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mcvis: A new framework for collinearity discovery, diagnostic and visualization J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200630
Chen Lin; Kevin Wang; Samuel MuellerCollinearity discovery through diagnostic tools is an important analysis step when performing linear regression. Despite their widespread use, collinearity indices such as the variance inflation factor and the condition number have limitations and may not be effective in some applications. In this article we will contribute to the study of conventional collinearity indices through theoretical and

Sparse Single Index Models for Multivariate Responses J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200630
Yuan Feng; Luo Xiao; Eric C. ChiJoint models are popular for analyzing data with multivariate responses. We propose a sparse multivariate single index model, where responses and predictors are linked by unspecified smooth functions and multiple matrix level penalties are employed to select predictors and induce lowrank structures across responses. An alternating direction method of multipliers (ADMM) based algorithm is proposed

Optimal Sampling for Generalized Linear Models under Measurement Constraints J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200608
Tao Zhang; Yang Ning; David RuppertUnder “measurement constraints,” responses are expensive to measure and initially unavailable on most of records in the dataset, but the covariates are available for the entire dataset. Our goal is to sample a relatively small portion of the dataset where the expensive responses will be measured and the resultant sampling estimator is statistically efficient. Measurement constraints require the sampling

Bayesian spatial clustering of extremal behaviour for hydrological variables J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200604
Christian Rohrbeck; Jonathan A. TawnTo address the need for efficient inference for a range of hydrological extreme value problems, spatial pooling of information is the standard approach for marginal tail estimation. We propose the first extreme value spatial clustering methods which account for both the similarity of the marginal tails and the spatial dependence structure of the data to determine the appropriate level of pooling. Spatial

A slice tour for finding hollowness in highdimensional data J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200604
Ursula Laa; Dianne Cook; German ValenciaTaking projections of highdimensional data is a common analytical and visualisation technique in statistics for working with highdimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualising data with concavities, or nonlinear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots

Illumination depth J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200604
Stanislav Nagy; Jiří DvořákThe concept of illumination bodies studied in convex geometry is used to amend the halfspace depth for multivariate data. The proposed notion of illumination enables finer resolution of the sample points, naturally breaks ties in the associated depthbased ordering, and introduces a depthlike function for points outside the convex hull of the support of the probability measure. The illumination is

Surrogate Residuals for Discrete Choice Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200602
Chao Cheng; Rui Wang; Heping ZhangDiscrete Choice Models (DCMs) are a class of models for modelling response variables that take values from a set of alternatives. Examples include logistic regression, probit regression, and multinomial logistic regression. These models are also referred together as generalized linear models. Although there exist methods for the goodness of fit of DCMs, defining intuitive residuals for such models

Delayed acceptance ABCSMC J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200602
Richard G. Everitt; Paulina A. RowińskaApproximate Bayesian computation (ABC) is now an established technique for statistical inference used in cases where the likelihood function is computationally expensive or not available. It relies on the use of a model that is specified in the form of a simulator, and approximates the likelihood at a parameter value θ by simulating auxiliary data sets x and evaluating the distance of x from the true

Correction J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200528
(2020). Correction. Journal of Computational and Graphical Statistics. Ahead of Print.

Efficient Parameter Sampling for Markov Jump Processes J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200507
Boqian Zhang; Vinayak RaoMarkov jump processes are continuoustime stochastic processes widely used in a variety of applied disciplines. Inference typically proceeds via Markov chain Monte Carlo, the stateoftheart being a uniformizationbased auxiliary variable Gibbs sampler. This was designed for situations where the process parameters are known, and Bayesian inference over unknown parameters is typically carried out by

Automated Redistricting Simulation Using Markov Chain Monte Carlo J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200507
Benjamin Fifield; , Michael Higgins; Kosuke Imai; Alexander TarrLegislative redistricting is a critical element of representative democracy. A number of political scientists have used simulation methods to sample redistricting plans under various constraints to assess their impact on partisanship and other aspects of representation. However, while many optimization algorithms have been proposed, surprisingly few simulation methods exist in the published scholarship

Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200507
Evan Baker; Peter Challenor; Matt EamesStatistically modeling the output of a stochastic computer model can be difficult to do accurately without a large simulation budget. We alleviate this problem by exploiting readily available deterministic approximations to efficiently learn about the respective stochastic computer models. This is done via the summation of two Gaussian processes; one responsible for modeling the deterministic approximation

Identifying Heterogeneous Effect using Latent Supervised Clustering with Adaptive Fusion J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200506
Jingxiang Chen; Quoc TranDinh; Michael R. Kosorok; Yufeng LiuPrecision medicine is an important area of research with the goal of identifying the optimal treatment for each individual patient. In the literature, various methods are proposed to divide the population into subgroups according to the heterogeneous effects of individuals. In this paper, a new exploratory machine learning tool, named latent supervised clustering, is proposed to identify the heterogeneous

Massive parallelization boosts big Bayesian multidimensional scaling J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200505
Andrew J. Holbrook; Philippe Lemey; Guy Baele; Simon Dellicour; Dirk Brockmann; Andrew Rambaut; Marc A. SuchardBig Bayes is the computationally intensive coapplication of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling (MDS) can help scientists learn viral trajectories through spacetime, but its computational burden prevents its wider use. Crucial MDS model calculations

Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200427
Gregory J. Hunt; Mark A. Dane; James E. Korkola; Laura M. Heiser; Johann A. GagnonBartschProper data transformation is an essential part of analysis. Choosing appropriate transformations for variables can enhance visualization, improve efficacy of analytical methods, and increase data interpretability. However, determining appropriate transformations of variables from highcontent imaging data poses new challenges. Imaging data produce hundreds of covariates from each of thousands of images

Rerandomization strategies for balancing covariates using preexperimental longitudinal data J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200421
Per Johansson; Mårten SchultzbergABSTRACT This paper considers experimental design based on the strategy of rerandomization to increase the efficiency in experiments. Two aspects of rerandomization are addressed. First, we propose a twostage allocation sample scheme for randomization inference to the units in experiments that guarantees that the differenceinmean estimator is an unbiased estimator of the sample average treatment

A Pliable Lasso J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190905
Robert Tibshirani; Jerome FriedmanWe propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such as gender, age, or time. The paradigm is quite general, with each lasso coefficient modified by a sparse linear function of the modifying variables Z. The model is estimated in a hierarchical fashion to control

Bivariate Residual Plots With Simulation Polygons J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190906
Rafael A. Moral; John Hinde; Clarice G. B. DemétrioWhen using univariate models, goodness of fit can be assessed through many different methods, including graphical tools such as halfnormal plots with a simulation envelope. This is straightforward due to the notion of ordering of a univariate sample, which can readily reveal possible outliers. In the bivariate case, however, it is often difficult to detect extreme points and verify whether a sample

Estimating TimeVarying Graphical Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190903
Jilei Yang; Jie PengIn this article, we study timevarying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance, the study of how stock prices interact with each other and how such interactions change over time. We propose a new model, LOcal

Bayesian Model Averaging Over Treebased Dependence Structures for Multivariate Extremes J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190829
Sabrina Vettori; Raphaël Huser; Johan Segers; Marc G. GentonDescribing the complex dependence structure of extreme phenomena is particularly challenging. To tackle this issue, we develop a novel statistical method that describes extremal dependence taking advantage of the inherent treebased dependence structure of the maxstable nested logistic distribution, and which identifies possible clusters of extreme variables using reversible jump Markov chain Monte

Estimating the Number of Clusters Using CrossValidation J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190930
Wei Fu; Patrick O. PerryMany clustering methods, including kmeans, require the user to specify the number of clusters as an input parameter. A variety of methods have been devised to choose the number of clusters automatically, but they often rely on strong modeling assumptions. This article proposes a datadriven approach to estimate the number of clusters based on a novel form of crossvalidation. The proposed method differs

Parallelization of a Common Changepoint Detection Method J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190906
S. O. Tickle; I. A. Eckley; P. Fearnhead; K. HaynesAbstract In recent years, various means of efficiently detecting changepoints have been proposed, with one popular approach involving minimizing a penalized cost function using dynamic programming. In some situations, these algorithms can have an expected computational cost that is linear in the number of data points; however, the worst case cost remains quadratic. We introduce two means of improving

Efficient Construction of Test Inversion Confidence Intervals Using Quantile Regression J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190903
Eyal Fisher; Regev Schweiger; Saharon RossetModern problems in statistics often include estimators of high computational complexity and with complicated distributions. Statistical inference on such estimators usually relies on asymptotic normality assumptions, however, such assumptions are often not applicable for available sample sizes, due to dependencies in the data. A common alternative is the use of resampling procedures, such as bootstrapping

Testing SparsityInducing Penalties J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190819
Maryclare Griffin; Peter D. HoffMany penalized maximum likelihood estimators correspond to posterior mode estimators under specific prior distributions. Appropriateness of a particular class of penalty functions can therefore be interpreted as the appropriateness of a prior for the parameters. For example, the appropriateness of a lasso penalty for regression coefficients depends on the extent to which the empirical distribution

Diagonal Discriminant Analysis With Feature Selection for HighDimensional Data J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190816
Sarah E. Romanes; John T. Ormerod; Jean Y. H. YangWe introduce a new method of performing highdimensional discriminant analysis (DA), which we call multiDA. Starting from multiclass diagonal DA classifiers which avoid the problem of highdimensional covariance estimation we construct a hybrid model that seamlessly integrates feature selection components. Our feature selection component naturally simplifies to weights which are simple functions of

Bayesian Deep Net GLM and GLMM J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190816
M.N. Tran; N. Nguyen; D. Nott; R. KohnDeep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The consideration of neural networks with random effects is not widely used in the literature, perhaps because of the computational challenges of incorporating subject specific

Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190719
Michael Weylandt; John Nagorski; Genevera I. AllenConvex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despite these advantages, convex clustering has not been widely adopted, due to its computationally intensive nature and its lack of compelling visualizations. To address these impediments, we introduce Algorithmic Regularization

Scalable Visualization Methods for Modern Generalized Additive Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190719
Matteo Fasiolo; Raphaël Nedellec; Yannig Goude; Simon N. WoodIn the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify

A Function Emulation Approach for Doubly Intractable Distributions J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190723
Jaewoo Park; Murali HaranDoubly intractable distributions arise in many settings, for example, in Markov models for point processes and exponential random graph models for networks. Bayesian inference for these models is challenging because they involve intractable normalizing “constants” that are actually functions of the parameters of interest. Although several computational methods have been developed for these models,

Scalable Bayesian Nonparametric Clustering and Classification J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190719
Yang Ni; Peter Müller; Maurice Diesendruck; Sinead Williamson; Yitan Zhu; Yuan JiWe develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach make inference for a wide range of Bayesian nonparametric mixture models applicable to large

BIVAS: A Scalable Bayesian Method for BiLevel Variable Selection With Applications J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190719
Mingxuan Cai; Mingwei Dai; Jingsi Ming; Heng Peng; Jin Liu; Can YangIn this article, we consider a Bayesian bilevel variable selection problem in highdimensional regressions. In many practical situations, it is natural to assign group membership to each predictor. Examples include that genetic variants can be grouped at the gene level and a covariate from different tasks naturally forms a group. Thus, it is of interest to select important groups as well as important

Scalable Bayesian Regression in High Dimensions With Multiple Data Sources J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190715
Konstantinos Perrakis; Sach Mukherjee; The Alzheimer’s Disease Neuroimaging InitiativeApplications of highdimensional regression often involve multiple sources or types of covariates. We propose methodology for this setting, emphasizing the “wide data” regime with large total dimensionality p and sample size n≪p. We focus on a flexible ridgetype prior with shrinkage levels that are specific to each data type or source and that are set automatically by empirical Bayes. All estimation

Anomaly Detection in Streaming Nonstationary Temporal Data J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190624
Priyanga Dilini Talagala; Rob J. Hyndman; Kate SmithMiles; Sevvandi Kandanaarachchi; Mario A. MuñozThis article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming timeseries data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system’s typical behavior using extreme value theory. Then a sliding window

A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies With Auxiliary Covariates J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190702
Tianjian Zhou; Michael J. Daniels; Peter MüllerWe develop a semiparametric Bayesian approach to missing outcome data in longitudinal studies in the presence of auxiliary covariates. We consider a joint model for the full data response, missingness, and auxiliary covariates. We include auxiliary covariates to “move” the missingness “closer” to missing at random. In particular, we specify a semiparametric Bayesian model for the observed data via

Generalized Spatially Varying Coefficient Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200420
Myungjin Kim; Li WangIn this paper, we introduce a new class of nonparametric regression models, called generalized spatially varying coefficient models (GSVCMs), for data distributed over complex domains. For model estimation, we propose a nonparametric quasilikelihood approach using the bivariate penalized spline approximation technique. We show that our estimation procedure is able to handle irregularlyshaped spatial

HighDimensional Copula Variational Approximation Through Transformation J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200420
Michael Stanley Smith; Rubén LoaizaMaya; David J. NottVariational methods are attractive for computing Bayesian inference when exact inference is impractical. They approximate a target distribution—either the posterior or an augmented posterior—using a simpler distribution that is selected to balance accuracy with computational feasibility. Here, we approximate an elementwise parametric transformation of the target distribution as multivariate Gaussian

Poisson KernelBased Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200420
Mojgan Golzy; Marianthi MarkatouSpherical or directional data arise in many applications of interest. Furthermore, many nondirectional datasets can be usefully reexpressed in the form of directions and analyzed as spherical data. We have proposed a clustering algorithm using mixtures of Poissonkernelbased densities (PKBD) on the sphere. We prove convergence of the associated generalized EMalgorithm, investigate the identifiability

CoClustering of Ordinal Data via Latent Continuous Random Variables and Not Missing at Random Entries J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200420
Marco Corneli; Charles Bouveyron; Pierre LatoucheThis article is about the coclustering of ordinal data. Such data are very common on ecommerce platforms where customers rank the products/services they bought. In more detail, we focus on arrays of ordinal (possibly missing) data involving two disjoint sets of individuals/objects corresponding to the rows/columns of the arrays. Typically, an observed entry (i, j) in the array is an ordinal score

Influence Diagnostics for HighDimensional Lasso Regression J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190611
Bala Rajaratnam; Steven Roberts; Doug Sparks; Honglin YuAbstract The increased availability of highdimensional 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 highdimensional data, observations are relatively few; thus, each observation can potentially have tremendous influence on

Distributed Generalized CrossValidation for DivideandConquer Kernel Ridge Regression and Its Asymptotic Optimality J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190528
Ganggang Xu; Zuofeng Shang; Guang ChengTuning parameter selection is of critical importance for kernel ridge regression. To date, a datadriven tuning method for divideandconquer kernel ridge regression (dKRR) has been lacking in the literature, which limits the applicability of dKRR for large datasets. In this article, by modifying the generalized crossvalidation (GCV) score, we propose a distributed generalized crossvalidation (dGCV)

ComponentBased Regularization of Multivariate Generalized Linear Mixed Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190604
Jocelyn Chauvet; Catherine Trottier; Xavier Bry(2019). ComponentBased Regularization of Multivariate Generalized Linear Mixed Models. Journal of Computational and Graphical Statistics: Vol. 28, No. 4, pp. 909920.

Simultaneous Variable and Covariance Selection With the Multivariate SpikeandSlab LASSO J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190517
Sameer K. Deshpande; Veronika Ročková; Edward I. GeorgeWe propose a Bayesian procedure for simultaneous variable and covariance selection using continuous spikeandslab priors in multivariate linear regression models where q possibly correlated responses are regressed onto p predictors. Rather than relying on a stochastic search through the highdimensional model space, we develop an ECM algorithm similar to the EMVS procedure of Ročková and George targeting

The Generalized Ridge Estimator of the Inverse Covariance Matrix J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190606
Wessel N. van WieringenAbstract The ridge inverse covariance estimator is generalized to allow for entrywise penalization. An efficient algorithm for its evaluation is proposed. Its computational accuracy is benchmarked against implementations of specific cases the generalized ridge inverse covariance estimator encompasses. The proposed estimator shrinks toward a userspecified, nonrandom target matrix and is shown to be

Improving Estimation in Functional Linear Regression with Points of Impact: Insights into Google AdWords J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200416
Dominik Liebl; Stefan Rameseder; Christoph RustThe functional linear regression model with points of impact is a recent augmentation of the classical functional linear model with many practically important applications. In this work, however, we demonstrate that the existing datadriven procedure for estimating the parameters of this regression model can be very instable and inaccurate. The tendency to omit relevant points of impact is a particularly

Building Representative Matched Samples with Multivalued Treatments in Large Observational Studies* J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200416
Magdalena Bennett; Juan Pablo Vielma; José R. ZubizarretaIn this paper, we present a new way of matching in observational studies that overcomes three limitations of existing matching approaches. First, it directly balances covariates with multivalued treatments without explicitly estimating the generalized propensity score. Second, it builds selfweighted matched samples that are representative of a target population by design. Third, it can handle large

A Treebased SemiVarying Coefficient Model for the COMPoisson Distribution J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200416
Suneel Babu Chatla; Galit ShmueliWe propose a treebased semivarying coefficient model for the ConwayMaxwellPoisson (CMP or COMPoisson) distribution which is a twoparameter generalization of the Poisson distribution and is flexible enough to capture both underdispersion and overdispersion in count data. The advantage of treebased methods is their scalability to highdimensional data. We develop CMPMOB, an estimation procedure

StateDependent Kernel Selection for Conditional Sampling of Graphs J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200416
James A. Scott; Axel GandyThis article introduces new efficient algorithms for two problems: sampling conditional on vertex degrees in unweighted graphs, and conditional on vertex strengths in weighted graphs. The resulting conditional distributions provide the basis for exact tests on social networks and twoway contingency tables. The algorithms are able to sample conditional on the presence or absence of an arbitrary set

Testing for Equivalence of Network Distribution Using Subgraph Counts J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200416
P.A. G. Maugis; S. C. Olhede; C. E. Priebe; P. J. WolfeWe consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a network sample are drawn from a specified model. We achieve this by deriving the joint asymptotic properties of average subgraph counts as the number of observed networks increases but the number of nodes in each network remains

Consensus Monte Carlo for Random Subsets Using Shared Anchors J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200415
Yang Ni; Yuan Ji; Peter MüllerWe present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model,

Bayesian Variational Inference for Exponential Random Graph Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200415
Linda S. L. Tan; Nial FrielDeriving Bayesian inference for exponential random graph models (ERGMs) is a challenging “doubly intractable” problem as the normalizing constants of the likelihood and posterior density are both intractable. Markov chain Monte Carlo (MCMC) methods which yield Bayesian inference for ERGMs, such as the exchange algorithm, are asymptotically exact but computationally intensive, as a network has to be

Bayesian Semiparametric Analysis of Multivariate Continuous Responses, With Variable Selection J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200414
Georgios Papageorgiou; Benjamin C. MarshallThis article presents an approach to Bayesian semiparametric inference for Gaussian multivariate response regression. We are motivated by various small and medium dimensional problems from the physical and social sciences. The statistical challenges revolve around dealing with the unknown mean and variance functions and in particular, the correlation matrix. To tackle these problems, we have developed

Probabilistic Detection and Estimation of Conic Sections From Noisy Data J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200406
Subharup Guha; Sujit K. GhoshInferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for conic sections is that estimation methods rely on the underlying shape of the conics (being known to be ellipse, parabola, or hyperbola). A general purpose Bayesian hierarchical model is proposed for conic sections and corresponding

Forward EventChain Monte Carlo: Fast sampling by randomness control in irreversible Markov chains J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200403
Manon Michel; Alain Durmus; Stéphane SénécalIrreversible and rejectionfree Monte Carlo methods, recently developed in physics under the name EventChain and known in Statistics as Piecewise Deterministic Monte Carlo (PDMC), have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their randomwalk behavior. However, while applying such schemes to standard statistical models, one generally needs

Model checking for hidden Markov models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20200323
Jodie Buckby; Ting Wang; Jiancang Zhuang; Kazushige ObaraResidual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov models (HMMs) is limited. As HMMs with complex structures are increasingly used to accommodate different types of data, there is a need for further tools to check the validity of models applied to real world

An Expectation Conditional Maximization Approach for Gaussian Graphical Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190619
Zehang Richard Li; Tyler H. McCormickBayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In highdimensional settings, the space of possible graphs becomes enormous, rendering even stateoftheart Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian

Beyond Prediction: A Framework for Inference With Variational Approximations in Mixture Models J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190626
T. Westling; T. H. McCormickVariational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many settings in the health, social, and biological sciences. Variational inference in a frequentist context works by approximating intractable conditional distributions with a tractable family and optimizing the resulting lower bound on

Adaptive Incremental Mixture Markov Chain Monte Carlo. J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190607
Florian Maire,Nial Friel,Antonietta Mira,Adrian E RafteryWe propose adaptive incremental mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general statespace. 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 MetropolisHastings proposal distribution which takes the

Incremental Mixture Importance Sampling With Shotgun Optimization J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190528
Biljana Jonoska Stojkova; David A. CampbellThis article proposes a general optimization strategy, which combines results from different optimization or parameter estimation methods to overcome shortcomings of a single method. Shotgun optimization is developed as a framework which employs different optimization strategies, criteria, or conditional targets to enable wider likelihood exploration. The introduced shotgun optimization approach is

Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, with Optimal Acceleration J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190528
Seyoon Ko; Donghyeon Yu; JoongHo WonMany 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

Damped Anderson Acceleration With Restarts and Monotonicity Control for Accelerating EM and EMlike Algorithms J. Comput. Graph. Stat. (IF 2.319) Pub Date : 20190528
Nicholas C. Henderson; Ravi VaradhanThe expectationmaximization (EM) algorithm is a wellknown iterative method for computing maximum likelihood estimates in a variety of statistical problems. Despite its numerous advantages, a main drawback of the EM algorithm is its frequently observed slow convergence which often hinders the application of EM algorithms in highdimensional problems or in other complex settings. To address the need