cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7056401.v3
title: Semiparametric Regression Using Variational Approximations

description: Semiparametric regression offers a flexible framework for modeling nonlinear relationships between a response and covariates. A prime example are generalized additive models (GAMs) where splines (say) are used to approximate nonlinear functional components in conjunction with a quadratic penalty to control for overfitting. Estimation and inference are then generally performed based on the penalized likelihood, or under a mixed model framework. The penalized likelihood framework is fast but potentially unstable, and choosing the smoothing parameters needs to be done externally using cross-validation, for instance. The mixed model framework tends to be more stable and offers a natural way for choosing the smoothing parameters, but for nonnormal responses involves an intractable integral. In this article, we introduce a new framework for semiparametric regression based on variational approximations (VA). The approach possesses the stability and natural inference tools of the mixed model framework, while achieving computation times comparable to using penalized likelihood. Focusing on GAMs, we derive fully tractable variational likelihoods for some common response types. We present several features of the VA framework for inference, including a variational information matrix for inference on parametric components, and a closed-form update for estimating the smoothing parameter. We demonstrate the consistency of the VA estimates, and an asymptotic normality result for the parametric component of the model. Simulation studies show the VA framework performs similarly to and sometimes better than currently available software for fitting GAMs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7433033.v2
title: Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study

description: In comparing two treatments via a randomized clinical trial, the analysis of covariance (ANCOVA) technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its simple two sample estimation counterpart. Unfortunately, when the ANCOVA model is nonlinear, the resulting estimator is generally not consistent. Recently, various nonparametric alternatives to the ANCOVA, such as the augmentation methods, have been proposed to estimate the treatment effect by adjusting the covariates. However, the properties of these alternatives have not been studied in the presence of treatment allocation imbalance. In this article, we take a different approach to explore how to improve the precision of the naive two-sample estimate even when the observed distributions of baseline covariates between two groups are dissimilar. Specifically, we derive a bias-adjusted estimation procedure constructed from a conditional inference principle via relevant ancillary statistics from the observed covariates. This estimator is shown to be asymptotically equivalent to an augmentation estimator under the unconditional setting. We utilize the data from a clinical trial for evaluating a combination treatment of cardiovascular diseases to illustrate our findings.
DOI: 10.6084/m9.figshare.7015256.v1
title: Distance Metrics for Measuring Joint Dependence with Application to Causal Inference

description: Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to quantify joint dependence among d≥2 random vectors. We introduce the high-order distance covariance to measure the so-called Lancaster interaction dependence. The joint distance covariance is then defined as a linear combination of pairwise distance covariances and their higher-order counterparts which together completely characterize mutual independence. We further introduce some related concepts including the distance cumulant, distance characteristic function, and rank-based distance covariance. Empirical estimators are constructed based on certain Euclidean distances between sample elements. We study the large-sample properties of the estimators and propose a bootstrap procedure to approximate their sampling distributions. The asymptotic validity of the bootstrap procedure is justified under both the null and alternative hypotheses. The new metrics are employed to perform model selection in causal inference, which is based on the joint independence testing of the residuals from the fitted structural equation models. The effectiveness of the method is illustrated via both simulated and real datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7433114.v1
title: Dynamic Tensor Clustering

description: Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. There is also a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we propose a new dynamic tensor clustering method that works for a general-order dynamic tensor, and enjoys both strong statistical guarantee and high computational efficiency. Our proposal is based on a new structured tensor factorization that encourages both sparsity and smoothness in parameters along the specified tensor modes. Computationally, we develop a highly efficient optimization algorithm that benefits from substantial dimension reduction. Theoretically, we first establish a nonasymptotic error bound for the estimator from the structured tensor factorization. Built upon this error bound, we then derive the rate of convergence of the estimated cluster centers, and show that the estimated clusters recover the true cluster structures with high probability. Moreover, our proposed method can be naturally extended to co-clustering of multiple modes of the tensor data. The efficacy of our method is illustrated through simulations and a brain dynamic functional connectivity analysis from an autism spectrum disorder study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7780640.v3
title: Scaled von Mises–Fisher Distributions and Regression Models for Paleomagnetic Directional Data

description: We propose a new distribution for analyzing paleomagnetic directional data, that is, a novel transformation of the von Mises–Fisher distribution. The new distribution has ellipse-like symmetry, as does the Kent distribution; however, unlike the Kent distribution the normalizing constant in the new density is easy to compute and estimation of the shape parameters is straightforward. To accommodate outliers, the model also incorporates an additional shape parameter, which controls the tail-weight of the distribution. We also develop a general regression model framework that allows both the mean direction and the shape parameters of the error distribution to depend on covariates. The proposed regression procedure is shown to be equivariant with respect to the choice of coordinate system for the directional response. To illustrate, we analyses paleomagnetic directional data from the GEOMAGIA50.v3 database. We predict the mean direction at various geological time points and show that there is significant heteroscedasticity present. It is envisaged that the regression structures and error distribution proposed here will also prove useful when covariate information is available with (i) other types of directional response data; and (ii) square-root transformed compositional data of general dimension. Supplementary materials for this article are 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.7756823.v1
title: Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment

description: The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this paper, we extend Bayesian methods to incorporate statistical models for the error that is incurred in the numerical solution of the physical governing equations. This enables full uncertainty quantification within a principled computation-precision trade-off, in contrast to the over-confident inferences that are obtained when all sources of numerical error are ignored. The method is cast within a sequential Monte Carlo framework and an optimised implementation is provided in Python.
DOI: 10.6084/m9.figshare.7433192.v2
title: Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data

description: In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7780637.v2
title: Design, Identification, and Sensitivity Analysis for Patient Preference Trials

description: Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact. 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.7063802.v1
title: Efficient Signal Inclusion With Genomic Applications

description: This article addresses the challenge of efficiently capturing a high proportion of true signals for subsequent data analyses when sample sizes are relatively limited with respect to data dimension. We propose the signal missing rate (SMR) as a new measure for false-negative control to account for the variability of false-negative proportion. Novel data-adaptive procedures are developed to control SMR without incurring many unnecessary false positives under dependence. We justify the efficiency and adaptivity of the proposed methods via theory and simulation. The proposed methods are applied to GWAS on human height to effectively remove irrelevant single nucleotide polymorphisms (SNPs) while retaining a high proportion of relevant SNPs for subsequent polygenic analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7056404.v1
title: Covariate Information Matrix for Sufficient Dimension Reduction

description: Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regression function. CIM is implemented via eigen-decomposition of a matrix estimated with a previously developed efficient nonparametric density estimation technique. We also propose a bootstrap-based diagnostic plot for estimating the dimension of the CS. Results of simulations and real data applications demonstrate superior or competitive performance of CIM compared to that of some other SDR methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7075109.v3
title: Uncertainty quantification for computer models with spatial output using calibration-optimal bases

description: The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emulators to predict the output for calibration. We introduce the ‘terminal case’, in which the model cannot reproduce observations to within model discrepancy, and for which standard calibration methods in UQ fail to give sensible results. We show that even when there is no such issue with the model, the standard decomposition on the outputs can and usually does lead to a terminal case analysis. We present a simple test to allow a practitioner to establish whether their experiment will result in a terminal case analysis, and a methodology for defining calibration-optimal bases that avoid this whenever it is not inevitable. We present the optimal rotation algorithm for doing this, and demonstrate its efficacy for an idealised example for which the usual principal component methods fail. We apply these ideas to the CanAM4 model to demonstrate the terminal case issue arising for climate models. We discuss climate model tuning and the estimation of model discrepancy within this context, and show how the optimal rotation algorithm can be used in developing practical climate model tuning tools.
DOI: 10.6084/m9.figshare.8059646.v1
title: Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

description: Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. 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.7756817.v1
title: Downstream Effects of Upstream Causes

description: The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States’ most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal <i>g</i>-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll <i>a</i>—a proxy for algal production. Publicly available data from North Carolina’s Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll <i>a</i> levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric <i>g</i>-formula, a marginal structural model, and a structural nested model indicate that chlorophyll <i>a</i> concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. 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.14963766.v1
title: Pharmacological interventions for COVID-19: a systematic review of observational studies and clinical trials

description: <b>Introduction</b>: Currently, there is no approved therapeutic entity for coronavirus disease 2019 (COVID-19) and clinicians are primarily relying on drug repurposing. However, findings across studies are widely disparate, making it difficult to draw firm conclusions. Since clinicians need accurate evidence to treat COVID-19, this manuscript systematically analyzed the published and ongoing studies evaluating the pharmacological interventions for COVID-19. <b>Areas Covered</b>: A systematic search of observational studies and Clinical Trials on the treatment and prevention of COVID-19 was performed by using various databases from inception to 2 December 2020. <b>Expert Opinion</b>: A total of 460 studies met the inclusion criteria. Of these, 37 were research studies, 386 were ongoing trials, and 37 were completed trials. Anti-virals, steroids, anti-malarial, plasma exchange, and monoclonal antibodies were the most common treatment modalities used alone or in combination in these studies. However, tocilizumab, plasma exchange, and steroids have shown significant improvements in patient’s clinical and radiological status. Tocilizumab reported minimum hospital stay of 2 days along with maximum recovery and patient’s stability rate. Existing literature demonstrate promising results of tocilizumab, plasma exchange, and steroids among COVID-19 patients. Nevertheless, these studies are accompanied by several methodological disparities which should be considered while interpreting the results.
DOI: 10.6084/m9.figshare.14413524.v1
title: Self-medication Behavior with antibiotics: a national cross-sectional survey in Sri Lanka

description: Antibiotic self-medication is common in low- and middle-income countries. This study aimed to evaluate the Sri Lankan public’s knowledge about and attitudes toward antibiotic use and self-medication, and factors associated with self-medication. A national cross-sectional, interviewer-administered, survey of a random household sample (N = 1100) was conducted. Factor analysis of the attitudinal items was conducted to investigate the factors associated with antibiotic self-medication. A response rate of ninety-one percent (n = 998) responded. Knowledge about antibiotics was poor (mean = 12.5; SD = 3.5; (scale 0–27)). Half had previously used an antibiotic once in the past three months. About 11% (108/998) had self-medicated the last time they took antibiotics; mostly obtained from a pharmacy (82%; 89/108). Three attitudinal factors were obtained, explaining 56.1% of the variance. Respondents were less likely to self-medicate if they did not support <i>ease of access to antibiotics from pharmacies</i> (<i>p</i>situational use of antibiotics (<i>p</i>= 0.001); supported <i>appropriate use of antibiotics</i> (<i>p</i>= 0.003); and had greater <i>knowledge about prescription requirements</i> for antibiotics (<i>p</i>= 0.004). There is limited knowledge about, and a high rate of self-medication with antibiotics.Factors contributing to self-medication could be addressed with appropriate public education campaigns, and policy changes.
DOI: 10.6084/m9.figshare.14329092
title: Participation Measures for Infants and Toddlers Aged Birth to 23 Months: A Systematic Review

description: Participation is a key health outcome; however little is known specifically about how to measure participation of infants and toddlers. The aim of this review was to identify and examine the psychometric properties of participation measures for infants and toddlers aged birth to 23 months according to family of Participation-Related Constructs (fPRC). Four electronic databases were searched to identify (i) measures available in English, (ii) that quantify at least one participation concept of ‘attendance’ and/or ‘involvement’ according to the fPRC, (iii) with psychometric data for infants or toddlers aged from birth to 23 months published in a full text, peer reviewed journal. Psychometric quality was examined using the COSMIN checklist. Four measures met the inclusion criteria: Child Engagement in Daily Life (CEDL) measure, Daily Activities of Infants Scale (DAIS), Test of Playfulness (ToP), and Young Children’s Participation and Environment Measure (YC-PEM). All measured ‘attendance’ and three also measured ‘involvement’. Measures showed unknown to moderate validity and unknown to moderate reliability, with the CEDL reporting the strongest psychometric properties. There are few measures that evaluate infant and toddler participation. Further research is needed to develop psychometrically sound participation measures that evaluate ‘attendance’ and ‘involvement’ for this population.