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 Author:
 Wu, Zizhen; Hitchcock, David B.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 121136
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; cell cycle; genes; growth curves; yeasts
 Abstract:
 ... We develop a Bayesian method that simultaneously registers and clusters functional data of interest. Unlike other existing methods, which often assume a simple translation in the time domain, our method uses a discrete approximation generated from the family of Dirichlet distributions to allow warping functions of great flexibility. Under this Bayesian framework, a MCMC algorithm is proposed for p ...
 DOI:
 10.1016/j.csda.2016.02.010

http://dx.doi.org/10.1016/j.csda.2016.02.010
 Author:
 Wilson, Huon; Keich, Uri
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 300315
 ISSN:
 01679473
 Subject:
 algorithms; probability
 Abstract:
 ... A novel method is presented for fast convolution of a pair of probability mass functions defined on a finite lattice with guaranteed accuracy of all computed values. This method, called aFFTC (accurate FFT convolution), utilizes the Fast Fourier Transform (FFT) for the gain in speed, but relying on a rigorous analysis of the propagation of roundoff error, it can detect and circumvent the accumula ...
 DOI:
 10.1016/j.csda.2016.03.010

http://dx.doi.org/10.1016/j.csda.2016.03.010
 Author:
 O’Brien, Travis A.; Kashinath, Karthik; Cavanaugh, Nicholas R.; Collins, William D.; O’Brien, John P.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 148160
 ISSN:
 01679473
 Subject:
 covariance; probability distribution
 Abstract:
 ... Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a ke ...
 DOI:
 10.1016/j.csda.2016.02.014

http://dx.doi.org/10.1016/j.csda.2016.02.014
 Author:
 Cipolli III, William; Hanson, Timothy; McLain, Alexander C.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 6479
 ISSN:
 01679473
 Subject:
 Bayesian theory; DNA microarrays; Internet; computer software; models; normal distribution
 Abstract:
 ... Multiple testing, or multiplicity problems often require testing several means with the assumption of rejecting infrequently, as motivated by the need to analyze DNA microarray data. The goal is to keep the combined rate of false discoveries and nondiscoveries as small as possible. A discrete approximation to a Polya tree prior that enjoys fast, conjugate updating, centered at the usual Gaussian ...
 DOI:
 10.1016/j.csda.2016.02.016

http://dx.doi.org/10.1016/j.csda.2016.02.016
 Author:
 Lee, Namgil; Choi, Hyemi; Kim, SungHo
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 250276
 ISSN:
 01679473
 Subject:
 Bayesian theory; gene expression; models; sampling; time series analysis
 Abstract:
 ... We propose Bayesian shrinkage methods for coefficient estimation for highdimensional vector autoregressive (VAR) models using scale mixtures of multivariate normal distributions for independently sampled additive noises. We also suggest an efficient selection procedure for the shrinkage parameter as a computationally feasible alternative to the traditional MCMC sampling methods for highdimension ...
 DOI:
 10.1016/j.csda.2016.03.007

http://dx.doi.org/10.1016/j.csda.2016.03.007
 Author:
 Kim, Yongku; Berliner, L. Mark
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 8092
 ISSN:
 01679473
 Subject:
 Bayesian theory; climate; dynamic models; information sources; prediction; space and time; temperature
 Abstract:
 ... Spatiotemporal processes show complicated and different patterns across different space–time scales. Each process that we attempt to model must be considered in the context of its own spatial and temporal resolution. Both scientific understanding and observed data vary in form and content across scale. Such information sources can be combined through Bayesian hierarchical framework. This approach ...
 DOI:
 10.1016/j.csda.2016.02.013

http://dx.doi.org/10.1016/j.csda.2016.02.013
 Author:
 McElroy, Tucker S.; Holan, Scott H.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 4456
 ISSN:
 01679473
 Subject:
 case studies; models; time series analysis
 Abstract:
 ... Gegenbauer processes allow for flexible and convenient modeling of time series data with multiple spectral peaks, where the qualitative description of these peaks is via the concept of cyclical longrange dependence. The Gegenbauer class is extensive, including ARFIMA, seasonal ARFIMA, and GARMA processes as special cases. Model estimation is challenging for Gegenbauer processes when multiple zero ...
 DOI:
 10.1016/j.csda.2016.02.004

http://dx.doi.org/10.1016/j.csda.2016.02.004
 Author:
 Lee, Minjung; Han, Junhee
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 5763
 ISSN:
 01679473
 Subject:
 breast neoplasms; confidence interval; risk
 Abstract:
 ... Quantile inference with adjustment for covariates has not been widely investigated on competing risks data. We propose covariateadjusted quantile inferences based on the causespecific proportional hazards regression of the cumulative incidence function. We develop the construction of confidence intervals for quantiles of the cumulative incidence function given a value of covariates and for the d ...
 DOI:
 10.1016/j.csda.2016.02.012

http://dx.doi.org/10.1016/j.csda.2016.02.012
 Author:
 Gross, Samuel M.; Tibshirani, Robert
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 226235
 ISSN:
 01679473
 Subject:
 algorithms; artificial intelligence; credit; data collection; models; regression analysis
 Abstract:
 ... A model is presented for the supervised learning problem where the observations come from a fixed number of prespecified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind. The approach is applied ...
 DOI:
 10.1016/j.csda.2016.02.015

http://dx.doi.org/10.1016/j.csda.2016.02.015
 Author:
 Zhou, Lixing; Takane, Yoshio; Hwang, Heungsun
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 93109
 ISSN:
 01679473
 Subject:
 algorithms; brain; least squares; structural equation modeling
 Abstract:
 ... Effective connectivity in functional neuroimaging studies is defined as the time dependent causal influence that a certain brain region of interest (ROI) exerts on another. A new method of structural equation modeling (SEM) is proposed for analyzing common patterns among multiple subjects’ effective connectivity. The proposed method, called Dynamic GSCANO (Generalized Structured Canonical Correlat ...
 DOI:
 10.1016/j.csda.2016.03.001

http://dx.doi.org/10.1016/j.csda.2016.03.001
 Author:
 Ding, Wei; Song, Peter X.K.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 111
 ISSN:
 01679473
 Subject:
 algorithms; regression analysis
 Abstract:
 ... Rankbased correlation is widely used to measure dependence between variables when their marginal distributions are skewed. Estimation of such correlation is challenged by both the presence of missing data and the need for adjusting for confounding factors. In this paper, we consider a unified framework of Gaussian copula regression that enables us to estimate either Pearson correlation or rankba ...
 DOI:
 10.1016/j.csda.2016.01.008

http://dx.doi.org/10.1016/j.csda.2016.01.008
 Author:
 Kleiber, William
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 277288
 ISSN:
 01679473
 Subject:
 algorithms; climate models; data collection; deformation; normal distribution; temperature; variance; variance covariance matrix; Colorado
 Abstract:
 ... Simulation of random fields is a fundamental requirement for many spatial analyses. For small spatial networks, simulations can be produced using direct manipulations of the covariance matrix. Larger high resolution simulations are most easily available for stationary processes, where algorithms such as circulant embedding can be used to simulate a process at millions of locations. We discuss an a ...
 DOI:
 10.1016/j.csda.2016.03.005

http://dx.doi.org/10.1016/j.csda.2016.03.005
 Author:
 Hosseini, Reshad; Sra, Suvrit; Theis, Lucas; Bethge, Matthias
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 2943
 ISSN:
 01679473
 Subject:
 algorithms; models; statistical analysis
 Abstract:
 ... The authors study modeling and inference with the Elliptical Gamma Distribution (EGD). In particular, Maximum likelihood (ML) estimation for EGD scatter matrices is considered, a task for which the authors present new fixedpoint algorithms. The algorithms are shown to be efficient and convergent to global optima despite nonconvexity. Moreover, they turn out to be much faster than both a wellkno ...
 DOI:
 10.1016/j.csda.2016.02.009

http://dx.doi.org/10.1016/j.csda.2016.02.009
 Author:
 Mandal, B.N.; Ma, Jun
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 289299
 ISSN:
 01679473
 Subject:
 algorithms; linear models; regression analysis; standard deviation
 Abstract:
 ... In regression modeling, often a restriction that regression coefficients are nonnegative is faced. The problem of model selection in nonnegative generalized linear models (NNGLM) is considered using lasso, where regression coefficients in the linear predictor are subject to nonnegative constraints. Thus, nonnegatively constrained regression coefficient estimation is sought by maximizing the pe ...
 DOI:
 10.1016/j.csda.2016.03.009

http://dx.doi.org/10.1016/j.csda.2016.03.009
 Author:
 Hu, Hao; Wu, Yichao; Yao, Weixin
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 137147
 ISSN:
 01679473
 Subject:
 algorithms; models; statistical analysis
 Abstract:
 ... Finite mixture models are useful tools and can be estimated via the EM algorithm. A main drawback is the strong parametric assumption about the component densities. In this paper, a much more flexible mixture model is considered, which assumes each component density to be logconcave. Under fairly general conditions, the logconcave maximum likelihood estimator (LCMLE) exists and is consistent. Nu ...
 DOI:
 10.1016/j.csda.2016.03.002

http://dx.doi.org/10.1016/j.csda.2016.03.002
 Author:
 Bedair, Khaled; Hong, Yili; Li, Jie; AlKhalidi, Hussein R.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 161173
 ISSN:
 01679473
 Subject:
 algorithms; dietary supplements; models; regression analysis; skin neoplasms
 Abstract:
 ... Multitype recurrent event data arise in many situations when two or more different event types may occur repeatedly over an observation period. For example, in a randomized controlled clinical trial to study the efficacy of nutritional supplements for skin cancer prevention, there can be two types of skin cancer events occur repeatedly over time. The research objectives of analyzing such data oft ...
 DOI:
 10.1016/j.csda.2016.01.018

http://dx.doi.org/10.1016/j.csda.2016.01.018
 Author:
 Korobilis, Dimitris
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 110120
 ISSN:
 01679473
 Subject:
 Bayesian theory; models
 Abstract:
 ... Bayesian shrinkage priors have been very popular in estimating vector autoregressions (VARs) of possibly large dimensions. Many of these priors are not appropriate for multicountry settings, as they cannot account for the type of restrictions typically met in panel vector autoregressions (PVARs). With this in mind, new parametric and semiparametric priors for PVARs are proposed, which perform va ...
 DOI:
 10.1016/j.csda.2016.02.011

http://dx.doi.org/10.1016/j.csda.2016.02.011
 Author:
 Hatjispyros, Spyridon J.; Nicoleris, Theodoros; Walker, Stephen G.
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 236249
 ISSN:
 01679473
 Subject:
 prediction; probability
 Abstract:
 ... The construction of pairwise dependence between m random density functions each of which is modeled as a mixture of Dirichlet processes is considered. The key to this is how to create dependencies between random Dirichlet processes. A method previously used for creating pairwise dependence is adapted, with the simplification that all random Dirichlet processes share the same atoms. The main conten ...
 DOI:
 10.1016/j.csda.2016.03.008

http://dx.doi.org/10.1016/j.csda.2016.03.008
 Author:
 Li, Qi; Lian, Heng; Zhu, Fukang
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 209225
 ISSN:
 01679473
 Subject:
 autocorrelation; data collection; models; prediction; stock exchange
 Abstract:
 ... A closedform estimator and its several robust versions for the integervalued GARCH(1, 1) model are proposed. These estimators are easy to implement and do not require the use of any numerical optimization procedure. Consistency and asymptotic normality for the nonrobust closedform estimator is established. The robustification of the closedform estimator is done by replacing the sample mean an ...
 DOI:
 10.1016/j.csda.2016.03.006

http://dx.doi.org/10.1016/j.csda.2016.03.006
 Author:
 Hobæk Haff, Ingrid; Aas, Kjersti; Frigessi, Arnoldo; Lacal, Virginia
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 186208
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; graphs; models; prediction; vines
 Abstract:
 ... Learning the structure of a Bayesian Network from multidimensional data is an important task in many situations, as it allows understanding conditional (in)dependence relations which in turn can be used for prediction. Current methods mostly assume a multivariate normal or a discrete multinomial model. A new greedy learning algorithm for continuous nonGaussian variables, where marginal distributi ...
 DOI:
 10.1016/j.csda.2016.03.003

http://dx.doi.org/10.1016/j.csda.2016.03.003