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 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:
 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:
 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:
 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