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 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:
 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:
 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:
 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:
 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:
 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:
 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:
 Prater, Ashley; Shen, Lixin; Suter, Bruce W.
 Source:
 Computational statistics & data analysis 2015 v.90 pp. 3646
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; mathematical models; regression analysis; Show all 4 Subjects
 Abstract:
 ... A simple iterative method for finding the Dantzig selector, designed for linear regression problems, is introduced. The method consists of two stages. The first stage approximates the Dantzig selector through a fixedpoint formulation of solutions to the Dantzig selector problem; the second stage constructs a new estimator by regressing data onto the support of the approximated Dantzig selector. T ...
 DOI:
 10.1016/j.csda.2015.04.005

http://dx.doi.org/10.1016/j.csda.2015.04.005
 Author:
 Bürgin, Reto; Ritschard, Gilbert
 Source:
 Computational statistics & data analysis 2015 v.86 pp. 6580
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; regression analysis; unemployment; Show all 4 Subjects
 Abstract:
 ... A treebased algorithm for longitudinal regression analysis that aims to learn whether and how the effects of predictor variables depend on moderating variables is presented. The algorithm is based on multivariate generalized linear mixed models and it builds piecewise constant coefficient functions. Moreover, it is scalable for many moderators of possibly mixed scales, integrates interactions bet ...
 DOI:
 10.1016/j.csda.2015.01.003

http://dx.doi.org/10.1016/j.csda.2015.01.003
 Author:
 Ding, Jieli; Tian, GuoLiang; Yuen, Kam Chuen
 Source:
 Computational statistics & data analysis 2015 v.84 pp. 135151
 ISSN:
 01679473
 Subject:
 algorithms, etc ; data collection; models; regression analysis; Show all 4 Subjects
 Abstract:
 ... The constrained estimation in Cox’s model for the rightcensored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear ...
 DOI:
 10.1016/j.csda.2014.11.005

http://dx.doi.org/10.1016/j.csda.2014.11.005
 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:
 Yu, Wenbao; Park, Taesung
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 1527
 ISSN:
 01679473
 Subject:
 algorithms, etc ; biomarkers; data collection; diagnostic techniques; graphs; regression analysis; Show all 6 Subjects
 Abstract:
 ... In clinical practices, it is common that several biomakers are related to a specific disease and each single marker does not have enough diagnostic power. An effective way to improve the diagnostic accuracy is to combine multiple markers. It is known that the area under the receiver operating characteristic curve (AUC) is very popular for evaluation of a diagnostic tool. Su and Liu (1993) derived ...
 DOI:
 10.1016/j.csda.2014.12.002

http://dx.doi.org/10.1016/j.csda.2014.12.002
 Author:
 Wang, Naichen; Wang, Lianming; McMahan, Christopher S.
 Source:
 Computational statistics & data analysis 2015 v.83 pp. 140150
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Chlamydia; data collection; models; public health; regression analysis; Nebraska; Show all 7 Subjects
 Abstract:
 ... The Gammafrailty proportional hazards (PH) model is commonly used to analyze correlated survival data. Despite this model’s popularity, the analysis of correlated current status data under the Gammafrailty PH model can prove to be challenging using traditional techniques. Consequently, in this paper we develop a novel expectation–maximization (EM) algorithm under the Gammafrailty PH model to st ...
 DOI:
 10.1016/j.csda.2014.10.013

http://dx.doi.org/10.1016/j.csda.2014.10.013
 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:
 Papastamoulis, Panagiotis; MartinMagniette, MarieLaure; MaugisRabusseau, Cathy
 Source:
 Computational statistics & data analysis 2016 v.93 pp. 97106
 ISSN:
 01679473
 Subject:
 algorithms, etc ; Poisson distribution; computer software; data collection; highthroughput nucleotide sequencing; models; regression analysis; Show all 7 Subjects
 Abstract:
 ... Modelling heterogeneity in large datasets of counts under the presence of covariates demands advanced clustering methods. Towards this direction a mixture of Poisson regressions is proposed. Conditionally on the covariates and a cluster, the multivariate distribution is a product of independent Poisson distributions. A variety of different parameterizations is taken into account for the slope of t ...
 DOI:
 10.1016/j.csda.2014.07.005

http://dx.doi.org/10.1016/j.csda.2014.07.005
 Author:
 Ciarleglio, Adam; Todd Ogden, R.
 Source:
 Computational statistics & data analysis 2016 v.93 pp. 8696
 ISSN:
 01679473
 Subject:
 algorithms, etc ; cognition; data collection; image analysis; models; regression analysis; sclerosis; wavelet; Show all 8 Subjects
 Abstract:
 ... Classical finite mixture regression is useful for modeling the relationship between scalar predictors and scalar responses arising from subpopulations defined by the differing associations between those predictors and responses. The classical finite mixture regression model is extended to incorporate functional predictors by taking a waveletbased approach in which both the functional predictors a ...
 DOI:
 10.1016/j.csda.2014.11.017

http://dx.doi.org/10.1016/j.csda.2014.11.017
 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:
 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:
 Tian, GuoLiang; Ma, Huijuan; Zhou, Yong; Deng, Dianliang
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 97114
 ISSN:
 01679473
 Subject:
 algorithms, etc ; binomial distribution; confidence interval; data collection; models; regression analysis; statistical inference; Show all 7 Subjects
 Abstract:
 ... To model binomial data with large frequencies of both zeros and rightendpoints, Deng and Zhang (in press) recently extended the zeroinflated binomial distribution to an endpointinflated binomial (EIB) distribution. Although they proposed the EIB mixed regression model, the major goal of Deng and Zhang (2015) is just to develop score tests for testing whether endpointinflation exists. However, ...
 DOI:
 10.1016/j.csda.2015.03.009

http://dx.doi.org/10.1016/j.csda.2015.03.009