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 Author:
 Mkhadri, Abdallah; Ouhourane, Mohamed
 Source:
 Computational statistics & data analysis 2013 v.57 no.1 pp. 631644
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; regression analysis; Show all 4 Subjects
 Abstract:
 ... The problem of variable selection for linear regression in a high dimension model is considered. A new method, called ExtendedVISA (ExtVISA), is proposed to simultaneously select variables and encourage a grouping effect where strongly correlated predictors tend to be in or out of the model together. Moreover, ExtVISA is capable of selecting a sparse model while avoiding the overshrinkage of a ...
 DOI:
 10.1016/j.csda.2012.07.023

http://dx.doi.org/10.1016/j.csda.2012.07.023
 Author:
 SalterTownshend, Michael; Murphy, Thomas Brendan
 Source:
 Computational statistics & data analysis 2013 v.57 no.1 pp. 661671
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; social behavior; social networks; Show all 6 Subjects
 Abstract:
 ... A number of recent approaches to modeling social networks have focussed on embedding the nodes in a latent “social space”. Nodes that are in close proximity are more likely to form links than those who are distant. This naturally accounts for reciprocal and transitive relationships which are commonly found in many network datasets. The Latent Position Cluster Model is one such model that also expl ...
 DOI:
 10.1016/j.csda.2012.08.004

http://dx.doi.org/10.1016/j.csda.2012.08.004
 Author:
 Biernacki, Christophe; Jacques, Julien
 Source:
 Computational statistics & data analysis 2013 v.58 pp. 162176
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; statistical analysis; Show all 4 Subjects
 Abstract:
 ... An original and meaningful probabilistic generative model for full rank data modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process relies on paired comparisons, the insertion sort algorithm is known as being the best candidate in order to minimize the number of potential paired misclassifications for a moderate ...
 DOI:
 10.1016/j.csda.2012.08.008

http://dx.doi.org/10.1016/j.csda.2012.08.008
 Author:
 Nandi, Swagata; Kundu, Debasis; Srivastava, Rajesh Kumar
 Source:
 Computational statistics & data analysis 2013 v.58 pp. 147161
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; least squares; models; variance; Show all 5 Subjects
 Abstract:
 ... The estimation of the parameters of the twodimensional sinusoidal signal model has been addressed. The proposed method is the twodimensional extension of the onedimensional noise space decomposition method. It provides consistent estimators of the unknown parameters and they are noniterative in nature. Two pairing algorithms, which help in identifying the frequency pairs have been proposed. It ...
 DOI:
 10.1016/j.csda.2011.03.002

http://dx.doi.org/10.1016/j.csda.2011.03.002
 Author:
 Chakraborty, A.; Beamonte, M.A.; Gelfand, A.E.; Alonso, M.P.; Gargallo, P.; Salvador, M.
 Source:
 Computational statistics & data analysis 2013 v.58 pp. 292307
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; labor force; labor market; markets; models; stochastic processes; Spain; Show all 9 Subjects
 Abstract:
 ... As a result of increased mobility patterns of workers, explaining labor flows and partitioning regions into local labor markets (LLMs) have become important economic issues. For the former, it is useful to understand jointly where individuals live and where they work. For the latter, such markets attempt to delineate regions with a high proportion of workers both living and working. To address the ...
 DOI:
 10.1016/j.csda.2012.08.016

http://dx.doi.org/10.1016/j.csda.2012.08.016
 Author:
 Villegas, Cristian; Paula, Gilberto A.; Cysneiros, Francisco José A.; Galea, Manuel
 Source:
 Computational statistics & data analysis 2013 v.59 pp. 161170
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; diagnostic techniques; least squares; linear models; Show all 5 Subjects
 Abstract:
 ... The aim of this paper is to introduce generalized symmetric linear models (GSLMs) in the same sense of generalized linear models (GLMs), in which a link function is defined to establish a relationship between the mean values of symmetric distributions and linear predictors. The class of symmetric distributions contains various distributions with lighter and heavier tails than normal and hence offe ...
 DOI:
 10.1016/j.csda.2012.10.012

http://dx.doi.org/10.1016/j.csda.2012.10.012
 Author:
 Delatola, E.I.; Griffin, J.E.
 Source:
 Computational statistics & data analysis 2013 v.60 pp. 97110
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; Show all 4 Subjects
 Abstract:
 ... A Bayesian semiparametric stochastic volatility model for financial data is developed. This nonparametrically estimates the return distribution from the data allowing for stylized facts such as heavy tails of the distribution of returns whilst also allowing for correlation between the returns and changes in volatility, which is usually termed the leverage effect. An efficient MCMC algorithm is des ...
 DOI:
 10.1016/j.csda.2012.10.023

http://dx.doi.org/10.1016/j.csda.2012.10.023
 Author:
 Lee, DaeJin; Durbán, María; Eilers, Paul
 Source:
 Computational statistics & data analysis 2013 v.61 pp. 2237
 ISSN:
 01679473
 Subject:
 algorithms, etc ; analysis of variance; computer software; data collection; statistical models; Show all 5 Subjects
 Abstract:
 ... Lowrank smoothing techniques have gained much popularity in nonstandard regression modeling. In particular, penalized splines and tensor product smooths are used as flexible tools to study nonparametric relationships among several covariates. The use of standard statistical software facilitates their use for several types of problems and applications. However, when interaction terms are conside ...
 DOI:
 10.1016/j.csda.2012.11.013

http://dx.doi.org/10.1016/j.csda.2012.11.013
 Author:
 Lee, Seokho; Huang, Jianhua Z.
 Source:
 Computational statistics & data analysis 2013 v.62 pp. 2638
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; principal component analysis; Show all 3 Subjects
 Abstract:
 ... Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization–minimizati ...
 DOI:
 10.1016/j.csda.2013.01.001

http://dx.doi.org/10.1016/j.csda.2013.01.001
 Author:
 Melnykov, Volodymyr; Shen, Gang
 Source:
 Computational statistics & data analysis 2013 v.62 pp. 110
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; probability distribution; Show all 3 Subjects
 Abstract:
 ... There is a vast variety of clustering methods available in the literature. The performance of many of them strongly depends on specific patterns in data. This paper introduces a clustering procedure based on the empirical likelihood method which inherits many advantages of the classical likelihood approach without imposing restrictive probability distribution constraints. The performance of the pr ...
 DOI:
 10.1016/j.csda.2012.12.011

http://dx.doi.org/10.1016/j.csda.2012.12.011
 Author:
 Chen, Pengcheng; Zhang, Jiajia; Zhang, Riquan
 Source:
 Computational statistics & data analysis 2013 v.62 pp. 171180
 ISSN:
 01679473
 Subject:
 algorithms, etc ; coronary disease; data collection; models; nitroglycerin; patients; variance; Show all 7 Subjects
 Abstract:
 ... The frailty model is one of the most popular models used to analyze clustered failure time data, where the frailty term is used to assess an association within each cluster. The frailty model based on the semiparametric accelerated failure time model attracts less attention than the one based on the proportional hazards model due to its computational difficulties. In this paper, we relax the frail ...
 DOI:
 10.1016/j.csda.2013.01.016

http://dx.doi.org/10.1016/j.csda.2013.01.016
 Author:
 Vandewalle, Vincent; Biernacki, Christophe; Celeux, Gilles; Govaert, Gérard
 Source:
 Computational statistics & data analysis 2013 v.64 pp. 220236
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; entropy; models; Show all 5 Subjects
 Abstract:
 ... Semisupervised classification can help to improve generative classifiers by taking into account the information provided by the unlabeled data points, especially when there are far more unlabeled data than labeled data. The aim is to select a generative classification model using both unlabeled and labeled data. A predictive deviance criterion, AICcond, aiming to select a parsimonious and relevan ...
 DOI:
 10.1016/j.csda.2013.02.010

http://dx.doi.org/10.1016/j.csda.2013.02.010
 Author:
 Ghitany, M.E.; AlMutairi, D.K.; Balakrishnan, N.; AlEnezi, L.J.
 Source:
 Computational statistics & data analysis 2013 v.64 pp. 2033
 ISSN:
 01679473
 Subject:
 algorithms, etc ; confidence interval; data collection; models; statistical analysis; Show all 5 Subjects
 Abstract:
 ... A new twoparameter power Lindley distribution is introduced and its properties are discussed. These include the shapes of the density and hazard rate functions, the moments, skewness and kurtosis measures, the quantile function, and the limiting distributions of order statistics. Maximum likelihood estimation of the parameters and their estimated asymptotic standard errors are derived. Three algo ...
 DOI:
 10.1016/j.csda.2013.02.026

http://dx.doi.org/10.1016/j.csda.2013.02.026
 Author:
 Wang, Jixian; Quartey, George
 Source:
 Computational statistics & data analysis 2013 v.67 pp. 248257
 ISSN:
 01679473
 Subject:
 algorithms, etc ; computer software; cystic fibrosis; data collection; dropouts; medical treatment; models; patients; Show all 8 Subjects
 Abstract:
 ... Event duration and prevalence are important features for assessing outcomes of medical treatment. Although semiparametric approaches have been well developed for analysis of recurrent events, applications to analysis of event duration, in particular the duration of multiple overlapping events, are relatively rare. Various approaches are considered using semiparametric multiplicative models for c ...
 DOI:
 10.1016/j.csda.2013.05.023

http://dx.doi.org/10.1016/j.csda.2013.05.023
 Author:
 Wichitaksorn, Nuttanan; Tsurumi, Hiroki
 Source:
 Computational statistics & data analysis 2013 v.67 pp. 226235
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Markov chain; data collection; males; models; probability distribution; surveys; Show all 7 Subjects
 Abstract:
 ... The analysis of Tobit model with nonnormal error distribution is extended to the case of asymmetric Laplace distribution (ALD). Since the ALD probability density function is known to be continuous but not differentiable, the usual modefinding algorithms such as maximum likelihood can be difficult and result in the inconsistent parameter estimates. Various Markov chain Monte Carlo algorithms incl ...
 DOI:
 10.1016/j.csda.2013.06.003

http://dx.doi.org/10.1016/j.csda.2013.06.003
 Author:
 Rastogi, Manoj Kumar; Tripathi, Yogesh Mani
 Source:
 Computational statistics & data analysis 2013 v.67 pp. 268281
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Markov chain; confidence interval; data collection; statistical analysis; Show all 5 Subjects
 Abstract:
 ... The problem of estimating unknown parameters of a twoparameter distribution with bathtub shape is considered under the assumption that samples are hybrid censored. The maximum likelihood estimates are obtained using an EM algorithm. The Fisher information matrix is obtained as well and the asymptotic confidence intervals are constructed. Further, two bootstrap interval estimates are also proposed ...
 DOI:
 10.1016/j.csda.2013.05.022

http://dx.doi.org/10.1016/j.csda.2013.05.022
 Author:
 Givens, G.H.; Beveridge, J.R.; Phillips, P.J.; Draper, B.; Lui, Y.M.; Bolme, D.
 Source:
 Computational statistics & data analysis 2013 v.67 pp. 236247
 ISSN:
 01679473
 Subject:
 algorithms, etc ; biometry; data collection; face; linear models; multivariate analysis; Show all 6 Subjects
 Abstract:
 ... The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. A new opportunity for the application of statistical methods is driven by growing interest in biometric performance evaluation. Methods for performance evaluation seek to identify, compare and ...
 DOI:
 10.1016/j.csda.2013.05.025

http://dx.doi.org/10.1016/j.csda.2013.05.025
 Author:
 Kang, Le; Xiong, Chengjie; Tian, Lili
 Source:
 Computational statistics & data analysis 2013 v.68 pp. 326338
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Alzheimer disease; biomedical research; cohort studies; confidence interval; data collection; ethics; neuropsychological tests; risk; statistical analysis; Washington (state); Show all 11 Subjects
 Abstract:
 ... With three ordinal diagnostic categories, the most commonly used measures for the overall diagnostic accuracy are the volume under the ROC surface (VUS) and partial volume under the ROC surface (PVUS), which are the extensions of the area under the ROC curve (AUC) and partial area under the ROC curve (PAUC), respectively. A gold standard (GS) test on the true disease status is required to estimate ...
 DOI:
 10.1016/j.csda.2013.07.007

http://dx.doi.org/10.1016/j.csda.2013.07.007
 Author:
 Luong, The Minh; Rozenholc, Yves; Nuel, Gregory
 Source:
 Computational statistics & data analysis 2013 v.68 pp. 129140
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Markov chain; bioinformatics; computer software; computers; confidence interval; copy number variation; data collection; models; uncertainty; Show all 11 Subjects
 Abstract:
 ... The detection of changepoints in heterogeneous sequences is a statistical challenge with applications across a wide variety of fields. In bioinformatics, a vast amount of methodology exists to identify an ideal set of changepoints for detecting Copy Number Variation (CNV). While considerable efficient algorithms are currently available for finding the best segmentation of the data in CNV, relati ...
 DOI:
 10.1016/j.csda.2013.06.020

http://dx.doi.org/10.1016/j.csda.2013.06.020
 Author:
 Molas, Marek; Noh, Maengseok; Lee, Youngjo; Lesaffre, Emmanuel
 Source:
 Computational statistics & data analysis 2013 v.68 pp. 239250
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; linear models; rheumatoid arthritis; Show all 4 Subjects
 Abstract:
 ... Likelihood based inference for correlated data involves the evaluation of a marginal likelihood integrating out random effects. In general this integral does not have a closed form. Moreover, its numerical evaluation might create difficulties especially when the dimension of random effects is high. Hlikelihood inference has been proposed where the explicit evaluation of the integral is avoided. T ...
 DOI:
 10.1016/j.csda.2013.07.011

http://dx.doi.org/10.1016/j.csda.2013.07.011
 Author:
 Bernhardt, Paul W.; Wang, Huixia Judy; Zhang, Daowen
 Source:
 Computational statistics & data analysis 2014 v.69 pp. 8191
 ISSN:
 01679473
 Subject:
 algorithms, etc ; biomarkers; data collection; detection limit; models; statistical analysis; variance; Show all 7 Subjects
 Abstract:
 ... Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationallyefficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time mo ...
 DOI:
 10.1016/j.csda.2013.07.027

http://dx.doi.org/10.1016/j.csda.2013.07.027
 Author:
 Dimeglio, Chloé; Gallón, Santiago; Loubes, JeanMichel; Maza, Elie
 Source:
 Computational statistics & data analysis 2014 v.70 pp. 373386
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; principal geodesic analysis; Show all 3 Subjects
 Abstract:
 ... The problem of finding a template function that represents the common pattern of a sample of curves is considered. To address this issue, a novel algorithm based on a robust version of the isometric featuring mapping (Isomap) algorithm is developed. When the functional data lie on an unknown intrinsically lowdimensional smooth manifold, the corresponding empirical Fréchet median function is chose ...
 DOI:
 10.1016/j.csda.2013.09.030

http://dx.doi.org/10.1016/j.csda.2013.09.030
 Author:
 Roberts, W.J.J.
 Source:
 Computational statistics & data analysis 2014 v.70 pp. 6166
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; factor analysis; models; Show all 4 Subjects
 Abstract:
 ... An expectation–maximization (EM) algorithm for factor analysis parameter estimation when observations are missing is developed. In contrast to existing EM algorithms for this problem, the algorithm here is developed assuming the missing observations are not part of the complete data in the EM formulation. The resulting algorithm provides increased computational efficiency through sparse matrix ope ...
 DOI:
 10.1016/j.csda.2013.08.018

http://dx.doi.org/10.1016/j.csda.2013.08.018
 Author:
 CruzCano, Raul; Lee, MeiLing Ting
 Source:
 Computational statistics & data analysis 2014 v.70 pp. 88100
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; equations; microRNA; multivariate analysis; neoplasms; risk; system optimization; Show all 8 Subjects
 Abstract:
 ... Canonical correlation analysis is a popular statistical method for the study of the correlations between two sets of variables. Finding the canonical correlations between these datasets requires the inversion of their corresponding sample correlation matrices. When the number of variables is large compared to the number of experimental units it is impossible to calculate the inverse of these matri ...
 DOI:
 10.1016/j.csda.2013.09.020

http://dx.doi.org/10.1016/j.csda.2013.09.020
 Author:
 Galimberti, Giuliano; Soffritti, Gabriele
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 138150
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Monte Carlo method; data collection; models; regression analysis; Show all 5 Subjects
 Abstract:
 ... Recently, finite mixture models have been used to model the distribution of the error terms in multivariate linear regression analysis. In particular, Gaussian mixture models have been employed. A novel approach that assumes that the error terms follow a finite mixture of t distributions is introduced. This assumption allows for an extension of multivariate linear regression models, making these m ...
 DOI:
 10.1016/j.csda.2013.01.017

http://dx.doi.org/10.1016/j.csda.2013.01.017
 Author:
 Gupta, Mayetri
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 375391
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; genomewide association study; Show all 4 Subjects
 Abstract:
 ... In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering or biclustering is a more appropriate technique to use compared to onedimensional clustering. A flexible ...
 DOI:
 10.1016/j.csda.2013.07.006

http://dx.doi.org/10.1016/j.csda.2013.07.006
 Author:
 Jacques, Julien; Preda, Cristian
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 92106
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; principal component analysis; Show all 4 Subjects
 Abstract:
 ... The first modelbased clustering algorithm for multivariate functional data is proposed. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal component scores, is defined and estimated by an EMlike algorithm. The main advantage of the proposed model is its ability to take into account the ...
 DOI:
 10.1016/j.csda.2012.12.004

http://dx.doi.org/10.1016/j.csda.2012.12.004
 Author:
 Bartolucci, Francesco; Bacci, Silvia; Gnaldi, Michela
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 971985
 ISSN:
 01679473
 Subject:
 algorithms, etc ; anxiety; computer software; data collection; mathematics; models; statistical analysis; Show all 7 Subjects
 Abstract:
 ... A class of Item Response Theory (IRT) models for binary and ordinal polytomous items is illustrated and an R package for dealing with these models, named MultiLCIRT, is described. The models at issue extend traditional IRT models allowing for multidimensionality and discreteness of latent traits. They also allow for different parameterizations of the conditional distribution of the response variab ...
 DOI:
 10.1016/j.csda.2013.05.018

http://dx.doi.org/10.1016/j.csda.2013.05.018
 Author:
 Asquith, William H.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 955970
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; statistical analysis; Show all 3 Subjects
 Abstract:
 ... The implementation characteristics of two method of Lmoments (MLM) algorithms for parameter estimation of the 4parameter Asymmetric Exponential Power (AEP4) distribution are studied using the R environment for statistical computing. The objective is to validate the algorithms for general application of the AEP4 using R. An algorithm was introduced in the original study of the Lmoments for the A ...
 DOI:
 10.1016/j.csda.2012.12.013

http://dx.doi.org/10.1016/j.csda.2012.12.013
 Author:
 Vrbik, Irene; McNicholas, Paul D.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 196210
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; Show all 4 Subjects
 Abstract:
 ... Robust mixture modeling approaches using skewed distributions have recently been explored to accommodate asymmetric data. Parsimonious skewt and skewnormal analogues of the GPCM family that employ an eigenvalue decomposition of a scale matrix are introduced. The methods are compared to existing models in both unsupervised and semisupervised classification frameworks. Parameter estimation is car ...
 DOI:
 10.1016/j.csda.2013.07.008

http://dx.doi.org/10.1016/j.csda.2013.07.008
 Author:
 Eugster, Manuel J.A.; Leisch, Friedrich; Strobl, Carolin
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 9861000
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; Show all 3 Subjects
 Abstract:
 ... It is common knowledge that the performance of different learning algorithms depends on certain characteristics of the data—such as dimensionality, linear separability or sample size. However, formally investigating this relationship in an objective and reproducible way is not trivial. A new formal framework for describing the relationship between data set characteristics and the performance of di ...
 DOI:
 10.1016/j.csda.2013.08.007

http://dx.doi.org/10.1016/j.csda.2013.08.007
 Author:
 Song, Weixing; Yao, Weixin; Xing, Yanru
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 128137
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; regression analysis; Show all 3 Subjects
 Abstract:
 ... A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture of a normal and a latent distribution, this procedure is implemented by an EM algorithm which incorporates two types of missing information from the mixture class membership and ...
 DOI:
 10.1016/j.csda.2013.06.022

http://dx.doi.org/10.1016/j.csda.2013.06.022
 Author:
 AbantoValle, Carlos A.; Dey, Dipak K.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 274287
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Markov chain; cumulative distribution; data collection; models; time series analysis; variance; Show all 8 Subjects
 Abstract:
 ... A state space mixed models for binary time series where the inverse link function is modeled to be a cumulative distribution function of the scale mixture of normal (SMN) distributions. Specific inverse links examined include the normal, Studentt, slash and the variance gamma links. The threshold latent approach to represent the binary system as a linear state space model is considered. Using a B ...
 DOI:
 10.1016/j.csda.2013.01.009

http://dx.doi.org/10.1016/j.csda.2013.01.009
 Author:
 Lim, Hwa Kyung; Li, Wai Keung; Yu, Philip L.H.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 151158
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; least squares; models; Show all 4 Subjects
 Abstract:
 ... Excess zeros and overdispersion are common phenomena that limit the use of traditional Poisson regression models for modeling count data. Both excess zeros and overdispersion caused by unobserved heterogeneity are accounted for by the proposed zeroinflated Poisson (ZIP) regression mixture model. To estimate the parameters of the model, an EM algorithm with an embedded iteratively reweighted least ...
 DOI:
 10.1016/j.csda.2013.06.021

http://dx.doi.org/10.1016/j.csda.2013.06.021
 Author:
 Oh, ManSuk
 Source:
 Computational statistics & data analysis 2014 v.72 pp. 147157
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; probability; Show all 5 Subjects
 Abstract:
 ... In the RC association model for a twoway contingency table, it is often natural to impose order constraints on the score parameters of the row and column variables. In this article, a simple and efficient Bayesian model selection procedure is proposed that simultaneously compares all possible combinations of (in)equalities of successive score parameters in the order restricted RC association mode ...
 DOI:
 10.1016/j.csda.2013.11.009

http://dx.doi.org/10.1016/j.csda.2013.11.009
 Author:
 Drovandi, Christopher C.; Pettitt, Anthony N.; Henderson, Robert D.; McCombe, Pamela A.
 Source:
 Computational statistics & data analysis 2014 v.72 pp. 128146
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Markov chain; data collection; models; neurons; Show all 6 Subjects
 Abstract:
 ... Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to a loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast realtime MUNE method has proved elusive hitherto. Previously, a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been implemented to pro ...
 DOI:
 10.1016/j.csda.2013.11.003

http://dx.doi.org/10.1016/j.csda.2013.11.003
 Author:
 Moffa, Giusi; Kuipers, Jack
 Source:
 Computational statistics & data analysis 2014 v.72 pp. 252272
 ISSN:
 01679473
 Subject:
 algorithms, etc ; cities; data collection; models; variance covariance matrix; Show all 5 Subjects
 Abstract:
 ... Multivariate probit models have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses. The key for the dependence modelling is the covariance matrix of an underlying latent multivariate Gaussian. Most approaches to maximum likelihood estimation in multivariate probit regression rely on Monte Carlo EM algorithms to avoid comp ...
 DOI:
 10.1016/j.csda.2013.10.019

http://dx.doi.org/10.1016/j.csda.2013.10.019
 Author:
 Todorov, Diman; Setchi, Rossi
 Source:
 Computational statistics & data analysis 2014 v.72 pp. 105127
 ISSN:
 01679473
 Subject:
 algorithms, etc ; DNA; data collection; entropy; nucleotide sequences; sequence analysis; Show all 6 Subjects
 Abstract:
 ... An algorithm is proposed for calculating correlation measures based on entropy. The proposed algorithm allows exhaustive exploration of variable subsets on real data. Its time efficiency is demonstrated by comparison against three other variable selection methods based on entropy using 8 data sets from various domains as well as simulated data. The method is applicable to discrete data with a limi ...
 DOI:
 10.1016/j.csda.2013.10.026

http://dx.doi.org/10.1016/j.csda.2013.10.026
 Author:
 Fujita, André; Takahashi, Daniel Y.; Patriota, Alexandre G.
 Source:
 Computational statistics & data analysis 2014 v.73 pp. 2739
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Monte Carlo method; breast neoplasms; data collection; Show all 4 Subjects
 Abstract:
 ... An important and yet unsolved problem in unsupervised data clustering is how to determine the number of clusters. The proposed slope statistic is a nonparametric and data driven approach for estimating the number of clusters in a dataset. This technique uses the output of any clustering algorithm and identifies the maximum number of groups that breaks down the structure of the dataset. Intensive ...
 DOI:
 10.1016/j.csda.2013.11.012

http://dx.doi.org/10.1016/j.csda.2013.11.012
 Author:
 Lloyd, Louise K.; Forster, Jonathan J.
 Source:
 Computational statistics & data analysis 2014 v.73 pp. 189204
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; accidents; data collection; models; traffic; Great Britain; Show all 7 Subjects
 Abstract:
 ... Traffic flow data are primarily used to monitor road use and to compute road accident rates in Great Britain. The main traffic flow data used for these purposes measure annual traffic flow in vehicle kilometres, however this dataset is limited in its disaggregation. In particular, it is not possible to quantify traffic flow by different types of cars using just these flow data. Two additional sour ...
 DOI:
 10.1016/j.csda.2013.10.020

http://dx.doi.org/10.1016/j.csda.2013.10.020
 Author:
 Cabral, Celso Rômulo Barbosa; daSilva, Cibele Queiroz; Migon, Helio S.
 Source:
 Computational statistics & data analysis 2014 v.74 pp. 6480
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; covariance; data collection; dynamic models; linear models; time series analysis; Show all 7 Subjects
 Abstract:
 ... We develop a Bayesian dynamic model for modeling and forecasting multivariate time series relaxing the assumption of normality for the initial distribution of the state space parameter, and replacing it by a more flexible class of distributions, which we call Generalized SkewNormal (GSN) Distributions. We develop a version of the classic Kalman filter, again obtaining GSN predictive and filtering ...
 DOI:
 10.1016/j.csda.2013.12.008

http://dx.doi.org/10.1016/j.csda.2013.12.008
 Author:
 Alfaro, Carlos A.; Aydın, Burcu; Valencia, Carlos E.; Bullitt, Elizabeth; Ladha, Alim
 Source:
 Computational statistics & data analysis 2014 v.74 pp. 157179
 ISSN:
 01679473
 Subject:
 algorithms, etc ; brain; data collection; females; males; principal component analysis; treeline; United States; Show all 8 Subjects
 Abstract:
 ... The statistical analysis of tree structured data is a new topic in statistics with wide application areas. Some Principal Component Analysis (PCA) ideas have been previously developed for binary tree spaces. These ideas are extended to the more general space of rooted and ordered trees. Concepts such as treeline and forward principal component treeline are redefined for this more general space, ...
 DOI:
 10.1016/j.csda.2013.12.007

http://dx.doi.org/10.1016/j.csda.2013.12.007
 Author:
 Yousef, Waleed A.; Kundu, Subrata
 Source:
 Computational statistics & data analysis 2014 v.74 pp. 181197
 ISSN:
 01679473
 Subject:
 algorithms, etc ; artificial intelligence; data collection; models; Show all 4 Subjects
 Abstract:
 ... In machine learning problems a learning algorithm tries to learn the input–output dependency (relationship) of a system from a training dataset. This input–output relationship is usually deformed by a random noise. From experience, simulations, and special case theories, most practitioners believe that increasing the size of the training set improves the performance of the learning algorithm. It i ...
 DOI:
 10.1016/j.csda.2013.05.021

http://dx.doi.org/10.1016/j.csda.2013.05.021
 Author:
 Braun, Julia; Sabanés Bové, Daniel; Held, Leonhard
 Source:
 Computational statistics & data analysis 2014 v.75 pp. 190202
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; least squares; models; Show all 4 Subjects
 Abstract:
 ... The choice of generalized linear mixed models is difficult, because it involves the selection of both fixed and random effects. Classical criteria like Akaike’s information criterion (AIC) are often not suitable for the latter task, and others which are useful in linear mixed models are difficult to extend to the generalized case, especially for overdispersed data. A predictive leaveoneout cross ...
 DOI:
 10.1016/j.csda.2014.02.008

http://dx.doi.org/10.1016/j.csda.2014.02.008
 Author:
 Yen, YuMin; Yen, TsoJung
 Source:
 Computational statistics & data analysis 2014 v.76 pp. 737759
 ISSN:
 01679473
 Subject:
 algorithms, etc ; assets; data collection; system optimization; variance; Show all 5 Subjects
 Abstract:
 ... A fast method based on coordinatewise descent algorithms is developed to solve portfolio optimization problems in which asset weights are constrained by lq norms for 1≤q≤2. The method is first applied to solve a minimum variance portfolio (mvp) optimization problem in which asset weights are constrained by a weighted l1 norm and a squared l2 norm. Performances of the weighted norm penalized mvp a ...
 DOI:
 10.1016/j.csda.2013.07.010

http://dx.doi.org/10.1016/j.csda.2013.07.010
 Author:
 Kundu, Debasis; Franco, Manuel; Vivo, JuanaMaria
 Source:
 Computational statistics & data analysis 2014 v.77 pp. 98112
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; statistical analysis; Show all 4 Subjects
 Abstract:
 ... Several univariate proportional reversed hazard models have been proposed in the literature. Recently, Kundu and Gupta (2010) proposed a class of bivariate models with proportional reversed hazard marginals. It is observed that the proposed bivariate proportional reversed hazard models have a singular component. In this paper we introduce the multivariate proportional reversed hazard models along ...
 DOI:
 10.1016/j.csda.2014.02.004

http://dx.doi.org/10.1016/j.csda.2014.02.004
 Author:
 Brechmann, Eike C.; Joe, Harry
 Source:
 Computational statistics & data analysis 2014 v.77 pp. 233251
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; factor analysis; finance; models; multivariate analysis; vines; Show all 7 Subjects
 Abstract:
 ... Both in classical multivariate analysis and in modern copula modeling, correlation matrices are a central concept of dependence modeling using multivariate normal distributions and copulas. Since the number of correlation parameters quadratically increases with the number of variables, parsimonious parameterizations of large correlation matrices in terms of O(d) parameters are important. While fac ...
 DOI:
 10.1016/j.csda.2014.03.002

http://dx.doi.org/10.1016/j.csda.2014.03.002
 Author:
 Trevezas, S.; Malefaki, S.; Cournède, P.H.
 Source:
 Computational statistics & data analysis 2014 v.78 pp. 8299
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Markov chain; crops; data collection; mathematical models; organogenesis; plant growth; statistical analysis; sugar beet; Show all 9 Subjects
 Abstract:
 ... Mathematical modeling of plant growth has gained increasing interest in recent years due to its potential applications. A general family of models, known as functional–structural plant models (FSPMs) and formalized as dynamic systems, serves as the basis for the current study. Modeling, parameterization and estimation are very challenging problems due to the complicated mechanisms involved in plan ...
 DOI:
 10.1016/j.csda.2014.04.004

http://dx.doi.org/10.1016/j.csda.2014.04.004
 Author:
 Marés, Jordi; Shlomo, Natalie
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 113
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; risk; Show all 3 Subjects
 Abstract:
 ... Dissemination of data with sensitive information has an implicit risk of unauthorized disclosure. Several masking methods have been developed in order to protect the data without the loss of too much information. One such method is the Post Randomization Method (PRAM) based on perturbations of a categorical variable according to a Markov probability transition matrix. The method has the drawback t ...
 DOI:
 10.1016/j.csda.2014.05.002

http://dx.doi.org/10.1016/j.csda.2014.05.002
 Author:
 Costa, D.R.; Lachos, V.H.; Bazan, J.L.; Azevedo, C.L.N.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 248260
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Monte Carlo method; data collection; diagnostic techniques; education; factor analysis; models; selection criteria; Show all 9 Subjects
 Abstract:
 ... Tobit confirmatory factor analysis is particularly useful in analysis of multivariate data with censored information. Two methods for estimating multivariate Tobit confirmatory factor analysis models with covariates from a Bayesian and likelihoodbased perspectives are proposed. In contrast with previous likelihoodbased developments that consider Monte Carlo simulations for maximum likelihood est ...
 DOI:
 10.1016/j.csda.2014.05.021

http://dx.doi.org/10.1016/j.csda.2014.05.021