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Journal of Computational and Graphical Statistics

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https://www.readbyqxmd.com/read/28239248/efficient-computation-of-the-joint-sample-frequency-spectra-for-multiple-populations
#1
John A Kamm, Jonathan Terhorst, Yun S Song
A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences and provides a highly efficient dimensional reduction of large-scale population genomic variation data. Recently, there has been much interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on...
2017: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/28239247/bayesian-model-assessment-in-joint-modeling-of-longitudinal-and-survival-data-with-applications-to-cancer-clinical-trials
#2
Danjie Zhang, Ming-Hui Chen, Joseph G Ibrahim, Mark E Boye, Wei Shen
Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes (PROs). In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately...
2017: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27175055/covariance-partition-priors-a-bayesian-approach-to-simultaneous-covariance-estimation-for-longitudinal-data
#3
J T Gaskins, M J Daniels
The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times...
January 2, 2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/28316461/convex-modeling-of-interactions-with-strong-heredity
#4
Asad Haris, Daniela Witten, Noah Simon
We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMILY, a very general framework for this task. Our proposal is a generalization of several existing methods, such as VANISH [Radchenko and James, 2010], hierNet [Bien et al., 2013], the all-pairs lasso, and the lasso using only main effects. It can be formulated as the solution to a convex optimization problem, which we solve using an efficient alternating directions method of multipliers (ADMM) algorithm...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/28239246/fused-lasso-additive-model
#5
Ashley Petersen, Daniela Witten, Noah Simon
We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/28133430/accelerated-path-following-iterative-shrinkage-thresholding-algorithm-with-application-to-semiparametric-graph-estimation
#6
Tuo Zhao, Han Liu
We propose an accelerated path-following iterative shrinkage thresholding algorithm (APISTA) for solving high dimensional sparse nonconvex learning problems. The main difference between APISTA and the path-following iterative shrinkage thresholding algorithm (PISTA) is that APISTA exploits an additional coordinate descent subroutine to boost the computational performance. Such a modification, though simple, has profound impact: APISTA not only enjoys the same theoretical guarantee as that of PISTA, i.e., APISTA attains a linear rate of convergence to a unique sparse local optimum with good statistical properties, but also significantly outperforms PISTA in empirical benchmarks...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/28082825/bayesian-variable-selection-on-model-spaces-constrained-by-heredity-conditions
#7
Daniel Taylor-Rodriguez, Andrew Womack, Nikolay Bliznyuk
This paper investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type of dependence found in polynomial response surfaces of orders two and higher, whose model spaces are required to satisfy weak or strong heredity conditions. These conditions restrict the inclusion of higher-order terms depending upon the inclusion of lower-order parent terms. We develop classes of priors on the model space, investigate their theoretical and finite sample properties, and provide a Metropolis-Hastings algorithm for searching the space of models...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27974868/sr-hardi-spatially-regularizing-high-angular-resolution-diffusion-imaging
#8
Shangbang Rao, Joseph G Ibrahim, Jian Cheng, Pew-Thian Yap, Hongtu Zhu
High angular resolution diffusion imaging (HARDI) has recently been of great interest in mapping the orientation of intra-voxel crossing fibers, and such orientation information allows one to infer the connectivity patterns prevalent among different brain regions and possible changes in such connectivity over time for various neurodegenerative and neuropsychiatric diseases. The aim of this paper is to propose a penalized multi-scale adaptive regression model (PMARM) framework to spatially and adaptively infer the orientation distribution function (ODF) of water diffusion in regions with complex fiber configurations...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27891045/reinforced-angle-based-multicategory-support-vector-machines
#9
Chong Zhang, Yufeng Liu, Junhui Wang, Hongtu Zhu
The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of k classification functions for a k-class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with k - 1 functions directly...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27752219/parameter-expanded-algorithms-for-bayesian-latent-variable-modeling-of-genetic-pleiotropy-data
#10
Lizhen Xu, Radu V Craiu, Lei Sun, Andrew D Paterson
Motivated by genetic association studies of pleiotropy, we propose a Bayesian latent variable approach to jointly study multiple outcomes. The models studied here can incorporate both continuous and binary responses, and can account for serial and cluster correlations. We consider Bayesian estimation for the model parameters, and we develop a novel MCMC algorithm that builds upon hierarchical centering and parameter expansion techniques to efficiently sample from the posterior distribution. We evaluate the proposed method via extensive simulations and demonstrate its utility with an application to aa association study of various complication outcomes related to type 1 diabetes...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27667910/laplace-variational-approximation-for-semiparametric-regression-in-the-presence-of-heteroskedastic-errors
#11
Bruce D Bugbee, F Jay Breidt, Mark J van der Woerd
Variational approximations provide fast, deterministic alternatives to Markov Chain Monte Carlo for Bayesian inference on the parameters of complex, hierarchical models. Variational approximations are often limited in practicality in the absence of conjugate posterior distributions. Recent work has focused on the application of variational methods to models with only partial conjugacy, such as in semiparametric regression with heteroskedastic errors. Here, both the mean and log variance functions are modeled as smooth functions of covariates...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27524872/bayesian-ensemble-trees-bet-for-clustering-and-prediction-in-heterogeneous-data
#12
Leo L Duan, John P Clancy, Rhonda D Szczesniak
We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian Ensemble Trees (BET) and model them as a Dirichlet process. We show that BET determines the optimal number of trees by adapting to the data heterogeneity. Compared with the other ensemble methods, BET requires much fewer trees and shows equivalent prediction accuracy using weighted averaging...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27217713/computational-aspects-of-optional-p%C3%A3-lya-tree
#13
Hui Jiang, John Chong Mu, Kun Yang, Chao Du, Luo Lu, Wing Hung Wong
Optional Pólya tree (OPT) is a flexible nonparametric Bayesian prior for density estimation. Despite its merits, the computation for OPT inference is challenging. In this paper we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named limited-lookahead optional Pólya tree (LL-OPT), aims at accelerating the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewise linear density estimate...
2016: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/26858518/gene-regulation-network-inference-with-joint-sparse-gaussian-graphical-models
#14
Hyonho Chun, Xianghua Zhang, Hongyu Zhao
Revealing biological networks is one key objective in systems biology. With microarrays, researchers now routinely measure expression profiles at the genome level under various conditions, and, such data may be utilized to statistically infer gene regulation networks. Gaussian graphical models (GGMs) have proven useful for this purpose by modeling the Markovian dependence among genes. However, a single GGM may not be adequate to describe the potentially differing networks across various conditions, and hence it is more natural to infer multiple GGMs from such data...
October 1, 2015: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/26539023/copcar-a-flexible-regression-model-for-areal-data
#15
John Hughes
Non-Gaussian spatial data are common in many fields. When fitting regressions for such data, one needs to account for spatial dependence to ensure reliable inference for the regression coefficients. The two most commonly used regression models for spatially aggregated data are the automodel and the areal generalized linear mixed model (GLMM). These models induce spatial dependence in different ways but share the smoothing approach, which is intuitive but problematic. This article develops a new regression model for areal data...
September 16, 2015: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/27114697/convexlar-an-extension-of-least-angle-regression
#16
Wei Xiao, Yichao Wu, Hua Zhou
The least angle regression (LAR) was proposed by Efron, Hastie, Johnstone and Tibshirani (2004) for continuous model selection in linear regression. It is motivated by a geometric argument and tracks a path along which the predictors enter successively and the active predictors always maintain the same absolute correlation (angle) with the residual vector. Although it gains popularity quickly, its extensions seem rare compared to the penalty methods. In this expository article, we show that the powerful geometric idea of LAR can be generalized in a fruitful way...
July 1, 2015: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/26101465/role-analysis-in-networks-using-mixtures-of-exponential-random-graph-models
#17
Michael Salter-Townshend, Thomas Brendan Murphy
A novel and flexible framework for investigating the roles of actors within a network is introduced. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes into roles. We refer to this model as the ego-ERGM. An Expectation-Maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation...
June 1, 2015: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/26361433/estimating-and-identifying-unspecified-correlation-structure-for-longitudinal-data
#18
Jianhua Hu, Peng Wang, Annie Qu
Identifying correlation structure is important to achieving estimation efficiency in analyzing longitudinal data, and is also crucial for drawing valid statistical inference for large size clustered data. In this paper, we propose a nonparametric method to estimate the correlation structure, which is applicable for discrete longitudinal data. We utilize eigenvector-based basis matrices to approximate the inverse of the empirical correlation matrix and determine the number of basis matrices via model selection...
April 1, 2015: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/26347592/functional-additive-mixed-models
#19
Fabian Scheipl, Ana-Maria Staicu, Sonja Greven
We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms...
April 1, 2015: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/26345204/sparse-regression-by-projection-and-sparse-discriminant-analysis
#20
Xin Qi, Ruiyan Luo, Raymond J Carroll, Hongyu Zhao
Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross validation procedure to achieve the largest prediction accuracy...
April 1, 2015: Journal of Computational and Graphical Statistics
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