cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13242435
title: Gaining Outlier Resistance With Progressive Quantiles: Fast Algorithms and Theoretical Studies

description: Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this article, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algorithms with implementation ease and guaranteed fast convergence. In particular, a new technique is proposed to alleviate the requirement on the starting point such that on regular datasets, the number of data resamplings can be substantially reduced. Based on combined statistical and computational treatments, we are able to perform nonasymptotic analysis beyond <i>M</i>-estimation. The obtained resistant estimators, though not necessarily globally or even locally optimal, enjoy minimax rate optimality in both low dimensions and high dimensions. Experiments in regression, classification, and neural networks show excellent performance of the proposed methodology at the occurrence of gross outliers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13242435.v3
title: Gaining Outlier Resistance With Progressive Quantiles: Fast Algorithms and Theoretical Studies

description: Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this article, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algorithms with implementation ease and guaranteed fast convergence. In particular, a new technique is proposed to alleviate the requirement on the starting point such that on regular datasets, the number of data resamplings can be substantially reduced. Based on combined statistical and computational treatments, we are able to perform nonasymptotic analysis beyond <i>M</i>-estimation. The obtained resistant estimators, though not necessarily globally or even locally optimal, enjoy minimax rate optimality in both low dimensions and high dimensions. Experiments in regression, classification, and neural networks show excellent performance of the proposed methodology at the occurrence of gross outliers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13373997
title: Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design

description: Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable gold-standard case definition. Instead, we leverage genetic traits that are protective against malaria but not against other illnesses, for example, the sickle cell trait, to identify vaccine efficacy in a randomized trial. Inspired by Mendelian randomization, we introduce Mendelian factorial design, a method that augments a randomized trial with genetic variation to produce a natural factorial experiment, which identifies vaccine efficacy under realistic assumptions. A robust, covariance adjusted estimation procedure is developed for estimating vaccine efficacy on the risk ratio and incidence rate ratio scales. Simulations suggest that our estimator has good performance whereas standard methods are systematically biased. We demonstrate that a combined estimator using both our proposed estimator and the standard approach yields significant improvements when the Mendelian factor is only weakly protective. Our method can be applied in vaccine and prevention trials of other childhood diseases that have no gold-standard case definition and known genetic risk factors. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13373997.v1
title: Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design

description: Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable gold-standard case definition. Instead, we leverage genetic traits that are protective against malaria but not against other illnesses, for example, the sickle cell trait, to identify vaccine efficacy in a randomized trial. Inspired by Mendelian randomization, we introduce Mendelian factorial design, a method that augments a randomized trial with genetic variation to produce a natural factorial experiment, which identifies vaccine efficacy under realistic assumptions. A robust, covariance adjusted estimation procedure is developed for estimating vaccine efficacy on the risk ratio and incidence rate ratio scales. Simulations suggest that our estimator has good performance whereas standard methods are systematically biased. We demonstrate that a combined estimator using both our proposed estimator and the standard approach yields significant improvements when the Mendelian factor is only weakly protective. Our method can be applied in vaccine and prevention trials of other childhood diseases that have no gold-standard case definition and known genetic risk factors. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13210554
title: Variational Bayes for High-Dimensional Linear Regression With Sparse Priors

description: We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility conditions on the design matrix, oracle inequalities are derived for the mean-field VB approximation, implying that it converges to the sparse truth at the optimal rate and gives optimal prediction of the response vector. The empirical performance of our algorithm is studied, showing that it works comparably well as other state-of-the-art Bayesian variable selection methods. We also numerically demonstrate that the widely used coordinate-ascent variational inference algorithm can be highly sensitive to the parameter updating order, leading to potentially poor performance. To mitigate this, we propose a novel prioritized updating scheme that uses a data-driven updating order and performs better in simulations. The variational algorithm is implemented in the R package sparsevb. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13210554.v2
title: Variational Bayes for High-Dimensional Linear Regression With Sparse Priors

description: We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility conditions on the design matrix, oracle inequalities are derived for the mean-field VB approximation, implying that it converges to the sparse truth at the optimal rate and gives optimal prediction of the response vector. The empirical performance of our algorithm is studied, showing that it works comparably well as other state-of-the-art Bayesian variable selection methods. We also numerically demonstrate that the widely used coordinate-ascent variational inference algorithm can be highly sensitive to the parameter updating order, leading to potentially poor performance. To mitigate this, we propose a novel prioritized updating scheme that uses a data-driven updating order and performs better in simulations. The variational algorithm is implemented in the R package sparsevb. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13242435.v2
title: Gaining Outlier Resistance With Progressive Quantiles: Fast Algorithms and Theoretical Studies

description: Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this article, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algorithms with implementation ease and guaranteed fast convergence. In particular, a new technique is proposed to alleviate the requirement on the starting point such that on regular datasets, the number of data resamplings can be substantially reduced. Based on combined statistical and computational treatments, we are able to perform nonasymptotic analysis beyond <i>M</i>-estimation. The obtained resistant estimators, though not necessarily globally or even locally optimal, enjoy minimax rate optimality in both low dimensions and high dimensions. Experiments in regression, classification, and neural networks show excellent performance of the proposed methodology at the occurrence of gross outliers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13607649
title: Extremile Regression

description: Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the families of spectral risk measures and concave distortion risk measures. This article provides the first detailed study exploring implications of the extremile terminology in a general setting of presence of covariates. We rely on local linear (least squares) check function minimization for estimating conditional extremiles and deriving the asymptotic normality of their estimators. We also extend extremile regression far into the tails of heavy-tailed distributions. Extrapolated estimators are constructed and their asymptotic theory is developed. Some applications to real data are provided.
DOI: 10.6084/m9.figshare.13607649.v1
title: Extremile Regression

description: Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the families of spectral risk measures and concave distortion risk measures. This article provides the first detailed study exploring implications of the extremile terminology in a general setting of presence of covariates. We rely on local linear (least squares) check function minimization for estimating conditional extremiles and deriving the asymptotic normality of their estimators. We also extend extremile regression far into the tails of heavy-tailed distributions. Extrapolated estimators are constructed and their asymptotic theory is developed. Some applications to real data are provided.
DOI: 10.6084/m9.figshare.13210554.v1
title: Variational Bayes for high-dimensional linear regression with sparse priors

description: We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility conditions on the design matrix, oracle inequalities are derived for the mean-field VB approximation, implying that it converges to the sparse truth at the optimal rate and gives optimal prediction of the response vector. The empirical performance of our algorithm is studied, showing that it works comparably well as other state-of-the-art Bayesian variable selection methods. We also numerically demonstrate that the widely used coordinate-ascent variational inference (CAVI) algorithm can be highly sensitive to the parameter updating order, leading to potentially poor performance. To mitigate this, we propose a novel prioritized updating scheme that uses a data-driven updating order and performs better in simulations. The variational algorithm is implemented in the R package sparsevb.
DOI: 10.6084/m9.figshare.13392645
title: Sensitivity Analysis via the Proportion of Unmeasured Confounding

description: In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby the sensitivity parameter is the “proportion of unmeasured confounding”: the proportion of units for whom the treatment is not as good as randomized even after conditioning on the observed covariates. We consider different assumptions on the probability of a unit being unconfounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study’s robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right heart catheterization. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13392645.v1
title: Sensitivity Analysis via the Proportion of Unmeasured Confounding

description: In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby the sensitivity parameter is the “proportion of unmeasured confounding”: the proportion of units for whom the treatment is not as good as randomized even after conditioning on the observed covariates. We consider different assumptions on the probability of a unit being unconfounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study’s robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right heart catheterization. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19357300
title: High-Dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity

description: Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a dataset consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of great scientific interest is to identify the active mediating loci out of the 385,882 ones. Houtepen et al. conducted 385,882 linear mediation analyses, in each of which one locus was considered, and identified three active mediating loci. More recently, van Kesteren and Oberski proposed a coordinate-wise mediation filter (CMF) and applied it to the same dataset. They identified five active mediating loci. Unfortunately, the three loci identified by Houtepen et al. are completely different from the five loci identified by van Kesteren and Oberski, probably because both Houtepen et al. and van Kesteren and Oberski did not consider all loci jointly in their analyses. The high dimensional DNA methylation loci indeed necessitate new techniques for identifying active mediating loci and testing the direct and indirect effects of the early life traumatic stress on later cortisol alteration. Motivated by the contradictory results in the aforementioned two scientific works, we develop a new estimating and testing procedure, and apply it to the same dataset as that analyzed by the two works. We identify three new loci: cg19230917, cg06422529 and cg03199124, and their effect sizes and <i>p</i>-values are 321.196 (<i>p</i>-value = 0.035965), 418.173 (<i>p</i>-value = 0.000234) and 471.865 (<i>p</i>-value = 0.001691), respectively. These three loci possess both reasonably neurobiological interpretations and statistically significant effects via our proposed tests. Based on our new procedure, we further confirm that the childhood trauma does not have significant direct effects on cortisol change—it only indirectly affects cortisol through DNA methylation, and the indirect effect is negative. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21084721
title: Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”

description: Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”
DOI: 10.6084/m9.figshare.21084721.v1
title: Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”

description: Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”
DOI: 10.6084/m9.figshare.19357300.v1
title: High-dimensional mediation analysis for selecting DNA methylation Loci mediating childhood trauma and cortisol stress reactivity*

description: Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. (2016) conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a data set consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of great scientific interest is to identify the active mediating loci out of the 385,882 ones. Houtepen et al. (2016) conducted 385,882 linear mediation analyses, in each of which one locus was considered, and identified three active mediating loci. More recently, van Kesteren & Oberski (2019) proposed a coordinate-wise mediation filter (CMF) and applied it to the same data set. They identified five active mediating loci. Unfortunately, the three loci identified by Houtepen et al. (2016) are completely different from the five loci identified by van Kesteren & Oberski (2019), probably because both Houtepen et al. (2016) and van Kesteren & Oberski (2019) did not consider all loci jointly in their analyses. The high dimensional DNA methylation loci indeed necessitate new techniques for identifying active mediating loci and testing the direct and indirect effects of the early life traumatic stress on later cortisol alteration. Motivated by the contradictory results in the aforementioned two scientific works, we develop a new estimating and testing procedure, and apply it to the same data set as that analyzed by the two works. We identify three new loci: cg19230917, cg06422529 and cg03199124, and their effect sizes and p-values are 321.196 (P-value =0.035965), 418.173 (P-value =0.000234) and 471.865 (P-value =0.001691), respectively. These three loci possess both reasonably neurobiological interpretations and statistically significant effects via our proposed tests. Based on our new procedure, we further confirm that the childhood trauma does not have significant direct effects on cortisol change - it only indirectly affects cortisol through DNA methylation, and the indirect effect is negative.
DOI: 10.6084/m9.figshare.21084724
title: Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts

description: The decision to engage in military conflict is shaped by many factors, including state- and dyad-level characteristics as well as the state’s membership in geopolitical coalitions. Supporters of the democratic peace theory, for example, hypothesize that the community of democratic states is less likely to wage war with each other. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on group memberships. To test these arguments, we develop a dynamic model of network data by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict dynamic node memberships in latent groups as well as the direct formation of edges between dyads. While prior substantive research often assumes the decision to engage in international militarized conflict is independent across states and static over time, we demonstrate that conflict is driven by states’ evolving membership in geopolitical blocs. Our analysis of militarized disputes from 1816 to 2010 identifies two distinct blocs of democratic states, only one of which exhibits unusually low rates of conflict. Changes in monadic covariates like democracy shift states between coalitions, making some states more pacific but others more belligerent. Supplementary materials for this article are available online.