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 Author:
 Tian, GuoLiang; Liu, Yin; Tang, ManLai; Li, Tao
 Source:
 Computational statistics & data analysis 2019 v.140 pp. 122143
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; epidemiology; medicine; models; probability; psychology; sports; statistical analysis; Show all 10 Subjects
 Abstract:
 ... Incomplete categorical data often occur in the fields such as biomedicine, epidemiology, psychology, sports and so on. In this paper, we first introduce a novel minorization–maximization (MM) algorithm to calculate the maximum likelihood estimates (MLEs) of parameters and the posterior modes for the analysis of general incomplete categorical data. Although the data augmentation (DA) algorithm and ...
 DOI:
 10.1016/j.csda.2019.04.012

https://dx.doi.org/10.1016/j.csda.2019.04.012
 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:
 Shen, Paosheng; Chen, HsinJen; Pan, WenHarn; Chen, ChyongMei
 Source:
 Computational statistics & data analysis 2019 v.140 pp. 7487
 ISSN:
 01679473
 Subject:
 algorithms, etc ; cardiovascular diseases; cohort studies; data collection; models; regression analysis; risk factors; statistical inference; variance; Show all 9 Subjects
 Abstract:
 ... Interval censoring and truncation arise often in cohort studies, longitudinal and sociological research. In this article, we formulate the effects of covariates on lefttruncated and mixed case intervalcensored (LTIC) data without or with a cure fraction through a general class of semiparametric transformation models. We propose the conditional likelihood approach for statistical inference. For d ...
 DOI:
 10.1016/j.csda.2019.06.006

https://dx.doi.org/10.1016/j.csda.2019.06.006
 Author:
 FuentesGarcía, Ruth; Mena, Ramsés H.; Walker, Stephen G.
 Source:
 Computational statistics & data analysis 2019 v.137 pp. 92100
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Markov chain; Monte Carlo method; data collection; models; Show all 6 Subjects
 Abstract:
 ... Motivated by the Hopfield’s network, a conditional maximization routine is used in order to compute the posterior mode of a random allocation model. The proposed approach applies to a general framework covering parametric and nonparametric Bayesian mixture models, product partition models, and change point models, among others. The resulting algorithm is simple to code and very fast, thus providin ...
 DOI:
 10.1016/j.csda.2019.02.008

https://dx.doi.org/10.1016/j.csda.2019.02.008
 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:
 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:
 Yu, Weichang; Azizi, Lamiae; Ormerod, John T.
 Source:
 Computational statistics & data analysis 2019 pp. 106817
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; discriminant analysis; models; uncertainty; Show all 6 Subjects
 Abstract:
 ... Variable selection and classification are common objectives in the analysis of highdimensional data. Most such methods make distributional assumptions that may not be compatible with the diverse families of distributions data can take. A novel Bayesian nonparametric discriminant analysis model that performs both variable selection and classification within a seamless framework is proposed. Pólya ...
 DOI:
 10.1016/j.csda.2019.106817

https://dx.doi.org/10.1016/j.csda.2019.106817
 Author:
 Kwon, Yongchan; Won, JoongHo; Kim, Beom Joon; Paik, Myunghee Cho
 Source:
 Computational statistics & data analysis 2019 pp. 106816
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; computer vision; data collection; neural networks; prediction; stroke; uncertainty; Show all 8 Subjects
 Abstract:
 ... Most recent research of deep neural networks in the field of computer vision has focused on improving performances of point predictions by developing network architectures or learning algorithms. Reliable uncertainty quantification accompanied by point estimation can lead to a more informed decision, and the quality of prediction can be improved. In this paper, we invoke a Bayesian neural network ...
 DOI:
 10.1016/j.csda.2019.106816

https://dx.doi.org/10.1016/j.csda.2019.106816
 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:
 Derumigny, Alexis; Fermanian, JeanDavid
 Source:
 Computational statistics & data analysis 2019 v.135 pp. 7094
 ISSN:
 01679473
 Subject:
 algorithms, etc ; artificial intelligence; data collection; decision support systems; neural networks; statistical analysis; Show all 6 Subjects
 Abstract:
 ... It is shown how the problem of estimating conditional Kendall’s tau can be rewritten as a classification task. Conditional Kendall’s tau is a conditional dependence parameter that is a characteristic of a given pair of random variables. The goal is to predict whether the pair is concordant (value of 1) or discordant (value of −1) conditionally on some covariates. The consistency and the asymptotic ...
 DOI:
 10.1016/j.csda.2019.01.013

https://dx.doi.org/10.1016/j.csda.2019.01.013
 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:
 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:
 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:
 Banerjee, Sayantan; Akbani, Rehan; Baladandayuthapani, Veerabhadran
 Source:
 Computational statistics & data analysis 2019 v.132 pp. 4669
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; geometry; graphs; models; neoplasms; proteomics; Show all 8 Subjects
 Abstract:
 ... Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on th ...
 DOI:
 10.1016/j.csda.2018.08.009

https://dx.doi.org/10.1016/j.csda.2018.08.009
 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:
 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:
 Hsu, HsiangLing; Chang, Yuanchin Ivan; Chen, RayBing
 Source:
 Computational statistics & data analysis 2019 v.129 pp. 119134
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; experimental design; models; regression analysis; telescopes; Show all 6 Subjects
 Abstract:
 ... We study a logistic modelbased active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled traini ...
 DOI:
 10.1016/j.csda.2018.08.013

http://dx.doi.org/10.1016/j.csda.2018.08.013
 Author:
 Yu, Dalei; Ding, Chang; He, Na; Wang, Ruiwu; Zhou, Xiaohua; Shi, Lei
 Source:
 Computational statistics & data analysis 2019 v.129 pp. 93118
 ISSN:
 01679473
 Subject:
 algorithms, etc ; case studies; confidence interval; data collection; metaanalysis; models; quantitative analysis; statistical analysis; variance; Show all 9 Subjects
 Abstract:
 ... Metaanalysis provides a quantitative method for combining results from independent studies with the same treatment. However, existing estimation methods are sensitive to the presence of outliers in the datasets. In this paper we study the robust estimation for the parameters in metaregression, including the betweenstudy variance and regression parameters. Huber’s rho function and Tukey’s biweig ...
 DOI:
 10.1016/j.csda.2018.08.010

http://dx.doi.org/10.1016/j.csda.2018.08.010
 Author:
 Li, Peili; Xiao, Yunhai
 Source:
 Computational statistics & data analysis 2018 v.128 pp. 292307
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; multivariate analysis; system optimization; variance covariance matrix; Show all 6 Subjects
 Abstract:
 ... Estimating large and sparse inverse covariance matrix plays a fundamental role in modern multivariate analysis, because the zero entries capture the conditional independence between pairs of variables given all other variables. This estimation task can be realized by penalizing the maximum likelihood estimation with an adaptive group lasso penalty imposed directly on the elements of the inverse, w ...
 DOI:
 10.1016/j.csda.2018.07.011

https://dx.doi.org/10.1016/j.csda.2018.07.011
 Author:
 Wang, Ketong; Porter, Michael D.
 Source:
 Computational statistics & data analysis 2018 v.128 pp. 395411
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; Show all 4 Subjects
 Abstract:
 ... Bayesian modelbased clustering is a widely applied procedure for discovering groups of related observations in a dataset. These approaches use Bayesian mixture models, estimated with MCMC, which provide posterior samples of the model parameters and clustering partition. While inference on model parameters is well established, inference on the clustering partition is less developed. A new method i ...
 DOI:
 10.1016/j.csda.2018.08.002

https://dx.doi.org/10.1016/j.csda.2018.08.002
 Author:
 Chen, ChyongMei; Shen, Paosheng; Tseng, YiKuan
 Source:
 Computational statistics & data analysis 2018 v.128 pp. 116127
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Human immunodeficiency virus; biomarkers; clinical trials; cohort studies; data collection; models; statistical analysis; Show all 8 Subjects
 Abstract:
 ... In many clinical trials and epidemiology research, subjects are followedup repeatedly, and repeated measurements on longitudinal covariates as well as an observation on a possibly censored timetoevent are collected on each subject. The longitudinal covariates are often measured intermittently with measurement errors, and the measurement process is terminated by a correlated event process, leadi ...
 DOI:
 10.1016/j.csda.2018.07.001

https://dx.doi.org/10.1016/j.csda.2018.07.001
 Author:
 Zhao, Weihua; Jiang, Xuejun; Lian, Heng
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 269280
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; regression analysis; Show all 4 Subjects
 Abstract:
 ... A principal varyingcoefficient model for quantile regression based on regression splines estimation is proposed. Convergence rate and local asymptotics for the coefficient functions are then derived. Furthermore, penalization is used to obtain joint variable selection and dimension reduction in quantile varyingcoefficient models. A group coordinate descent algorithm is adopted for a computationa ...
 DOI:
 10.1016/j.csda.2018.05.021

http://dx.doi.org/10.1016/j.csda.2018.05.021
 Author:
 Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi
 Source:
 Computational statistics & data analysis 2018
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; decision support systems; hydrofluorocarbons; Show all 4 Subjects
 Abstract:
 ... Classification of the highdimensional data is challenging due to the curse of dimensionality, heavy computational burden and decreasing precision of algorithms. In order to mitigate these effects, feature selection approaches that can determine an efficient subset of features are utilized in the processing. However, most of these techniques attain just one subset of nonredundant features includi ...
 DOI:
 10.1016/j.csda.2018.08.015

http://dx.doi.org/10.1016/j.csda.2018.08.015
 Author:
 Wong, Raymond K.W.; Zhang, Xiaoke
 Source:
 Computational statistics & data analysis 2018
 ISSN:
 01679473
 Subject:
 algorithms, etc ; covariance; data collection; ingredients; traffic; Show all 5 Subjects
 Abstract:
 ... In functional data analysis (FDA), the covariance function is fundamental not only as a critical quantity for understanding elementary aspects of functional data but also as an indispensable ingredient for many advanced FDA methods. A new class of nonparametric covariance function estimators in terms of various spectral regularizations of an operator associated with a reproducing kernel Hilbert sp ...
 DOI:
 10.1016/j.csda.2018.05.013

http://dx.doi.org/10.1016/j.csda.2018.05.013
 Author:
 Wang, Shangshan; Xiang, Liming
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 136154
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; gene expression; models; regression analysis; system optimization; Show all 6 Subjects
 Abstract:
 ... We advocate linear regression by modeling the error term through a finite mixture of asymmetric Laplace distributions (ALDs). The model expands the flexibility of linear regression to account for heterogeneity among data and allows us to establish the equivalence between maximum likelihood estimation of the model parameters and the composite quantile regression (CQR) estimation developed by Zou an ...
 DOI:
 10.1016/j.csda.2017.06.002

http://dx.doi.org/10.1016/j.csda.2017.06.002
 Author:
 Marbac, Matthieu; Sedki, Mohammed
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 130145
 ISSN:
 01679473
 Subject:
 algorithms, etc ; computer software; data collection; models; probability; Show all 5 Subjects
 Abstract:
 ... A new family of onefactor distributions for modeling highdimensional binary data is introduced. The model provides an explicit probability for each event, thus avoiding the numeric approximations often made by existing methods. Model interpretation is easy, because each variable is described by two continuous parameters (corresponding to the marginal probability and to the strength of dependency ...
 DOI:
 10.1016/j.csda.2017.04.010

http://dx.doi.org/10.1016/j.csda.2017.04.010
 Author:
 Bermúdez, Lluís; Karlis, Dimitris; Santolino, Miguel
 Source:
 Computational statistics & data analysis 2017 v.112 pp. 1423
 ISSN:
 01679473
 Subject:
 algorithms, etc ; accidents; data collection; models; Show all 4 Subjects
 Abstract:
 ... A new modelling approach, based on finite mixtures of multiple discrete distributions of different multiplicities, is proposed to fit data with a lot of periodic spikes in certain values. An EM algorithm is provided in order to ensure the models’ easeoffit and then a simulation study is presented to show its efficiency. A numerical application with a real data set involving the length, measured ...
 DOI:
 10.1016/j.csda.2017.02.013

http://dx.doi.org/10.1016/j.csda.2017.02.013
 Author:
 Lai, Xin; Yau, Kelvin K.W.; Liu, Liu
 Source:
 Computational statistics & data analysis 2017 v.112 pp. 215223
 ISSN:
 01679473
 Subject:
 algorithms, etc ; bone marrow transplant; clinical trials; data collection; hospitals; leukemia; models; normal distribution; patients; risk; Show all 10 Subjects
 Abstract:
 ... Competing risks are often observed in clinical trial studies. As exemplified in two data sets, the bone marrow transplantation study for leukaemia patients and the primary biliary cirrhosis study, patients could experience two competing events which may be correlated due to shared unobservable factors within the same cluster. With the presence of random hospital/cluster effects, a causespecific h ...
 DOI:
 10.1016/j.csda.2017.03.011

http://dx.doi.org/10.1016/j.csda.2017.03.011
 Author:
 Kojadinovic, Ivan
 Source:
 Computational statistics & data analysis 2017 v.112 pp. 2441
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Monte Carlo method; data collection; insurance; models; Show all 5 Subjects
 Abstract:
 ... When modeling the distribution of a multivariate continuous random vector using the socalled copula approach, it is not uncommon to have ties in the coordinate samples of the available data because of rounding or lack of measurement precision. Yet, the vast majority of existing inference procedures on the underlying copula were both theoretically derived and practically implemented under the assu ...
 DOI:
 10.1016/j.csda.2017.02.006

http://dx.doi.org/10.1016/j.csda.2017.02.006
 Author:
 Doove, Lisa L.; Wilderjans, Tom F.; Calcagnì, Antonio; Van Mechelen, Iven
 Source:
 Computational statistics & data analysis 2017 v.107 pp. 8191
 ISSN:
 01679473
 Subject:
 algorithms, etc ; biostatistics; data collection; models; statistical analysis; Show all 5 Subjects
 Abstract:
 ... In benchmarking studies with simulated data sets in which two or more statistical methods are compared, over and above the search of a universally winning method, one may investigate how the winning method may vary over patterns of characteristics of the data or the datagenerating mechanism. Interestingly, this problem bears strong formal similarities to the problem of looking for optimal treatme ...
 DOI:
 10.1016/j.csda.2016.10.016

http://dx.doi.org/10.1016/j.csda.2016.10.016
 Author:
 Krivitsky, Pavel N.
 Source:
 Computational statistics & data analysis 2017 v.107 pp. 149161
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; Show all 3 Subjects
 Abstract:
 ... Exponentialfamily models for dependent data have applications in a wide variety of areas, but the dependence often results in an intractable likelihood, requiring either analytic approximation or MCMCbased techniques to fit, the latter requiring an initial parameter configuration to seed their simulations. A poor initial configuration can lead to slow convergence or outright failure. The approxi ...
 DOI:
 10.1016/j.csda.2016.10.015

http://dx.doi.org/10.1016/j.csda.2016.10.015
 Author:
 Wang, Dan; Tian, Lili
 Source:
 Computational statistics & data analysis 2017 v.106 pp. 1226
 ISSN:
 01679473
 Subject:
 algorithms, etc ; adults; biomarkers; confidence interval; data collection; gene expression; gene expression regulation; microarray technology; probability distribution; Show all 9 Subjects
 Abstract:
 ... Overlap coefficient (OVL), the proportion of overlap area between two probability distributions, is a direct measure of similarity between two distributions. It is useful in microarray analysis for the purpose of identifying differentially expressed biomarkers, especially when data follow multimodal distribution which cannot be transformed to normal. However, the inference methods about OVL are qu ...
 DOI:
 10.1016/j.csda.2016.08.013

http://dx.doi.org/10.1016/j.csda.2016.08.013
 Author:
 Kim, Seongho; Jang, Hyejeong; Koo, Imhoi; Lee, Joohyoung; Zhang, Xiang
 Source:
 Computational statistics & data analysis 2017 v.105 pp. 96111
 ISSN:
 01679473
 Subject:
 algorithms, etc ; biomarkers; comprehensive twodimensional gas chromatography; data collection; gas chromatographymass spectrometry; metabolomics; models; Show all 7 Subjects
 Abstract:
 ... Compared to other analytical platforms, comprehensive twodimensional gas chromatography coupled with mass spectrometry (GC×GC–MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC–MS. Therefore, the normal–exponential–Bernoul ...
 DOI:
 10.1016/j.csda.2016.07.015

http://dx.doi.org/10.1016/j.csda.2016.07.015
 Author:
 Ippel, L.; Kaptein, M.C.; Vermunt, J.K.
 Source:
 Computational statistics & data analysis 2016 v.104 pp. 169182
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; mobile telephones; models; schools; Show all 5 Subjects
 Abstract:
 ... Multilevel models are often used for the analysis of grouped data. Grouped data occur for instance when estimating the performance of pupils nested within schools or analyzing multiple observations nested within individuals. Currently, multilevel models are mostly fit to static datasets. However, recent technological advances in the measurement of social phenomena have led to data arriving in a co ...
 DOI:
 10.1016/j.csda.2016.06.008

http://dx.doi.org/10.1016/j.csda.2016.06.008
 Author:
 Gruenhage, Gina; Opper, Manfred; Barthelme, Simon
 Source:
 Computational statistics & data analysis 2016 v.104 pp. 5165
 ISSN:
 01679473
 Subject:
 algorithms, etc ; artificial intelligence; data collection; topology; Show all 4 Subjects
 Abstract:
 ... Most Machine Learning (ML) methods, from clustering to classification, rely on a distance function to describe relationships between datapoints. For complex datasets it is hard to avoid making some arbitrary choices when defining a distance function. To compare images, one must choose a spatial scale, for signals, a temporal scale. The right scale is hard to pin down and it is preferable when resu ...
 DOI:
 10.1016/j.csda.2016.06.006

http://dx.doi.org/10.1016/j.csda.2016.06.006
 Author:
 Chauveau, Didier; Hoang, Vy Thuy Lynh
 Source:
 Computational statistics & data analysis 2016 v.103 pp. 116
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; mathematical models; Show all 3 Subjects
 Abstract:
 ... Models and algorithms for nonparametric estimation of finite multivariate mixtures have been recently proposed, where it is usually assumed that coordinates are independent conditional on the subpopulation from which each observation is drawn. Hence in these models the dependence structure comes only from the mixture. This assumption is relaxed, allowing for independent multivariate blocks of coor ...
 DOI:
 10.1016/j.csda.2016.04.013

http://dx.doi.org/10.1016/j.csda.2016.04.013
 Author:
 Feng, Sanying; Lian, Heng; Xue, Liugen
 Source:
 Computational statistics & data analysis 2016 v.102 pp. 98109
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; linear models; variance covariance matrix; Show all 4 Subjects
 Abstract:
 ... In this paper, we propose a nested modified Cholesky decomposition for modeling the covariance structure in multivariate longitudinal data analysis. The entries of this decomposition have simple structures and can be interpreted as the generalized moving average coefficient matrices and innovation covariance matrices. We model the elements of these matrices by a class of unconstrained linear model ...
 DOI:
 10.1016/j.csda.2016.04.006

http://dx.doi.org/10.1016/j.csda.2016.04.006
 Author:
 Gross, Samuel M.; Tibshirani, Robert
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 226235
 ISSN:
 01679473
 Subject:
 algorithms, etc ; artificial intelligence; credit; data collection; models; regression analysis; Show all 6 Subjects
 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:
 Kleiber, William
 Source:
 Computational statistics & data analysis 2016 v.101 pp. 277288
 ISSN:
 01679473
 Subject:
 algorithms, etc ; climate models; data collection; deformation; normal distribution; temperature; variance; variance covariance matrix; Colorado; Show all 9 Subjects
 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:
 Kapetanios, George; Marcellino, Massimiliano; Papailias, Fotis
 Source:
 Computational statistics & data analysis 2016 v.100 pp. 369382
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; least squares; macroeconomics; models; prediction; Show all 7 Subjects
 Abstract:
 ... Forecasting macroeconomic variables using many predictors is considered. Model selection and model reduction approaches are compared. Model selection includes heuristic optimisation of information criteria using: simulated annealing, genetic algorithms, MC³ and sequential testing. Model reduction employs the methods of principal components, partial least squares and Bayesian shrinkage regression. ...
 DOI:
 10.1016/j.csda.2015.02.017

http://dx.doi.org/10.1016/j.csda.2015.02.017
 Author:
 Zhou, Jie; Zhang, Jiajia; McLain, Alexander C.; Cai, Bo
 Source:
 Computational statistics & data analysis 2016 v.99 pp. 105114
 ISSN:
 01679473
 Subject:
 algorithms, etc ; breast neoplasms; data collection; epidemiology; models; monitoring; patients; probability; variance; Georgia; Show all 10 Subjects
 Abstract:
 ... The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval censored, the estimation of this model is challenging due to its complex data structure. A multiple imputation algorithm is proposed to obtain parameter and variance estimates for both the cure probability and the survival distribution of the ...
 DOI:
 10.1016/j.csda.2016.01.013

http://dx.doi.org/10.1016/j.csda.2016.01.013
 Author:
 Mount, David M.; Netanyahu, Nathan S.; Piatko, Christine D.; Wu, Angela Y.; Silverman, Ruth
 Source:
 Computational statistics & data analysis 2016 v.99 pp. 148170
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; linear models; probability analysis; Show all 4 Subjects
 Abstract:
 ... The linear least trimmed squares (LTS) estimator is a statistical technique for fitting a linear model to a set of points. It was proposed by Rousseeuw as a robust alternative to the classical least squares estimator. Given a set of n points in Rd, the objective is to minimize the sum of the smallest 50% squared residuals (or more generally any given fraction). There exist practical heuristics for ...
 DOI:
 10.1016/j.csda.2016.01.016

http://dx.doi.org/10.1016/j.csda.2016.01.016
 Author:
 Bongiorno, Enea G.; Goia, Aldo
 Source:
 Computational statistics & data analysis 2016 v.99 pp. 204222
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; probability; probability distribution; Show all 4 Subjects
 Abstract:
 ... An unsupervised and a supervised classification approach for Hilbert random curves are studied. Both rest on the use of a surrogate of the probability density which is defined, in a distributionfree mixture context, from an asymptotic factorization of the smallball probability. That surrogate density is estimated by a kernel approach from the principal components of the data. The focus is on the ...
 DOI:
 10.1016/j.csda.2016.01.019

http://dx.doi.org/10.1016/j.csda.2016.01.019
 Author:
 Leung, Andy; Zhang, Hongyang; Zamar, Ruben
 Source:
 Computational statistics & data analysis 2016 v.99 pp. 111
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; regression analysis; Show all 4 Subjects
 Abstract:
 ... Cellwise outliers are likely to occur together with casewise outliers in modern datasets of relatively large dimension. Recent work has shown that traditional robust regression methods may fail when applied to such datasets. We propose a new robust regression procedure to deal with casewise and cellwise outliers. The proposed method, called threestep regression, proceeds as follows: first, it use ...
 DOI:
 10.1016/j.csda.2016.01.004

http://dx.doi.org/10.1016/j.csda.2016.01.004
 Author:
 GarcíaEscudero, Luis Angel; Gordaliza, Alfonso; Greselin, Francesca; Ingrassia, Salvatore; MayoIscar, Agustín
 Source:
 Computational statistics & data analysis 2016 v.99 pp. 131147
 ISSN:
 01679473
 Subject:
 algorithms, etc ; covariance; data collection; models; Show all 4 Subjects
 Abstract:
 ... Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering–at the same time–dimension reduction and modelbased clustering. The high prevalence of spurious solutions and the disturbing effects of outlying observations in maximum likelihood estimation may cause biased or misleading inferences. Restrictions for the component covariances are consider ...
 DOI:
 10.1016/j.csda.2016.01.005

http://dx.doi.org/10.1016/j.csda.2016.01.005
 Author:
 Ikemoto, Hiroki; Adachi, Kohei
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 118
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; least squares; models; principal component analysis; Show all 5 Subjects
 Abstract:
 ... Threeway principal component analysis (3WPCA) models have been developed for analyzing a threeway data array of objects × variables × sources. Among the 3WPCA models, the least restrictive is the Tucker2 model, in which an extended core array describes the sourcespecific relationships between the components underlying objects and those for variables. In contrast to Tucker2 with the core array u ...
 DOI:
 10.1016/j.csda.2015.12.007

http://dx.doi.org/10.1016/j.csda.2015.12.007
 Author:
 Ruggieri, Eric; Antonellis, Marcus
 Source:
 Computational statistics & data analysis 2016 v.97 pp. 7186
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; data collection; models; regression analysis; surface temperature; time series analysis; uncertainty; Show all 8 Subjects
 Abstract:
 ... Change point models seek to fit a piecewise regression model with unknown breakpoints to a data set whose parameters are suspected to change through time. However, the exponential number of possible solutions to a multiple change point problem requires an efficient algorithm if long time series are to be analyzed. A sequential Bayesian change point algorithm is introduced that provides uncertainty ...
 DOI:
 10.1016/j.csda.2015.11.010

http://dx.doi.org/10.1016/j.csda.2015.11.010
 Author:
 Michoel, Tom
 Source:
 Computational statistics & data analysis 2016 v.97 pp. 6070
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; gene expression; linear models; neoplasms; prototypes; regression analysis; Show all 7 Subjects
 Abstract:
 ... The problem of finding the maximum likelihood estimates for the regression coefficients in generalised linear models with an ℓ1 sparsity penalty is shown to be equivalent to minimising the unpenalised maximum loglikelihood function over a box with boundary defined by the ℓ1penalty parameter. In oneparameter models or when a single coefficient is estimated at a time, this result implies a generi ...
 DOI:
 10.1016/j.csda.2015.11.009

http://dx.doi.org/10.1016/j.csda.2015.11.009
 Author:
 Wang, Yixin; So, Mike K.P.
 Source:
 Computational statistics & data analysis 2016 v.95 pp. 3956
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Bayesian theory; Markov chain; binomial distribution; data collection; models; statistical inference; uncertainty; China; Show all 9 Subjects
 Abstract:
 ... Bayesian spatial modeling of extreme values has become increasingly popular due to its ability to obtain relevant uncertainty measures for the estimates. This has implications for the problem of limited data on the study of extreme climatological events. Noticing the abundance of nondaily environmental records, 1h and 6h records in particular, we propose a Bayesian hierarchical model that can a ...
 DOI:
 10.1016/j.csda.2015.09.001

http://dx.doi.org/10.1016/j.csda.2015.09.001
 Author:
 Secchi, Piercesare; Vantini, Simone; Zanini, Paolo
 Source:
 Computational statistics & data analysis 2016 v.95 pp. 133149
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; electroencephalography; independent component analysis; Show all 4 Subjects
 Abstract:
 ... A new method named Hierarchical Independent Component Analysis is presented, particularly suited for dealing with two problems regarding the analysis of highdimensional and complex data: dimensional reduction and multiresolution analysis. It takes into account the Blind Source Separation framework, where the purpose is the research of a basis for a dimensional reduced space to represent data, wh ...
 DOI:
 10.1016/j.csda.2015.09.014

http://dx.doi.org/10.1016/j.csda.2015.09.014