当前期刊: Journal of Computational and Graphical Statistics Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • Assessing and Visualizing Simultaneous Simulation Error
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-18
    Nathan Robertson; James M. Flegal; Dootika Vats; Galin L. Jones

    Monte Carlo experiments produce samples in order to estimate features such as means and quantiles of a given distribution. However, simultaneous estimation of means and quantiles has received little attention. In this setting we establish a multivariate central limit theorem for any finite combination of sample means and quantiles under the assumption of a strongly mixing process, which includes the

    更新日期:2020-09-20
  • Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-16
    Congyuan Yang; Carey E. Priebe; Youngser Park; David J. Marchette

    Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating

    更新日期:2020-09-16
  • Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-11
    Indrayudh Ghosal; Giles Hooker

    In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a one-step boosted forest. We show with simulated and real data that the one-step boosted forest has a reduced

    更新日期:2020-09-12
  • Global Consensus Monte Carlo
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-08
    Lewis J. Rendell; Adam M. Johansen; Anthony Lee; Nick Whiteley

    To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribute the data across multiple machines. We consider a likelihood function expressed as a product of terms, each associated with a subset of the data. Inspired by global variable consensus optimisation, we introduce an instrumental hierarchical model associating auxiliary statistical parameters with each

    更新日期:2020-09-08
  • Model-based edge clustering
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-04
    Daniel K. Sewell

    Relational data can be studied using network analytic techniques which define the network as a set of actors and a set of edges connecting these actors. One important facet of network analysis that receives significant attention is community detection. However, while most community detection algorithms focus on clustering the actors of the network, it is very intuitive to cluster the edges. Connections

    更新日期:2020-09-05
  • An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-01
    Qi Wang; Vinayak Rao; Yee Whye Teh

    Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating paths from these processes is usually an intractable problem, and typically involves time-discretization approximations. We propose an exact Markov chain Monte Carlo sampling algorithm that involves no such time-discretization

    更新日期:2020-09-02
  • Improving Bayesian Local Spatial Models in Large Data Sets
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-01
    Amanda Lenzi; Stefano Castruccio; Håvard Rue; Marc G. Genton

    Environmental processes resolved at a sufficiently small scale in space and time inevitably display non-stationary behavior. Such processes are both challenging to model and computationally expensive when the data size is large. Instead of modeling the global non-stationarity explicitly, local models can be applied to disjoint regions of the domain. The choice of the size of these regions is dictated

    更新日期:2020-09-02
  • Shrinking the Covariance Matrix using Convex Penalties on the Matrix-Log Transformation
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-01
    Mengxi Yi; David E. Tyler

    For q-dimensional data, penalized versions of the sample covariance matrix are important when the sample size is small or modest relative to q. Since the negative log-likelihood under multivariate normal sampling is convex in Σ−1, the inverse of the covariance matrix, it is common to consider additive penalties which are also convex in Σ−1. More recently, Deng and Tsui (2013) and Yu et al. (2017) have

    更新日期:2020-09-02
  • Quantum Annealing via Path-Integral Monte Carlo with Data Augmentation
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-01
    Jianchang Hu; Yazhen Wang

    This paper considers quantum annealing in the Ising framework for solving combinatorial optimization problems. The path-integral Monte Carlo simulation approach is often used to approximate quantum annealing and implement the approximation by classical computers, which refers to simulated quantum annealing. In this paper we introduce a data augmentation scheme into simulated quantum annealing and develop

    更新日期:2020-09-02
  • Nonlinear Variable Selection via Deep Neural Networks
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-09-01
    Yao Chen; Qingyi Gao; Faming Liang; Xiao Wang

    This paper presents a general framework for high-dimensional nonlinear variable selection using deep neural networks under the framework of supervised learning. The network architecture includes both a selection layer and approximation layers. The problem can be cast as a sparsity-constrained optimization with a sparse parameter in the selection layer and other parameters in the approximation layers

    更新日期:2020-09-02
  • Reduced-dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-24
    Jaewoo Park; Murali Haran

    Monte Carlo maximum likelihood (MCML) provides an elegant approach to find maximum likelihood estimators (MLEs) for latent variable models. However, MCML algorithms are computationally expensive when the latent variables are high-dimensional and correlated, as is the case for latent Gaussian random field models. Latent Gaussian random field models are widely used, for example in building flexible regression

    更新日期:2020-08-25
  • Nonstationary modeling with sparsity for spatial data via the basis graphical lasso
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-19
    Mitchell Krock; William Kleiber; Stephen Becker

    Many modern spatial models express the stochastic variation component as a basis expansion with random coefficients. Low rank models, approximate spectral decompositions, multiresolution representations, stochastic partial differential equations, and empirical orthogonal functions all fall within this basic framework. Given a particular basis, stochastic dependence relies on flexible modeling of the

    更新日期:2020-08-20
  • Dimension reduction for outlier detection using DOBIN
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-18
    Sevvandi Kandanaarachchi; Rob J. Hyndman

    This paper introduces DOBIN, a new approach to select a set of basis vectors tailored for outlier detection. DOBIN has a simple mathematical foundation and can be used as a dimension reduction tool for outlier detection tasks. We demonstrate the effectiveness of DOBIN on an extensive data repository, by comparing the performance of outlier detection methods using DOBIN and other bases. We further illustrate

    更新日期:2020-08-18
  • Functional regression for densely observed data with novel regularization
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-14
    Ruiyan Luo; Xin Qi

    Smoothness penalty is an efficient regularization method in functional data analysis. However, for a spiky coefficient function which may arise when densely observed spiky functional data are involved, the traditional smoothness penalty could be too strong and lead to an over-smoothed estimate. In this paper, we propose a new family of smoothness penalties which are expressed using wavelet coefficients

    更新日期:2020-08-15
  • Fast Search and Estimation of Bayesian Nonparametric Mixture Models Using a Classification Annealing EM Algorithm
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-14
    George Karabatsos

    Bayesian nonparametric (BNP) infinite-mixture models provide flexible and accurate density estimation, cluster analysis, and regression. However, for the posterior inference of such a model, MCMC algorithms are complex, often need to be tailor-made for different BNP priors, and are intractable for large data sets. We introduce a BNP classification annealing EM algorithm which employs importance sampling

    更新日期:2020-08-15
  • Trace Ratio Optimization for High-Dimensional Multi-Class Discrimination
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-14
    Jeongyoun Ahn; Hee Cheol Chung; Yongho Jeon

    In multi-class discrimination with high-dimensional data, identifying a lower-dimensional subspace with maximum class separation is crucial. We propose a new optimization criterion for finding such a discriminant subspace, which is the ratio of two traces: the trace of between-class scatter matrix and the trace of within-class scatter matrix. Since this problem is not well-defined for high-dimensional

    更新日期:2020-08-14
  • Spectrally Sparse Nonparametric Regression via Elastic Net Regularized Smoothers
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-14
    Nathaniel E. Helwig

    Nonparametric regression frameworks, such as generalized additive models (GAMs) and smoothing spline analysis of variance (SSANOVA) models, extend the generalized linear model (GLM) by allowing for unknown functional relationships between an exponential family response variable and a collection of predictor variables. The unknown functional relationships are typically estimated using penalized likelihood

    更新日期:2020-08-14
  • Model-Free Variable Selection with Matrix-Valued Predictors
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-14
    Zeda Li; Yuexiao Dong

    We introduce a novel framework for model-free variable selection with matrix-valued predictors. To test the importance of rows, columns, and submatrices of the predictor matrix in terms of predicting the response, three types of hypotheses are formulated under a unified framework. The asymptotic properties of the test statistics under the null hypothesis are established and a permutation testing algorithm

    更新日期:2020-08-14
  • Anomaly Detection in High Dimensional Data
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-13
    Priyanga Dilini Talagala; Rob J. Hyndman; Kate Smith-Miles

    The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define an anomaly as an observation where its k-nearest

    更新日期:2020-08-14
  • Marginally-calibrated deep distributional regression
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-13
    Nadja Klein; David J. Nott; Michael Stanley Smith

    Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are ‘marginally calibrated’. This is where the long run

    更新日期:2020-08-14
  • An efficient algorithm for minimizing multi non-smooth component functions
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-08-05
    Minh Pham; Anh Ninh; Hoang Le; Yufeng Liu

    Many problems in statistics and machine learning can be formulated as an optimization problem of a finite sum of non-smooth convex functions. We propose an algorithm to minimize this type of objective functions based on the idea of alternating linearization. Our algorithm retains the simplicity of contemporary methods without any restrictive assumptions on the smoothness of the loss function. We apply

    更新日期:2020-08-05
  • Model interpretation through lower-dimensional posterior summarization
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-07-21
    Spencer Woody; Carlos M. Carvalho; Jared S. Murray

    Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive ability in empirical settings, they are often difficult to interpret and do not address the underlying inferential goals of the analyst or decision maker. In this paper, we propose a modular two-stage approach

    更新日期:2020-07-22
  • U-statistical inference for hierarchical clustering
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-07-20
    Marcio Valk; Gabriela Bettella Cybis

    Clustering methods are valuable tools for the identification of patterns in high dimensional data with applications in many scientific fields. However, quantifying uncertainty in clustering is a challenging problem, particularly when dealing with High Dimension Low Sample Size (HDLSS) data. We develop a U-statistics based clustering approach that assesses statistical significance in clustering and

    更新日期:2020-07-20
  • mcvis: A new framework for collinearity discovery, diagnostic and visualization
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-30
    Chen Lin; Kevin Wang; Samuel Mueller

    Collinearity discovery through diagnostic tools is an important analysis step when performing linear regression. Despite their wide-spread 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

    更新日期:2020-06-30
  • Sparse Single Index Models for Multivariate Responses
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-30
    Yuan Feng; Luo Xiao; Eric C. Chi

    Joint 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 low-rank structures across responses. An alternating direction method of multipliers (ADMM) based algorithm is proposed

    更新日期:2020-06-30
  • Optimal Sampling for Generalized Linear Models under Measurement Constraints
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-08
    Tao Zhang; Yang Ning; David Ruppert

    Under “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

    更新日期:2020-06-08
  • Bayesian spatial clustering of extremal behaviour for hydrological variables
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-04
    Christian Rohrbeck; Jonathan A. Tawn

    To 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

    更新日期:2020-06-04
  • A slice tour for finding hollowness in high-dimensional data
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-04
    Ursula Laa; Dianne Cook; German Valencia

    Taking projections of high-dimensional data is a common analytical and visualisation technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for visualising data with concavities, or non-linear structure. It is associated with conditional distributions in statistics, and also linked brushing between plots

    更新日期:2020-06-04
  • Illumination depth
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-04
    Stanislav Nagy; Jiří Dvořák

    The 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 depth-based ordering, and introduces a depth-like function for points outside the convex hull of the support of the probability measure. The illumination is

    更新日期:2020-06-04
  • Surrogate Residuals for Discrete Choice Models
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-02
    Chao Cheng; Rui Wang; Heping Zhang

    Discrete 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

    更新日期:2020-06-02
  • Delayed acceptance ABC-SMC
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-06-02
    Richard G. Everitt; Paulina A. Rowińska

    Approximate 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

    更新日期:2020-06-02
  • Correction
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-05-28

    (2020). Correction. Journal of Computational and Graphical Statistics. Ahead of Print.

    更新日期:2020-05-28
  • Efficient Parameter Sampling for Markov Jump Processes
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-05-07
    Boqian Zhang; Vinayak Rao

    Markov jump processes are continuous-time stochastic processes widely used in a variety of applied disciplines. Inference typically proceeds via Markov chain Monte Carlo, the state-of-the-art being a uniformization-based 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

    更新日期:2020-05-07
  • Automated Redistricting Simulation Using Markov Chain Monte Carlo
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-05-07
    Benjamin Fifield; , Michael Higgins; Kosuke Imai; Alexander Tarr

    Legislative 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

    更新日期:2020-05-07
  • Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-05-07
    Evan Baker; Peter Challenor; Matt Eames

    Statistically 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

    更新日期:2020-05-07
  • Identifying Heterogeneous Effect using Latent Supervised Clustering with Adaptive Fusion
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-05-06
    Jingxiang Chen; Quoc Tran-Dinh; Michael R. Kosorok; Yufeng Liu

    Precision 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

    更新日期:2020-05-06
  • Massive parallelization boosts big Bayesian multidimensional scaling
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-05-05
    Andrew J. Holbrook; Philippe Lemey; Guy Baele; Simon Dellicour; Dirk Brockmann; Andrew Rambaut; Marc A. Suchard

    Big Bayes is the computationally intensive co-application 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 space-time, but its computational burden prevents its wider use. Crucial MDS model calculations

    更新日期:2020-05-05
  • Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-04-27
    Gregory J. Hunt; Mark A. Dane; James E. Korkola; Laura M. Heiser; Johann A. Gagnon-Bartsch

    Proper 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 high-content imaging data poses new challenges. Imaging data produce hundreds of covariates from each of thousands of images

    更新日期:2020-04-27
  • Rerandomization strategies for balancing covariates using pre-experimental longitudinal data
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-04-21
    Per Johansson; Mårten Schultzberg

    ABSTRACT 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 two-stage allocation sample scheme for randomization inference to the units in experiments that guarantees that the difference-in-mean estimator is an unbiased estimator of the sample average treatment

    更新日期:2020-04-21
  • A Pliable Lasso
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-09-05
    Robert Tibshirani; Jerome Friedman

    We 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

    更新日期:2020-04-20
  • Bivariate Residual Plots With Simulation Polygons
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-09-06
    Rafael A. Moral; John Hinde; Clarice G. B. Demétrio

    When using univariate models, goodness of fit can be assessed through many different methods, including graphical tools such as half-normal 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

    更新日期:2020-04-20
  • Estimating Time-Varying Graphical Models
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-09-03
    Jilei Yang; Jie Peng

    In this article, we study time-varying 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

    更新日期:2020-04-20
  • Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-08-29
    Sabrina Vettori; Raphaël Huser; Johan Segers; Marc G. Genton

    Describing 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 tree-based dependence structure of the max-stable nested logistic distribution, and which identifies possible clusters of extreme variables using reversible jump Markov chain Monte

    更新日期:2020-04-20
  • Estimating the Number of Clusters Using Cross-Validation
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-09-30
    Wei Fu; Patrick O. Perry

    Many clustering methods, including k-means, 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 data-driven approach to estimate the number of clusters based on a novel form of cross-validation. The proposed method differs

    更新日期:2020-04-20
  • Parallelization of a Common Changepoint Detection Method
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-09-06
    S. O. Tickle; I. A. Eckley; P. Fearnhead; K. Haynes

    Abstract 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

    更新日期:2020-04-20
  • Efficient Construction of Test Inversion Confidence Intervals Using Quantile Regression
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-09-03
    Eyal Fisher; Regev Schweiger; Saharon Rosset

    Modern 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

    更新日期:2020-04-20
  • Testing Sparsity-Inducing Penalties
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-08-19
    Maryclare Griffin; Peter D. Hoff

    Many 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

    更新日期:2020-04-20
  • Diagonal Discriminant Analysis With Feature Selection for High-Dimensional Data
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-08-16
    Sarah E. Romanes; John T. Ormerod; Jean Y. H. Yang

    We introduce a new method of performing high-dimensional discriminant analysis (DA), which we call multiDA. Starting from multiclass diagonal DA classifiers which avoid the problem of high-dimensional 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

    更新日期:2020-04-20
  • Bayesian Deep Net GLM and GLMM
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-08-16
    M.-N. Tran; N. Nguyen; D. Nott; R. Kohn

    Deep 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

    更新日期:2020-04-20
  • Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-19
    Michael Weylandt; John Nagorski; Genevera I. Allen

    Convex 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

    更新日期:2020-04-20
  • Scalable Visualization Methods for Modern Generalized Additive Models
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-19
    Matteo Fasiolo; Raphaël Nedellec; Yannig Goude; Simon N. Wood

    In 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

    更新日期:2020-04-20
  • A Function Emulation Approach for Doubly Intractable Distributions
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-23
    Jaewoo Park; Murali Haran

    Doubly 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,

    更新日期:2020-04-20
  • Scalable Bayesian Nonparametric Clustering and Classification
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-19
    Yang Ni; Peter Müller; Maurice Diesendruck; Sinead Williamson; Yitan Zhu; Yuan Ji

    We 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

    更新日期:2020-04-20
  • BIVAS: A Scalable Bayesian Method for Bi-Level Variable Selection With Applications
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-19
    Mingxuan Cai; Mingwei Dai; Jingsi Ming; Heng Peng; Jin Liu; Can Yang

    In this article, we consider a Bayesian bi-level variable selection problem in high-dimensional 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

    更新日期:2020-04-20
  • Scalable Bayesian Regression in High Dimensions With Multiple Data Sources
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-15
    Konstantinos Perrakis; Sach Mukherjee; The Alzheimer’s Disease Neuroimaging Initiative

    Applications of high-dimensional 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 ridge-type prior with shrinkage levels that are specific to each data type or source and that are set automatically by empirical Bayes. All estimation

    更新日期:2020-04-20
  • Anomaly Detection in Streaming Nonstationary Temporal Data
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-06-24
    Priyanga Dilini Talagala; Rob J. Hyndman; Kate Smith-Miles; Sevvandi Kandanaarachchi; Mario A. Muñoz

    This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series 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

    更新日期:2020-04-20
  • A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies With Auxiliary Covariates
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2019-07-02
    Tianjian Zhou; Michael J. Daniels; Peter Müller

    We 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

    更新日期:2020-04-20
  • Generalized Spatially Varying Coefficient Models
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-04-20
    Myungjin Kim; Li Wang

    In 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 quasi-likelihood approach using the bivariate penalized spline approximation technique. We show that our estimation procedure is able to handle irregularly-shaped spatial

    更新日期:2020-04-20
  • High-Dimensional Copula Variational Approximation Through Transformation
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-04-20
    Michael Stanley Smith; Rubén Loaiza-Maya; David J. Nott

    Variational 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 element-wise parametric transformation of the target distribution as multivariate Gaussian

    更新日期:2020-04-20
  • Poisson Kernel-Based Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling
    J. Comput. Graph. Stat. (IF 2.319) Pub Date : 2020-04-20
    Mojgan Golzy; Marianthi Markatou

    Spherical or directional data arise in many applications of interest. Furthermore, many nondirectional datasets can be usefully re-expressed in the form of directions and analyzed as spherical data. We have proposed a clustering algorithm using mixtures of Poisson-kernel-based densities (PKBD) on the sphere. We prove convergence of the associated generalized EM-algorithm, investigate the identifiability

    更新日期:2020-04-20
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
物理学研究前沿热点精选期刊推荐
chemistry
《自然》编辑与您分享如何成为优质审稿人-信息流
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
自然职场线上招聘会
ACS ES&T Engineering
科研绘图
ACS ES&T Water
ACS Publications填问卷
屿渡论文,编辑服务
阿拉丁试剂right
张晓晨
田蕾蕾
李闯创
刘天飞
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
X-MOL
苏州大学
廖矿标
深圳湾
试剂库存
down
wechat
bug