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 Author:
 Karimi, Belhal; Lavielle, Marc; Moulines, Eric
 Source:
 Computational statistics & data analysis 2020 v.141 pp. 123138
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; statistical analysis; Show all 3 Subjects
 Abstract:
 ... The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely used to perform such sampling, but this method is known to converge slowly for medium dimensional problems, or when the joint structure of the distributions to sample is spatially heterogeneous. The main contribution consi ...
 DOI:
 10.1016/j.csda.2019.07.001

https://dx.doi.org/10.1016/j.csda.2019.07.001
 Author:
 Chen, PingYang; Chen, RayBing; Lin, C. Devon
 Source:
 Computational statistics & data analysis 2018 v.118 pp. 8497
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... This paper considers the construction of Doptimal twolevel orthogonal arrays that allow for the joint estimation of all main effects and a specified set of twofactor interactions. A sharper upper bound on the determinant of the related matrix is derived. To numerically obtain Doptimal and nearly Doptimal orthogonal arrays of large run sizes, an efficient search procedure is proposed based on ...
 DOI:
 10.1016/j.csda.2017.08.012

http://dx.doi.org/10.1016/j.csda.2017.08.012
 Author:
 Wang, Cheng; Jiang, Binyan
 Source:
 Computational statistics & data analysis 2020 v.142 pp. 106812
 ISSN:
 01679473
 Subject:
 algorithms, etc ; learning; variance covariance matrix; Show all 3 Subjects
 Abstract:
 ... The estimation of high dimensional precision matrices has been a central topic in statistical learning. However, as the number of parameters scales quadratically with the dimension p, many stateoftheart methods do not scale well to solve problems with a very large p. In this paper, we propose a very efficient algorithm for precision matrix estimation via penalized quadratic loss functions. Unde ...
 DOI:
 10.1016/j.csda.2019.106812

https://dx.doi.org/10.1016/j.csda.2019.106812
 Author:
 Mazo, Gildas; Averyanov, Yaroslav
 Source:
 Computational statistics & data analysis 2019 v.138 pp. 170189
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; Show all 2 Subject
 Abstract:
 ... A novel algorithm for performing inference and/or clustering in semiparametric copulabased mixture models is presented. The standard kernel density estimator is replaced by a weighted version that permits to take into account the constraints put on the underlying marginal densities. Lower misclassification error rates and better estimates are obtained on simulations. The pointwise consistency of ...
 DOI:
 10.1016/j.csda.2019.04.010

https://dx.doi.org/10.1016/j.csda.2019.04.010
 Author:
 Gregory, Alastair
 Source:
 Computational statistics & data analysis 2019 v.135 pp. 5669
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; Show all 2 Subject
 Abstract:
 ... Empirical copula functions can be used to model the dependence structure of multivariate data. The Greenwald and Khanna algorithm is adapted in order to provide a spacememory efficient approximation to the empirical copula function of a bivariate stream of data. A succinct spacememory efficient summary of values seen in the stream up to a certain time is maintained and can be queried at any poin ...
 DOI:
 10.1016/j.csda.2019.01.015

https://dx.doi.org/10.1016/j.csda.2019.01.015
 Author:
 CevallosValdiviezo, Holger; Van Aelst, Stefan
 Source:
 Computational statistics & data analysis 2019 v.134 pp. 171185
 ISSN:
 01679473
 Subject:
 algorithms, etc ; ingredients; Show all 2 Subject
 Abstract:
 ... Dimension reduction is often an important step in the analysis of highdimensional data. PCA is a popular technique to find the best lowdimensional approximation of highdimensional data. However, classical PCA is very sensitive to atypical data. Robust methods to estimate the lowdimensional subspace that best approximates the regular data have been proposed. However, for highdimensional data t ...
 DOI:
 10.1016/j.csda.2018.12.013

https://dx.doi.org/10.1016/j.csda.2018.12.013
 Author:
 Yoshida, Takuma; Naito, Kanta
 Source:
 Computational statistics & data analysis 2019 v.134 pp. 123143
 ISSN:
 01679473
 Subject:
 algorithms, etc ; risk; Show all 2 Subject
 Abstract:
 ... This paper studies a curve estimation based on empirical risk minimization. The estimator is composed as a convex combination of words (learners) in a dictionary. A word is selected in each step of the proposed stagewise algorithm, which minimizes a certain divergence measure. A nonasymptotic error bound of the estimator is developed, and it is shown that the error bound becomes sharp as the numb ...
 DOI:
 10.1016/j.csda.2018.12.011

https://dx.doi.org/10.1016/j.csda.2018.12.011
 Author:
 Xiao, Yuanhui
 Source:
 Computational statistics & data analysis 2017 v.105 pp. 5358
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... By using the brute force algorithm, the application of the twodimensional twosample Kolmogorov–Smirnov test can be prohibitively computationally expensive. Thus a fast algorithm for computing the twosample Kolmogorov–Smirnov test statistic is proposed to alleviate this problem. The newly proposed algorithm is O(n) times more efficient than the brute force algorithm, where n is the sum of the tw ...
 DOI:
 10.1016/j.csda.2016.07.014

http://dx.doi.org/10.1016/j.csda.2016.07.014
 Author:
 Wang, WanLun; Castro, Luis M.; Lachos, Victor H.; Lin, TsungI
 Source:
 Computational statistics & data analysis 2019 v.140 pp. 104121
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; statistical analysis; Show all 3 Subjects
 Abstract:
 ... Mixtures of factor analyzers (MFA) provide a promising tool for modeling and clustering highdimensional data that contain an overwhelmingly large number of attributes measured on individuals arisen from a heterogeneous population. Due to the restriction of experimental apparatus, measurements can be limited to some lower and/or upper detection bounds and thus the data are possibly censored. In th ...
 DOI:
 10.1016/j.csda.2019.06.001

https://dx.doi.org/10.1016/j.csda.2019.06.001
 Author:
 Ng, Kenyon; Turlach, Berwin A.; Murray, Kevin
 Source:
 Computational statistics & data analysis 2019 v.138 pp. 1326
 ISSN:
 01679473
 Subject:
 algorithms, etc ; regression analysis; Show all 2 Subject
 Abstract:
 ... An algorithm is proposed that enables the imposition of shape constraints on regression curves, without requiring the constraints to be written as closedform expressions, nor assuming the functional form of the loss function. This algorithm is based on Sequential Monte Carlo–SimulatedAnnealing and only relies on an indicator function that assesses whether or not the constraints are fulfilled, thu ...
 DOI:
 10.1016/j.csda.2019.03.011

https://dx.doi.org/10.1016/j.csda.2019.03.011
 Author:
 Matsuda, Takeru; Komaki, Fumiyasu
 Source:
 Computational statistics & data analysis 2019 v.137 pp. 195210
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; shrinkage; variance; Show all 4 Subjects
 Abstract:
 ... We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris. Since the EB algorithm is derived as the Expectation–Maximization algorithm applied to a simple model, it does not require heuristic parameter tuning other than tolerance. Also, it can account for the hetero ...
 DOI:
 10.1016/j.csda.2019.02.006

https://dx.doi.org/10.1016/j.csda.2019.02.006
 Author:
 Bergé, Laurent R.; Bouveyron, Charles; Corneli, Marco; Latouche, Pierre
 Source:
 Computational statistics & data analysis 2019 v.137 pp. 247270
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; Show all 3 Subjects
 Abstract:
 ... Textual interaction data involving two disjoint sets of individuals/objects are considered. An example of such data is given by the reviews on web platforms (e.g. Amazon, TripAdvisor, etc.) where buyers comment on products/services they bought. A new generative model, the latent topic block model (LTBM), is developed along with an inference algorithm to simultaneously partition the elements of eac ...
 DOI:
 10.1016/j.csda.2019.03.005

https://dx.doi.org/10.1016/j.csda.2019.03.005
 Author:
 Chaudhuri, Arin; Hu, Wenhao
 Source:
 Computational statistics & data analysis 2019 v.135 pp. 1524
 ISSN:
 01679473
 Subject:
 algorithms, etc ; covariance; data collection; Show all 3 Subjects
 Abstract:
 ... Classical dependence measures such as Pearson correlation, Spearman’s ρ, and Kendall’s τ can detect only monotonic or linear dependence. To overcome these limitations, Székely et al. proposed distance covariance and its derived correlation. The distance covariance is a weighted L2 distance between the joint characteristic function and the product of marginal distributions; it is 0 if and only if t ...
 DOI:
 10.1016/j.csda.2019.01.016

https://dx.doi.org/10.1016/j.csda.2019.01.016
 Author:
 Fang, Fang; Chen, Yuanyuan
 Source:
 Computational statistics & data analysis 2019 v.133 pp. 180194
 ISSN:
 01679473
 Subject:
 algorithms, etc ; credit; engineering; models; Show all 4 Subjects
 Abstract:
 ... Credit scoring plays a critical role in many areas such as business, finance, engineering and health. The Kolmogorov–Smirnov statistic is one of the most important performance evaluation criteria for scoring methods and has been widely used in practice. However, none of the existing scoring methods deals with the Kolmogorov–Smirnov statistic directly at the modeling stage. To fill the gap, a new c ...
 DOI:
 10.1016/j.csda.2018.10.004

https://dx.doi.org/10.1016/j.csda.2018.10.004
 Author:
 Marbac, Matthieu; Vandewalle, Vincent
 Source:
 Computational statistics & data analysis 2019 v.132 pp. 167179
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; Show all 3 Subjects
 Abstract:
 ... In the framework of modelbased clustering, a model allowing several latent class variables is proposed. This model assumes that the distribution of the observed data can be factorized into several independent blocks of variables. Each block is assumed to follow a latent class model (i.e., mixture with conditional independence assumption). The proposed model includes variable selection, as a speci ...
 DOI:
 10.1016/j.csda.2018.06.013

https://dx.doi.org/10.1016/j.csda.2018.06.013
 Author:
 Wei, Yuhong; Tang, Yang; McNicholas, Paul D.
 Source:
 Computational statistics & data analysis 2019 v.130 pp. 1841
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; Show all 3 Subjects
 Abstract:
 ... Robust clustering from incomplete data is an important topic because, in many practical situations, real datasets are heavytailed, asymmetric, and/or have arbitrary patterns of missing observations. Flexible methods and algorithms for modelbased clustering are presented via mixture of the generalized hyperbolic distributions and its limiting case, the mixture of multivariate skewt distributions ...
 DOI:
 10.1016/j.csda.2018.08.016

https://dx.doi.org/10.1016/j.csda.2018.08.016
 Author:
 Dyckerhoff, Rainer; Mozharovskyi, Pavlo
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 1930
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... For computing the exact value of the halfspace depth of a point w.r.t. a data cloud of n points in arbitrary dimension, a theoretical framework is suggested. Based on this framework a whole class of algorithms can be derived. In all of these algorithms the depth is calculated as the minimum over a finite number of depth values w.r.t. proper projections of the data cloud. Three variants of this cla ...
 DOI:
 10.1016/j.csda.2015.12.011

http://dx.doi.org/10.1016/j.csda.2015.12.011
 Author:
 Lee, Sangin; Kwon, Sunghoon; Kim, Yongdai
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 275286
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... In this paper, we propose an optimization algorithm called the modified local quadratic approximation algorithm for minimizing various ℓ1penalized convex loss functions. The proposed algorithm iteratively solves ℓ1penalized local quadratic approximations of the loss function, and then modifies the solution whenever it fails to decrease the original ℓ1penalized loss function. As an extension, we ...
 DOI:
 10.1016/j.csda.2015.08.019

http://dx.doi.org/10.1016/j.csda.2015.08.019
 Author:
 Kirschstein, T.; Liebscher, S.; Porzio, G.C.; Ragozini, G.
 Source:
 Computational statistics & data analysis 2016 v.93 pp. 456468
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... Among the measures of a distribution’s location, the mode is probably the least often used, although it has some appealing properties. Estimators for the mode of univariate distributions are widely available. However, few contributions can be found for the multivariate case. A consistent direct multivariate mode estimation procedure, called minimum volume peeling, can be outlined as follows. The a ...
 DOI:
 10.1016/j.csda.2015.04.012

http://dx.doi.org/10.1016/j.csda.2015.04.012
 Author:
 Dai, Xinjie; Niu, Cuizhen; Guo, Xu
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 1531
 ISSN:
 01679473
 Subject:
 algorithms, etc ; statistics; Show all 2 Subject
 Abstract:
 ... In this paper, we consider testing for central symmetry and inference of the unknown center with multivariate data. Our proposed test statistics are based on weighted integrals of empirical characteristic functions. With two special weight functions, we obtain test statistics with simple and closed forms. The test statistics are easy to implement. In fact, they are based merely on pairwise distanc ...
 DOI:
 10.1016/j.csda.2018.05.007

http://dx.doi.org/10.1016/j.csda.2018.05.007
 Author:
 Li, Haocheng; Shu, Di; Zhang, Yukun; Yi, Grace Y.
 Source:
 Computational statistics & data analysis 2018 v.118 pp. 126137
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; Show all 2 Subject
 Abstract:
 ... Complex structured data settings are studied where outcomes are multivariate and multilevel and are collected longitudinally. Multivariate outcomes include both continuous and discrete responses. In addition, the data contain a large number of covariates but only some of them are important in explaining the dynamic features of the responses. To delineate the complex association structures of the r ...
 DOI:
 10.1016/j.csda.2017.09.004

http://dx.doi.org/10.1016/j.csda.2017.09.004
 Author:
 de Gunst, Mathisca; Knapik, Bartek; Mandjes, Michel; Sollie, Birgit
 Source:
 Computational statistics & data analysis 2019 v.140 pp. 88103
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Markov chain; statistical analysis; Show all 3 Subjects
 Abstract:
 ... A Markovmodulated independent sojourn process is a population process in which individuals arrive according to a Poisson process with Markovmodulated arrival rate, and leave the system after an exponentially distributed time. A procedure is developed to estimate the parameters of such a system, including those related to the modulation. It is assumed that the number of individuals in the system ...
 DOI:
 10.1016/j.csda.2019.06.008

https://dx.doi.org/10.1016/j.csda.2019.06.008
 Author:
 Tian, Yahui; Gel, Yulia R.
 Source:
 Computational statistics & data analysis 2019 v.139 pp. 99116
 ISSN:
 01679473
 Subject:
 algorithms, etc ; artificial intelligence; geometry; politics; Show all 4 Subjects
 Abstract:
 ... A new nonparametric supervised algorithm is proposed for detecting multiple communities in complex networks using the Depth vs. Depth (DD(G)) classifier. The key idea behind the new clustering method is the notion of robust and datadriven data depth methodology that still remains new and unexplored in network sciences. The developed new DD(G)method is inherently geometric and allows to simultane ...
 DOI:
 10.1016/j.csda.2019.01.007

https://dx.doi.org/10.1016/j.csda.2019.01.007
 Author:
 Grömping, Ulrike; Fontana, Roberto
 Source:
 Computational statistics & data analysis 2019 v.137 pp. 101114
 ISSN:
 01679473
 Subject:
 algorithms, etc ; computer software; statistical models; Show all 3 Subjects
 Abstract:
 ... An algorithm for the creation of mixed level arrays with generalized minimum aberration (GMA) is proposed. GMA mixed level arrays are particularly useful for experiments involving qualitative factors: for these, the number of factor levels is often a consequence of subject matter requirements, while a priori assumptions on a statistical model are not made, apart from assuming lower order effects t ...
 DOI:
 10.1016/j.csda.2019.01.020

https://dx.doi.org/10.1016/j.csda.2019.01.020
 Author:
 Xiu, Xianchao; Liu, Wanquan; Li, Ling; Kong, Lingchen
 Source:
 Computational statistics & data analysis 2019 v.136 pp. 5971
 ISSN:
 01679473
 Subject:
 algorithms, etc ; image analysis; regression analysis; Show all 3 Subjects
 Abstract:
 ... It is wellknown that the fused least absolute shrinkage and selection operator (FLASSO) has been playing an important role in signal and image processing. Recently, the nonconvex penalty is extensively investigated due to its success in sparse learning. In this paper, a novel nonconvex fused regression model, which integrates FLASSO and the nonconvex penalty nicely, is proposed. The developed alt ...
 DOI:
 10.1016/j.csda.2019.01.002

https://dx.doi.org/10.1016/j.csda.2019.01.002
 Author:
 Wang, Xia; Shojaie, Ali; Zou, Jian
 Source:
 Computational statistics & data analysis 2019 v.136 pp. 123136
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Markov chain; Show all 3 Subjects
 Abstract:
 ... An optimal and flexible multiple hypotheses testing procedure is constructed for dependent data based on Bayesian techniques, aiming at handling two challenges, namely dependence structure and nonnull distribution specification. Ignoring dependence among hypotheses tests may lead to loss of efficiency and bias in decision. Misspecification in the nonnull distribution, on the other hand, can resu ...
 DOI:
 10.1016/j.csda.2019.01.009

https://dx.doi.org/10.1016/j.csda.2019.01.009
 Author:
 Jhong, JaeHwan; Koo, JaYong
 Source:
 Computational statistics & data analysis 2019 v.133 pp. 228244
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; regression analysis; Show all 3 Subjects
 Abstract:
 ... We consider the problem of simultaneously estimating a finite number of quantile functions with Bsplines and the total variation penalty. For the implementation of simultaneous quantile function estimators, we develop a new coordinate descent algorithm taking into account a special structure of the total variation penalty determined by Bspline coefficients. The entire paths of solution paths for ...
 DOI:
 10.1016/j.csda.2018.10.005

https://dx.doi.org/10.1016/j.csda.2018.10.005
 Author:
 Morris, Katherine; Punzo, Antonio; McNicholas, Paul D.; Browne, Ryan P.
 Source:
 Computational statistics & data analysis 2019 v.132 pp. 145166
 ISSN:
 01679473
 Subject:
 algorithms, etc ; automatic detection; models; probability; Show all 4 Subjects
 Abstract:
 ... Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related noncontaminated mixture, for each (asymmetric) cluster, our model has one parameter controlling the proportion of outliers and another specifying the degree of ...
 DOI:
 10.1016/j.csda.2018.12.001

https://dx.doi.org/10.1016/j.csda.2018.12.001
 Author:
 Zhang, Lingsong; Lu, Shu; Marron, J.S.
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 100110
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... Motivated by the analysis of nonnegative data objects, a novel Nested Nonnegative Cone Analysis (NNCA) approach is proposed to overcome some drawbacks of existing methods. The application of traditional PCA/SVD method to nonnegative data often cause the approximation matrix leave the nonnegative cone, which leads to noninterpretable and sometimes nonsensical results. The nonnegative matrix factor ...
 DOI:
 10.1016/j.csda.2015.01.008

http://dx.doi.org/10.1016/j.csda.2015.01.008
 Author:
 Hubert, Mia; Rousseeuw, Peter; Vanpaemel, Dina; Verdonck, Tim
 Source:
 Computational statistics & data analysis 2015 v.81 pp. 6475
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... New deterministic robust estimators of multivariate location and scatter are presented. They combine ideas from the deterministic DetMCD estimator with steps from the subsamplingbased FastS and FastMM algorithms. The new DetS and DetMM estimators perform similarly to FastS and FastMM on lowdimensional data, whereas in high dimensions they are more robust. Their computation time is much lower tha ...
 DOI:
 10.1016/j.csda.2014.07.013

http://dx.doi.org/10.1016/j.csda.2014.07.013
 Author:
 Rathke, Fabian; Schnörr, Christoph
 Source:
 Computational statistics & data analysis 2019 v.140 pp. 4158
 ISSN:
 01679473
 Subject:
 algorithms, etc ; computer software; data collection; statistical analysis; Show all 4 Subjects
 Abstract:
 ... A novel computational approach to logconcave density estimation is proposed. Previous approaches utilize the piecewiseaffine parametrization of the density induced by the given sample set. The number of parameters as well as nonsmooth subgradientbased convex optimization for determining the maximum likelihood density estimate cause long runtimes for dimensions d≥2 and large sample sets. The pr ...
 DOI:
 10.1016/j.csda.2019.04.005

https://dx.doi.org/10.1016/j.csda.2019.04.005
 Author:
 Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco
 Source:
 Computational statistics & data analysis 2019 v.137 pp. 6791
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; case studies; models; probability; Show all 5 Subjects
 Abstract:
 ... A novel approach for parameter estimation in Bayesian networks is presented. The main idea is to introduce a hyperprior in the Multinomial–Dirichletmodel, traditionally used for conditional distribution estimation in Bayesian networks. The resulting hierarchical model jointly estimates different conditional distributions belonging to the same conditional probability table, thus borrowing statisti ...
 DOI:
 10.1016/j.csda.2019.02.004

https://dx.doi.org/10.1016/j.csda.2019.02.004
 Author:
 Li, Jinqing; Ma, Jun
 Source:
 Computational statistics & data analysis 2019 v.137 pp. 170180
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; regression analysis; system optimization; Show all 4 Subjects
 Abstract:
 ... Existing likelihood methods for the additive hazards model with interval censored survival data are limited and often ignore the nonnegative constraints on hazards. This paper proposes a maximum penalized likelihood method to fit additive hazards models with interval censoring. Our method firstly models the baseline hazard using a finite number of nonnegative basis functions, and then regression ...
 DOI:
 10.1016/j.csda.2019.02.010

https://dx.doi.org/10.1016/j.csda.2019.02.010
 Author:
 Caimo, Alberto; Gollini, Isabella
 Source:
 Computational statistics & data analysis 2019
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; uncertainty; Show all 5 Subjects
 Abstract:
 ... A new modelling approach for the analysis of weighted networks with ordinal/ polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be ge ...
 DOI:
 10.1016/j.csda.2019.106825

https://dx.doi.org/10.1016/j.csda.2019.106825
 Author:
 Geraci, Marco
 Source:
 Computational statistics & data analysis 2019 v.136 pp. 3046
 ISSN:
 01679473
 Subject:
 algorithms, etc ; growth curves; least squares; models; pharmacokinetics; Show all 5 Subjects
 Abstract:
 ... In regression applications, the presence of nonlinearity and correlation among observations offer computational challenges not only in traditional settings such as least squares regression, but also (and especially) when the objective function is nonsmooth as in the case of quantile regression. Methods are developed for the modelling and estimation of nonlinear conditional quantile functions when ...
 DOI:
 10.1016/j.csda.2018.12.005

https://dx.doi.org/10.1016/j.csda.2018.12.005
 Author:
 Zhu, Kailun; Kurowicka, Dorota; Nane, Gabriela F.
 Source:
 Computational statistics & data analysis 2019 pp. 106811
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; trees; vines; Show all 5 Subjects
 Abstract:
 ... The selection of vine structure to represent dependencies in a data set with a regular vine copula model is still an open question. Up to date, the most popular heuristic to choose the vine structure is to construct consecutive trees by capturing largest correlations in lower trees. However, this might not lead to the optimal vine structure. A new heuristic based on sampling orders implied by regu ...
 DOI:
 10.1016/j.csda.2019.106811

https://dx.doi.org/10.1016/j.csda.2019.106811
 Author:
 Li, Shuwei; Hu, Tao; Zhao, Xingqiu; Sun, Jianguo
 Source:
 Computational statistics & data analysis 2019 v.133 pp. 153165
 ISSN:
 01679473
 Subject:
 algorithms, etc ; children; models; mortality; probability; regression analysis; Show all 6 Subjects
 Abstract:
 ... This paper discusses regression analysis of intervalcensored failure time data with a cured subgroup under a general class of semiparametric transformation cure models. For inference, a novel and stable expectation maximization (EM) algorithm with the use of Poisson variables is developed to overcome the difficulty in maximizing the observed data likelihood function with complex form. The asympto ...
 DOI:
 10.1016/j.csda.2018.09.008

https://dx.doi.org/10.1016/j.csda.2018.09.008
 Author:
 Lin, L.; Fong, D.K.H.
 Source:
 Computational statistics & data analysis 2019 v.129 pp. 113
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; models; multidimensional scaling; Show all 4 Subjects
 Abstract:
 ... Multidimensional scaling methods are frequently used by researchers and practitioners to project high dimensional data into a low dimensional space. However, it is a challenge to integrate side information which is available along with the dissimilarities to perform such dimension reduction analysis. A novel Bayesian integrative multidimensional scaling procedure, namely Bayesian multidimensional ...
 DOI:
 10.1016/j.csda.2018.07.007

https://dx.doi.org/10.1016/j.csda.2018.07.007
 Author:
 Manghi, Roberto F.; Cysneiros, Francisco José A.; Paula, Gilberto A.
 Source:
 Computational statistics & data analysis 2019 v.129 pp. 4760
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; diagnostic techniques; linear models; Show all 4 Subjects
 Abstract:
 ... Statistical procedures are proposed in generalized additive partial linear models (GAPLM) for analyzing correlated data. A reweighed iterative process based on the backfitting algorithm is derived for the parameter estimation from a penalized GEE. Discussions on the inferential aspects of GAPLM, particularly on the asymptotic properties of the former estimators as well as on the effective degrees ...
 DOI:
 10.1016/j.csda.2018.08.005

https://dx.doi.org/10.1016/j.csda.2018.08.005
 Author:
 Andrews, Jeffrey L.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 160171
 ISSN:
 01679473
 Subject:
 algorithms, etc ; cluster analysis; models; Show all 3 Subjects
 Abstract:
 ... The expectation–maximization (EM) algorithm is a common approach for parameter estimation in the context of cluster analysis using finite mixture models. This approach suffers from the wellknown issue of convergence to local maxima, but also the less obvious problem of overfitting. These combined, and competing, concerns are illustrated through simulation and then addressed by introducing an algo ...
 DOI:
 10.1016/j.csda.2018.05.015

http://dx.doi.org/10.1016/j.csda.2018.05.015
 Author:
 Buonocore, A.; Nobile, A.G.; Pirozzi, E.
 Source:
 Computational statistics & data analysis 2018 v.118 pp. 4053
 ISSN:
 01679473
 Subject:
 algorithms, etc ; equations; probability distribution; Show all 3 Subjects
 Abstract:
 ... Algorithms to generate random variates from probability density function of Gauss–Markov processes restricted by special lower reflecting boundary are formulated. They are essentially obtained by means of discretizations of stochastic equations or via acceptance–rejection methods. Particular attention is dedicated to restricted Wiener and Ornstein–Uhlenbeck processes. ...
 DOI:
 10.1016/j.csda.2017.08.008

http://dx.doi.org/10.1016/j.csda.2017.08.008
 Author:
 Lin, ChangYun; Yang, Po
 Source:
 Computational statistics & data analysis 2018 v.118 pp. 98111
 ISSN:
 01679473
 Subject:
 algorithms, etc ; case studies; models; Show all 3 Subjects
 Abstract:
 ... Baseline designs have received considerable attention recently. Most existing methods for finding best baseline designs were developed for completely randomized experiments. How to select baseline designs for experiments under multistratum structures has not been studied in the literature. The purpose of this paper is to fill this gap and extend the use of the baseline design for experiments with ...
 DOI:
 10.1016/j.csda.2017.08.009

http://dx.doi.org/10.1016/j.csda.2017.08.009
 Author:
 Fu, Eric; Heckman, Nancy
 Source:
 Computational statistics & data analysis 2018
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Mirounga; models; Show all 3 Subjects
 Abstract:
 ... Functional data often exhibit both amplitude and phase variation around a common base shape, with phase variation represented by a so called warping function. The process of removing phase variation by curve alignment and inference of the warping functions is referred to as curve registration. When functional data are observed with substantial noise, modelbased methods can be employed for simulta ...
 DOI:
 10.1016/j.csda.2018.06.010

https://dx.doi.org/10.1016/j.csda.2018.06.010
 Author:
 Xue, Yuan; Zhang, Nan; Yin, Xiangrong; Zheng, Haitao
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 6778
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; Show all 2 Subject
 Abstract:
 ... By using Hilbert–Schmidt Independence Criterion, a sufficient dimension reduction method is proposed to estimate the directions in multipleindex models. A projection pursuit type of sufficient searching algorithm is introduced to reduce the computational complexity, as the original problem involves nonlinear optimization over multidimensional Grassmannmanifold. A bootstrap procedure with additi ...
 DOI:
 10.1016/j.csda.2017.05.002

http://dx.doi.org/10.1016/j.csda.2017.05.002
 Author:
 Gorynin, Ivan; Derrode, Stéphane; Monfrini, Emmanuel; Pieczynski, Wojciech
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 3846
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; Show all 2 Subject
 Abstract:
 ... Statistical smoothing in general nonlinear nonGaussian systems is a challenging problem. A new smoothing method based on approximating the original system by a recent switching model has been introduced. Such switching model allows fast and optimal smoothing. The new algorithm is validated through an application on stochastic volatility and dynamic beta models. Simulation experiments indicate it ...
 DOI:
 10.1016/j.csda.2017.04.007

http://dx.doi.org/10.1016/j.csda.2017.04.007
 Author:
 Yang, Lianqiang; Hong, Yongmiao
 Source:
 Computational statistics & data analysis 2017 v.108 pp. 7083
 ISSN:
 01679473
 Subject:
 algorithms, etc ; models; Show all 2 Subject
 Abstract:
 ... Data driven adaptive penalized splines are considered via the principle of constrained regression. A locally penalized vector based on the local ranges of the data is generated and added into the penalty matrix of the classical penalized splines, which remarkably improves the local adaptivity of the model for data heterogeneity. The algorithm complexity and simulations are studied. The results sho ...
 DOI:
 10.1016/j.csda.2016.10.022

http://dx.doi.org/10.1016/j.csda.2016.10.022
 Author:
 Wilhelm, Matthieu; Tillé, Yves; Qualité, Lionel
 Source:
 Computational statistics & data analysis 2017 v.105 pp. 1123
 ISSN:
 01679473
 Subject:
 algorithms, etc ; variance; Show all 2 Subject
 Abstract:
 ... A specific family of point processes is introduced that allow to select samples for the purpose of estimating the mean or the integral of a function of a real variable. These processes, called quasisystematic processes, depend on a tuning parameter r>0 that permits to control the likeliness of jointly selecting neighbor units in a same sample. When r is large, units that are close tend to not be ...
 DOI:
 10.1016/j.csda.2016.07.011

http://dx.doi.org/10.1016/j.csda.2016.07.011
 Author:
 Yamamoto, Michio; Terada, Yoshikazu
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 133148
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... A new procedure for simultaneously finding the optimal cluster structure of multivariate functional objects and finding the subspace to represent the cluster structure is presented. The method is based on the kmeans criterion for projected functional objects on a subspace in which a cluster structure exists. An efficient alternating leastsquares algorithm is described, and the proposed method is ...
 DOI:
 10.1016/j.csda.2014.05.010

http://dx.doi.org/10.1016/j.csda.2014.05.010
 Author:
 Hasler, Caren; Tillé, Yves
 Source:
 Computational statistics & data analysis 2014 v.74 pp. 8194
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. A procedure to select balanced samples in a stratified population has previously been proposed. Unfortunately, this procedure becomes very slow as the number of strata increases and it even fails to select samples for some large numbers of st ...
 DOI:
 10.1016/j.csda.2013.12.005

http://dx.doi.org/10.1016/j.csda.2013.12.005
 Author:
 Chen, RayBing; Hsu, YenWen; Hung, Ying; Wang, Weichung
 Source:
 Computational statistics & data analysis 2014 v.72 pp. 282297
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... Central composite discrepancy (CCD) has been proposed to measure the uniformity of a design over irregular experimental region. However, how CCDbased optimal uniform designs can be efficiently computed remains a challenge. Focusing on this issues, we proposed a particle swarm optimizationbased algorithm to efficiently find optimal uniform designs with respect to the CCD criterion. Parallel compu ...
 DOI:
 10.1016/j.csda.2013.10.015

http://dx.doi.org/10.1016/j.csda.2013.10.015