternative class to expectiles, called extremiles, which parallels the class of quantiles and includes the family of expected minima and expected maxima. The new class is motivated via several angles, which reveals its specific merits and strengths. Extremiles suggest better capability of fitting both location and spread in data points and provide an appropriate theory that better displays the interesting features of long-tailed distributions. We discuss their estimation in the range of the data and beyond the sample maximum. A number of motivating examples are given to illustrate the utility of estimated extremiles in modeling noncentral behavior. There is in particular an interesting connection with coherent measures of risk protection. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6826937.v3
title: Bayesian Uncertainty Directed Trial Designs

description: Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information at the end of the study. The approach can be applied to several designs, ranging from early stage multi-arm trials to biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of the patient allocation proportion to treatments, and illustrate the finite-sample operating characteristics of BUD designs through examples, including multi-arm trials, biomarker-stratified trials, and trials with multiple co-primary endpoints. 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.6938477.v2
title: Quantile-Regression Inference With Adaptive Control of Size

description: Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This article develops a new estimate of the asymptotic variance of regression quantiles that leads any resulting Wald-type test or confidence region to behave as well in large samples as its infeasible counterpart in which the true conditional response densities are embedded. We give explicit guidance on implementing the new variance estimator to control adaptively the size of any resulting Wald-type test. Monte Carlo evidence indicates the potential of our approach to deliver powerful tests of heterogeneity of quantile treatment effects in covariates with good size performance over different quantile levels, data-generating processes, and sample sizes. We also include an empirical example. Supplementary material is available online.
DOI: 10.6084/m9.figshare.7432907.v2
title: Automatic Detection and Uncertainty Quantification of Landmarks on Elastic Curves

description: A population quantity of interest in statistical shape analysis is the location of landmarks, which are points that aid in reconstructing and representing shapes of objects. We provide an automated, model-based approach to inferring landmarks given a sample of shape data. The model is formulated based on a linear reconstruction of the shape, passing through the specified points, and a Bayesian inferential approach is described for estimating unknown landmark locations. The question of how many landmarks to select is addressed in two different ways: (1) by defining a criterion-based approach and (2) joint estimation of the number of landmarks along with their locations. Efficient methods for posterior sampling are also discussed. We motivate our approach using several simulated examples, as well as data obtained from applications in computer vision, biology, and medical imaging. 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.6447905.v2
title: Skinny Gibbs: A Consistent and Scalable Gibbs Sampler for Model Selection

description: We consider the computational and statistical issues for high-dimensional Bayesian model selection under the Gaussian spike and slab priors. To avoid large matrix computations needed in a standard Gibbs sampler, we propose a novel Gibbs sampler called “Skinny Gibbs” which is much more scalable to high-dimensional problems, both in memory and in computational efficiency. In particular, its computational complexity grows only linearly in <i>p</i>, the number of predictors, while retaining the property of strong model selection consistency even when <i>p</i> is much greater than the sample size <i>n</i>. The present article focuses on logistic regression due to its broad applicability as a representative member of the generalized linear models. We compare our proposed method with several leading variable selection methods through a simulation study to show that Skinny Gibbs has a strong performance as indicated by our theoretical work. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6843794.v3
title: On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments

description: We investigate the behavior of the Lasso for selecting invalid instruments in linear instrumental variables models for estimating causal effects of exposures on outcomes, as proposed recently by Kang et al. Invalid instruments are such that they fail the exclusion restriction and enter the model as explanatory variables. We show that for this setup, the Lasso may not consistently select the invalid instruments if these are relatively strong. We propose a median estimator that is consistent when less than 50% of the instruments are invalid, and its consistency does not depend on the relative strength of the instruments, or their correlation structure. We show that this estimator can be used for adaptive Lasso estimation, with the resulting estimator having oracle properties. The methods are applied to a Mendelian randomization study to estimate the causal effect of body mass index (BMI) on diastolic blood pressure, using data on individuals from the UK Biobank, with 96 single nucleotide polymorphisms as potential instruments for BMI. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7445648.v3
title: Estimating and Testing Vaccine Sieve Effects Using Machine Learning

description: When available, vaccines are an effective means of disease prevention. Unfortunately, efficacious vaccines have not yet been developed for several major infectious diseases, including HIV and malaria. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the pathogen of interest, which can guide subsequent vaccine development and deployment. In sieve analyses, the effect of the vaccine on the cumulative incidence corresponding to each of several possible genotypes is often assessed within a competing risks framework. In the context of clinical trials, the estimators employed in these analyses generally do not account for covariates, even though the latter may be predictive of the study endpoint or censoring. Motivated by two recent preventive vaccine efficacy trials for HIV and malaria, we develop new methodology for vaccine sieve analysis. Our approach offers improved validity and efficiency relative to existing approaches by allowing covariate adjustment through ensemble machine learning. We derive results that indicate how to perform statistical inference using our estimators. Our analysis of the HIV and malaria trials shows markedly increased precision—up to doubled efficiency in both trials—under more plausible assumptions compared with standard methodology. Our findings provide greater evidence for vaccine sieve effects in both trials. 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.6220325.v4
title: Robust Bayesian Inference via Coarsening

description: The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the model generates data close to the observed data, in a distributional sense. When closeness is defined in terms of relative entropy, the resulting “coarsened” posterior can be approximated by simply tempering the likelihood—that is, by raising the likelihood to a fractional power—thus, inference can usually be implemented via standard algorithms, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6843785.v4
title: Multiple Testing When Many <i>p</i>-Values are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions

description: In the evaluation of treatment effects, it is of major policy interest to know if the treatment is beneficial for some and harmful for others, a phenomenon known as qualitative interaction. We formulate this question as a multiple testing problem with many conservative null <i>p</i>-values, in which the classical multiple testing methods may lose power substantially. We propose a simple technique—conditioning—to improve the power. A crucial assumption we need is uniform conservativeness, meaning for any conservative <i>p</i>-value <i>p</i>, the conditional distribution (<i>p</i>/τ) | <i>p</i> ⩽ τ is stochastically larger than the uniform distribution on (0, 1) for any τ. We show this property holds for one-sided tests in a one-dimensional exponential family (e.g., testing for qualitative interaction) as well as testing |μ| ⩽ η using a statistic <i>Y</i> ∼ N(μ, 1) (e.g., testing for practical importance with threshold η). We propose an adaptive method to select the threshold τ. Our theoretical and simulation results suggest that the proposed tests gain significant power when many <i>p</i>-values are uniformly conservative and lose little power when no <i>p</i>-value is uniformly conservative. We apply our method to two educational intervention datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7432541.v3
title: Diagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method

description: Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VFs) acquired at regular intervals. VF data are characterized by a complex spatiotemporal structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status. We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time. We show that our new method provides novel insight into vision loss that improves diagnosis of glaucoma progression using data from the Vein Pulsation Study Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are presented, showing the proposed methodology is preferred over existing spatial methods for VF data. Supplementary materials for this article are available online and the method is implemented in the R package womblR.
DOI: 10.15468/3hgv9n
title: New species of Hybristodryinus from mid-Cretaceous Myanmar amber (Hymenoptera, Dryinidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Wang, Zhen, Olmi, Massimo, Shih, Chungkun, Ren, Dong, Gao, Taiping (2021): New species of Hybristodryinus from mid-Cretaceous Myanmar amber (Hymenoptera, Dryinidae). Cretaceous Research 125, No. 104861: 1-6, DOI: 10.1016/j.cretres.2021.104861
DOI: 10.6084/m9.figshare.14672082
title: mdd2013

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.6084/m9.figshare.7823465.v2
title: A Hierarchical Framework for Correcting Under-Reporting in Count Data

description: Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, relying only on an informative prior distribution for the mean reporting rate to supplement the partial information in the data. Covariates are used to inform both the true count-generating process and the under-reporting mechanism, while also allowing for complex spatio-temporal structures. We present several sensitivity analyses based on simulation experiments to aid the elicitation of the prior distribution for the mean reporting rate and decisions relating to the inclusion of covariates. Both prior and posterior predictive model checking are presented, as well as a critical evaluation of the approach. 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.7445672.v2
title: Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts

description: Generalized additive model (GAM) forms offer a flexible approach to capturing marginal variation. Such forms are used here to represent distributional variation in extreme values and presented in terms of spatio-temporal variation, which is often evident in environmental processes. A two-stage procedure is proposed that identifies extreme values as exceedances of a high threshold, which is defined as a fixed quantile and estimated by quantile regression. Excesses of the threshold are modelled with the generalized Pareto distribution (GPD). GAM forms are adopted for the threshold and GPD parameters, and directly estimated—in particular smoothing parameters—by restricted maximum likelihood, which provides an objective and relatively fast method of inference. The GAM models are used to produce return level maps for extreme wind gust speeds over the United States, which show extreme quantiles of the distribution of annual maximum gust speeds. Supplementary materials for this article are available online.