cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8398049
title: Good Plot Symbols by Default

description: Scatterplots require different symbols for different purposes. For presentation, aesthetically pleasing symbols are popular. For analysis, highly discriminable symbols aid pattern detection. This study identifies a default symbol set suitable for both presentation and analysis. This is achieved by using popular symbols with preattentive differences. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7946285
title: Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models

description: Changes in computation and automated data collection have greatly increased interest in statistical models of dynamic networks. Many of the models employed for inference on large-scale dynamic networks suffer from limited forward simulation/prediction capabilities. One major problem with many of the forward simulation procedures is a tendency for the model to become degenerate in only a few time steps, that is, the simulation/prediction procedure results in either null graphs or complete graphs. Here, we describe an algorithm for simulating a sequence of networks generated from lagged dynamic network regression models DNR(V), a subfamily of TERGMs. Further, we introduce a smoothed estimator for forward prediction based on smoothing of the change statistics obtained for a dynamic network regression model. We focus on the implementation of the algorithm, providing a series of motivating examples with comparisons to dynamic network models from the literature. We find that our algorithm significantly improves multistep prediction/simulation over standard DNR(V) forecasting. Furthermore, we show that our method performs comparably to existing more complex dynamic network analysis frameworks (SAOM and STERGMs) for small networks over short time periods, and significantly outperforms these approaches over long time time intervals and/or large networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8398049.v3
title: Good Plot Symbols by Default

description: Scatterplots require different symbols for different purposes. For presentation, aesthetically pleasing symbols are popular. For analysis, highly discriminable symbols aid pattern detection. This study identifies a default symbol set suitable for both presentation and analysis. This is achieved by using popular symbols with preattentive differences. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7946285.v3
title: Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models

description: Changes in computation and automated data collection have greatly increased interest in statistical models of dynamic networks. Many of the models employed for inference on large-scale dynamic networks suffer from limited forward simulation/prediction capabilities. One major problem with many of the forward simulation procedures is a tendency for the model to become degenerate in only a few time steps, that is, the simulation/prediction procedure results in either null graphs or complete graphs. Here, we describe an algorithm for simulating a sequence of networks generated from lagged dynamic network regression models DNR(V), a subfamily of TERGMs. Further, we introduce a smoothed estimator for forward prediction based on smoothing of the change statistics obtained for a dynamic network regression model. We focus on the implementation of the algorithm, providing a series of motivating examples with comparisons to dynamic network models from the literature. We find that our algorithm significantly improves multistep prediction/simulation over standard DNR(V) forecasting. Furthermore, we show that our method performs comparably to existing more complex dynamic network analysis frameworks (SAOM and STERGMs) for small networks over short time periods, and significantly outperforms these approaches over long time time intervals and/or large networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8398049.v2
title: Good Plot Symbols by Default

description: Scatterplots require different symbols for different purposes. For presentation, aesthetically pleasing symbols are popular. For analysis, highly discriminable symbols aid pattern detection. This study identifies a default symbol set suitable for both presentation and analysis. This is achieved by using popular symbols with preattentive differences. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7904939
title: Component-Based Regularization of Multivariate Generalized Linear Mixed Models

description: We address the component-based regularization of a multivariate Generalized Linear Mixed Model (GLMM) in the framework of grouped data. A set Y of random responses is modelled with a multivariate GLMM, based on a set X of explanatory variables, a set A of additional explanatory variables, and random effects to introduce the within-group dependence of observations. Variables in X are assumed many and redundant so that regression demands regularization. This is not the case for A, which contains few and selected variables. Regularization is performed building an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in X. To estimate the model, we propose to maximize a criterion specific to the supervised component-based generalized linear regression (SCGLR) within an adaptation of Schall’s algorithm. This extension of SCGLR is tested on both simulated and real grouped data, and compared to ridge and LASSO regularizations. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.7946285.v2
title: Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models

description: Changes in computation and automated data collection have greatly increased interest in statistical models of dynamic networks. Many of the models employed for inference on large-scale dynamic networks suffer from limited forward simulation/prediction capabilities. One major problem with many of the forward simulation procedures is a tendency for the model to become degenerate in only a few time steps, that is, the simulation/prediction procedure results in either null graphs or complete graphs. Here, we describe an algorithm for simulating a sequence of networks generated from lagged dynamic network regression models DNR(V), a subfamily of TERGMs. Further, we introduce a smoothed estimator for forward prediction based on smoothing of the change statistics obtained for a dynamic network regression model. We focus on the implementation of the algorithm, providing a series of motivating examples with comparisons to dynamic network models from the literature. We find that our algorithm significantly improves multistep prediction/simulation over standard DNR(V) forecasting. Furthermore, we show that our method performs comparably to existing more complex dynamic network analysis frameworks (SAOM and STERGMs) for small networks over short time periods, and significantly outperforms these approaches over long time time intervals and/or large networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8295482
title: An Expectation Conditional Maximization Approach for Gaussian Graphical Models

description: Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian copula graphical models using an expectation conditional maximization (ECM) algorithm, extending the EM approach from Bayesian variable selection to graphical model estimation. We show that the ECM approach enables fast posterior exploration under a sequence of mixture priors, and can incorporate multiple sources of information. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8063768
title: Fast Generalized Linear Models by Database Sampling and One-Step Polishing

description: In this article, I show how to fit a generalized linear model to <i>N</i> observations on <i>p</i> variables stored in a relational database, using one sampling query and one aggregation query, as long as N12+δ observations can be stored in memory, for some δ&gt;0. The resulting estimator is fully efficient and asymptotically equivalent to the maximum likelihood estimator, and so its variance can be estimated from the Fisher information in the usual way. A proof-of-concept implementation uses R with MonetDB and with SQLite, and could easily be adapted to other popular databases. I illustrate the approach with examples of taxi-trip data in New York City and factors related to car color in New Zealand. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874378
title: Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, with Optimal Acceleration

description: Many statistical learning problems can be posed as minimization of a sum of two convex functions, one typically a composition of nonsmooth and linear functions. Examples include regression under structured sparsity assumptions. Popular algorithms for solving such problems, for example, ADMM, often involve nontrivial optimization subproblems or smoothing approximation. We consider two classes of primal–dual algorithms that do not incur these difficulties, and unify them from a perspective of monotone operator theory. From this unification, we propose a continuum of preconditioned forward–backward operator splitting algorithms amenable to parallel and distributed computing. For the entire region of convergence of the whole continuum of algorithms, we establish its rates of convergence. For some known instances of this continuum, our analysis closes the gap in theory. We further exploit the unification to propose a continuum of accelerated algorithms. We show that the whole continuum attains the theoretically optimal rate of convergence. The scalability of the proposed algorithms, as well as their convergence behavior, is demonstrated up to 1.2 million variables with a distributed implementation. The code is available at https://github.com/kose-y/dist-primal-dual. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7904942.v3
title: Influence Diagnostics for High-Dimensional Lasso Regression

description: The increased availability of high-dimensional data, and appeal of a “sparse” solution has made penalized likelihood methods commonplace. Arguably the most widely utilized of these methods is ℓ1 regularization, popularly known as the lasso. When the lasso is applied to high-dimensional data, observations are relatively few; thus, each observation can potentially have tremendous influence on model selection and inference. Hence, a natural question in this context is the identification and assessment of influential observations. We address this by extending the framework for assessing estimation influence in traditional linear regression, and demonstrate that it is equally, if not more, relevant for assessing model selection influence for high-dimensional lasso regression. Within this framework, we propose four new “deletion methods” for gauging the influence of an observation on lasso model selection: df-model, df-regpath, df-cvpath, and df-lambda. Asymptotic cut-offs for each measure, even when p→∞, are developed. We illustrate that in high-dimensional settings, individual observations can have a tremendous impact on lasso model selection. We demonstrate that application of our measures can help reveal relationships in high-dimensional real data that may otherwise remain hidden. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8295482.v3
title: An Expectation Conditional Maximization Approach for Gaussian Graphical Models

description: Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian copula graphical models using an expectation conditional maximization (ECM) algorithm, extending the EM approach from Bayesian variable selection to graphical model estimation. We show that the ECM approach enables fast posterior exploration under a sequence of mixture priors, and can incorporate multiple sources of information. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7959380
title: A Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models

description: Finding Bayesian optimal designs for nonlinear models is a difficult task because the optimality criterion typically requires us to evaluate complex integrals before we perform a constrained optimization. We propose a hybridized method where we combine an adaptive multidimensional integration algorithm and a metaheuristic algorithm called imperialist competitive algorithm to find Bayesian optimal designs. We apply our numerical method to a few challenging design problems to demonstrate its efficiency. They include finding <i>D</i>-optimal designs for an item response model commonly used in education, Bayesian optimal designs for survival models, and Bayesian optimal designs for a four-parameter sigmoid Emax dose response model. Supplementary materials for this article are available online and they contain an R package for implementing the proposed algorithm and codes for reproducing all the results in this article.
DOI: 10.6084/m9.figshare.7904939.v3
title: Component-Based Regularization of Multivariate Generalized Linear Mixed Models

description: We address the component-based regularization of a multivariate Generalized Linear Mixed Model (GLMM) in the framework of grouped data. A set Y of random responses is modelled with a multivariate GLMM, based on a set X of explanatory variables, a set A of additional explanatory variables, and random effects to introduce the within-group dependence of observations. Variables in X are assumed many and redundant so that regression demands regularization. This is not the case for A, which contains few and selected variables. Regularization is performed building an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in X. To estimate the model, we propose to maximize a criterion specific to the supervised component-based generalized linear regression (SCGLR) within an adaptation of Schall’s algorithm. This extension of SCGLR is tested on both simulated and real grouped data, and compared to ridge and LASSO regularizations. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.7904942
title: Influence Diagnostics for High-Dimensional Lasso Regression

description: The increased availability of high-dimensional data, and appeal of a “sparse” solution has made penalized likelihood methods commonplace. Arguably the most widely utilized of these methods is ℓ1 regularization, popularly known as the lasso. When the lasso is applied to high-dimensional data, observations are relatively few; thus, each observation can potentially have tremendous influence on model selection and inference. Hence, a natural question in this context is the identification and assessment of influential observations. We address this by extending the framework for assessing estimation influence in traditional linear regression, and demonstrate that it is equally, if not more, relevant for assessing model selection influence for high-dimensional lasso regression. Within this framework, we propose four new “deletion methods” for gauging the influence of an observation on lasso model selection: df-model, df-regpath, df-cvpath, and df-lambda. Asymptotic cut-offs for each measure, even when p→∞, are developed. We illustrate that in high-dimensional settings, individual observations can have a tremendous impact on lasso model selection. We demonstrate that application of our measures can help reveal relationships in high-dimensional real data that may otherwise remain hidden. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7904942.v2
title: Influence Diagnostics for High-Dimensional Lasso Regression

description: The increased availability of high-dimensional data, and appeal of a “sparse” solution has made penalized likelihood methods commonplace. Arguably the most widely utilized of these methods is ℓ1 regularization, popularly known as the lasso. When the lasso is applied to high-dimensional data, observations are relatively few; thus, each observation can potentially have tremendous influence on model selection and inference. Hence, a natural question in this context is the identification and assessment of influential observations. We address this by extending the framework for assessing estimation influence in traditional linear regression, and demonstrate that it is equally, if not more, relevant for assessing model selection influence for high-dimensional lasso regression. Within this framework, we propose four new “deletion methods” for gauging the influence of an observation on lasso model selection: df-model, df-regpath, df-cvpath, and df-lambda. Asymptotic cut-offs for each measure, even when p→∞, are developed. We illustrate that in high-dimensional settings, individual observations can have a tremendous impact on lasso model selection. We demonstrate that application of our measures can help reveal relationships in high-dimensional real data that may otherwise remain hidden. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7910549
title: Adaptive Incremental Mixture Markov Chain Monte Carlo

description: We propose adaptive incremental mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general state-space. While adaptive MCMC methods usually update a parametric proposal kernel with a global rule, AIMM locally adapts a semiparametric kernel. AIMM is based on an independent Metropolis–Hastings proposal distribution which takes the form of a finite mixture of Gaussian distributions. Central to this approach is the idea that the proposal distribution adapts to the target by locally adding a mixture component when the discrepancy between the proposal mixture and the target is deemed to be too large. As a result, the number of components in the mixture proposal is not fixed in advance. Theoretically, we prove that there exists a stochastic process that can be made arbitrarily close to AIMM and that converges to the correct target distribution. We also illustrate that it performs well in practice in a variety of challenging situations, including high-dimensional and multimodal target distributions. Finally, the methodology is successfully applied to two real data examples, including the Bayesian inference of a semiparametric regression model for the Boston Housing dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874378.v4
title: Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, with Optimal Acceleration

description: Many statistical learning problems can be posed as minimization of a sum of two convex functions, one typically a composition of nonsmooth and linear functions. Examples include regression under structured sparsity assumptions. Popular algorithms for solving such problems, for example, ADMM, often involve nontrivial optimization subproblems or smoothing approximation. We consider two classes of primal–dual algorithms that do not incur these difficulties, and unify them from a perspective of monotone operator theory. From this unification, we propose a continuum of preconditioned forward–backward operator splitting algorithms amenable to parallel and distributed computing. For the entire region of convergence of the whole continuum of algorithms, we establish its rates of convergence. For some known instances of this continuum, our analysis closes the gap in theory. We further exploit the unification to propose a continuum of accelerated algorithms. We show that the whole continuum attains the theoretically optimal rate of convergence. The scalability of the proposed algorithms, as well as their convergence behavior, is demonstrated up to 1.2 million variables with a distributed implementation. The code is available at https://github.com/kose-y/dist-primal-dual. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7959380.v4
title: A Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models

description: Finding Bayesian optimal designs for nonlinear models is a difficult task because the optimality criterion typically requires us to evaluate complex integrals before we perform a constrained optimization. We propose a hybridized method where we combine an adaptive multidimensional integration algorithm and a metaheuristic algorithm called imperialist competitive algorithm to find Bayesian optimal designs. We apply our numerical method to a few challenging design problems to demonstrate its efficiency. They include finding <i>D</i>-optimal designs for an item response model commonly used in education, Bayesian optimal designs for survival models, and Bayesian optimal designs for a four-parameter sigmoid Emax dose response model. Supplementary materials for this article are available online and they contain an R package for implementing the proposed algorithm and codes for reproducing all the results in this article.
DOI: 10.6084/m9.figshare.7981790
title: Variable-Domain Functional Principal Component Analysis

description: We introduce a novel method of principal component analysis for data with varying domain lengths for each functional observation. We refer to this technique as variable-domain functional principal component analysis, or vd-FPCA. We fit a trivariate smoother using penalized thin plate splines to estimate the covariance as a function of the domain length. Principal components are then calculated through eigen-decomposition of the estimated covariance matrix, conditional on the domain length. We apply vd-FPCA in two functional data settings, first to daily measures of patient wellness during a stay in the ICU, and second, to accelerometer recordings of repeated in-lab movements. In each example, vd-FPCA uses fewer principal components than typical FPCA methods to explain a greater proportion of the variation in the data. We also find the principal components provide greater flexibility in interpretation with respect to domain length than traditional approaches. These methods are easily implementable through standard statistical software and applicable to a wide variety of datasets involving continuous observations over a variable domain. Supplementary materials for this article are available online.