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 Author:
 Andrews, Jeffrey L.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 160171
 ISSN:
 01679473
 Subject:
 algorithms; cluster analysis; models
 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:
 Zhao, Weihua; Jiang, Xuejun; Lian, Heng
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 269280
 ISSN:
 01679473
 Subject:
 algorithms; data collection; models; regression analysis
 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:
 Li, Huiqiong; Tian, Guoliang; Tang, Niansheng; Cao, Hongyuan
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 6981
 ISSN:
 01679473
 Subject:
 Bayesian theory; data collection; risk
 Abstract:
 ... Testing equivalence of incomplete paired data arises frequently in biomedical studies. Most existing work impose the missing at random assumption, which is not realistic in practice. Two Bayesian approaches for testing the noninferiority of incomplete paired data under nonignorable missing mechanism are presented. In addition, Bayesian credible intervals and highest posterior density intervals f ...
 DOI:
 10.1016/j.csda.2018.05.009

http://dx.doi.org/10.1016/j.csda.2018.05.009
 Author:
 Wong, Tony S.T.; Lam, Kwok Fai; Zhao, Victoria X.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 248257
 ISSN:
 01679473
 Subject:
 breast neoplasms; linear models; statistical inference
 Abstract:
 ... Likelihoodbased methods play a central role in statistical inference for parametric models. Among these, the modified likelihood ratio test is preferred in testing for homogeneity in finite mixture models. The test statistic is related to the maximum of a quadratic function under general regularity conditions. Reparameterization is shown to have overcome the difficulty when linear independence i ...
 DOI:
 10.1016/j.csda.2018.05.010

http://dx.doi.org/10.1016/j.csda.2018.05.010
 Author:
 Das, Priyam; Ghosal, Subhashis
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 172186
 ISSN:
 01679473
 Subject:
 Bayesian theory; household income; hurricanes; prediction; regression analysis; United States
 Abstract:
 ... Bayesian methods for nonparametric quantile regression have been considered with multiple continuous predictors ranging values in the unit interval. Two methods are proposed based on assuming that either the quantile function or the distribution function is smooth in the explanatory variables and is expanded in tensor product of Bspline basis functions. Unlike other existing methods of nonparam ...
 DOI:
 10.1016/j.csda.2018.04.007

http://dx.doi.org/10.1016/j.csda.2018.04.007
 Author:
 Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 204216
 ISSN:
 01679473
 Subject:
 liver transplant; time series analysis
 Abstract:
 ... Wavelettransformed variables can have better classification performance for panel data than using variables on their original scale. Examples are provided showing the types of data where using a waveletbased representation is likely to improve classification accuracy. Results show that in most cases wavelettransformed data have better or similar classification accuracy to the original data, and ...
 DOI:
 10.1016/j.csda.2018.05.019

http://dx.doi.org/10.1016/j.csda.2018.05.019
 Author:
 Wojtyś, Małgorzata; Marra, Giampiero; Radice, Rosalba
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 114
 ISSN:
 01679473
 Subject:
 case studies; computer software; empirical research; equations; models
 Abstract:
 ... Nonrandom sample selection is a commonplace amongst many empirical studies and it appears when an output variable of interest is available only for a restricted nonrandom subsample of data. An extension of the generalized additive models for location, scale and shape which accounts for nonrandom sample selection by introducing a selection equation is discussed. The proposed approach allows for ...
 DOI:
 10.1016/j.csda.2018.05.001

http://dx.doi.org/10.1016/j.csda.2018.05.001
 Author:
 Chen, Shuo; Kang, Jian; Xing, Yishi; Zhao, Yunpeng; Milton, Donald K.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 8295
 ISSN:
 01679473
 Subject:
 topology; variance covariance matrix
 Abstract:
 ... Interactions between features of highdimensional biomedical data often exhibit complex and organized, yet latent, network topological structures. Estimating the nonsparse large covariance matrix of these highdimensional biomedical data while preserving and recognizing the latent network topology are challenging. A two step procedure is proposed that first detects latent network topological stru ...
 DOI:
 10.1016/j.csda.2018.05.008

http://dx.doi.org/10.1016/j.csda.2018.05.008
 Author:
 Lee, Kyoungjae; Lee, Jaeyong; Dass, Sarat C.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 116134
 ISSN:
 01679473
 Subject:
 Bayesian theory; climatology; differential equation; genetics; models; regression analysis; statistical inference
 Abstract:
 ... Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomena which are dynamical in nature, and which are abundant in areas such as biology, climatology and genetics. The estimation of parameters of ODE based models is essential for understanding its dynamics, but the lack of an analytical solution of the ODE makes e ...
 DOI:
 10.1016/j.csda.2018.05.014

http://dx.doi.org/10.1016/j.csda.2018.05.014
 Author:
 Chen, Sixia; Haziza, David
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 258268
 ISSN:
 01679473
 Subject:
 chisquare distribution; confidence interval; surveys
 Abstract:
 ... A novel jackknife empirical likelihood method for constructing confidence intervals for multiply robust estimators is proposed in the context of missing data. Under mild regularity conditions, the proposed jackknife empirical likelihood ratio has been shown to converge to a standard chisquare distribution. A simulation study supports the findings and shows the benefits of the proposed method. The ...
 DOI:
 10.1016/j.csda.2018.05.011

http://dx.doi.org/10.1016/j.csda.2018.05.011
 Author:
 Álvarez de Toledo, Pablo; Núñez, Fernando; Usabiaga, Carlos
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 135159
 ISSN:
 01679473
 Subject:
 databases; labor; labor market
 Abstract:
 ... The general framework of contingency tables is used to develop previous methodological contributions on labour matching data. A contingency table is generated by the combination of the multiple characteristics that define each row and column category (worker and job categories in our field). In this context, a dimension problem arises that has to be addressed. Two key concepts related to the labou ...
 DOI:
 10.1016/j.csda.2018.05.012

http://dx.doi.org/10.1016/j.csda.2018.05.012
 Author:
 Hubin, Aliaksandr; Storvik, Geir
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 281297
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; crime; epigenetics; models; prediction; United States
 Abstract:
 ... Generalized linear mixed models (GLMM) are used for inference and prediction in a wide range of different applications providing a powerful scientific tool. An increasing number of sources of data are becoming available, introducing a variety of candidate explanatory variables for these models. Selection of an optimal combination of variables is thus becoming crucial. In a Bayesian setting, the po ...
 DOI:
 10.1016/j.csda.2018.05.020

http://dx.doi.org/10.1016/j.csda.2018.05.020
 Author:
 Yu, Jun; Kong, Xiangshun; Ai, Mingyao; Tsui, Kwok Leung
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 217228
 ISSN:
 01679473
 Subject:
 dose response; medicine; models; patients; planning; precision medicine
 Abstract:
 ... Personalized medicine is becoming more and more important nowadays since the efficacy of a certain medicine vary among different patients. This requires to combine the effects of the prognostic factors or covariates along with different dosages when planning a dose–response experiment. Statistically, this corresponds to the construction of optimal designs for estimating dose–response curves in the ...
 DOI:
 10.1016/j.csda.2018.05.017

http://dx.doi.org/10.1016/j.csda.2018.05.017
 Author:
 Zhang, Michael Minyi; Lam, Henry; Lin, Lizhen
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 229247
 ISSN:
 01679473
 Subject:
 Bayesian theory; data collection; geometry; models; selection criteria
 Abstract:
 ... Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large datasets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that damage the inference quality. The parallel “divide and conquer” model selection str ...
 DOI:
 10.1016/j.csda.2018.05.016

http://dx.doi.org/10.1016/j.csda.2018.05.016
 Author:
 Bhattacharya, Arnab; Wilson, Simon P.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 187203
 ISSN:
 01679473
 Subject:
 Bayesian theory; models; prediction
 Abstract:
 ... A method for sequential Bayesian inference of the static parameters of a dynamic state space model is proposed. The method is able to use any valid approximation to the filtering and prediction densities of the state process. It computes the posterior distribution of the static parameters on a discrete grid that tracks the support dynamically. For inference of the state process, the Kalman filter ...
 DOI:
 10.1016/j.csda.2018.05.018

http://dx.doi.org/10.1016/j.csda.2018.05.018
 Author:
 Dai, Xinjie; Niu, Cuizhen; Guo, Xu
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 1531
 ISSN:
 01679473
 Subject:
 algorithms; statistics
 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:
 Giguelay, J.; Huet, S.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 96115
 ISSN:
 01679473
 Subject:
 data analysis
 Abstract:
 ... The development of nonparametric procedures for testing shape constraint (monotonicity, convexity, unimodality, etc.) has received increasing interest. Nevertheless, testing the kmonotonicity of a discrete density for k larger than 2 has received little attention. To deal with this issue, several testing procedures based on the empirical distribution of the observations have been developed. They ...
 DOI:
 10.1016/j.csda.2018.02.006

http://dx.doi.org/10.1016/j.csda.2018.02.006
 Author:
 Zhao, Weihua; Zhou, Yan; Lian, Heng
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 3249
 ISSN:
 01679473
 Subject:
 Monte Carlo method; markets; models
 Abstract:
 ... We consider simultaneous semiparametric estimation of conditional quantiles for multiple responses using a dynamic singleindex structure. Motivated by a financial application, a market factor index is constructed that is shared among different portfolios which results in a more interpretable and efficient model, compared to separately building multiple conditional quantiles. On the other hand, th ...
 DOI:
 10.1016/j.csda.2018.05.006

http://dx.doi.org/10.1016/j.csda.2018.05.006
 Author:
 Chen, Nan; Carlin, Bradley P.; Hobbs, Brian P.
 Source:
 Computational statistics & data analysis 2018 v.127 pp. 5068
 ISSN:
 01679473
 Subject:
 Bayesian theory; Internet; computer software; computers; languages; models; patients; randomized clinical trials
 Abstract:
 ... A collection of webbased statistical tools (http://research.mdacc.tmc.edu/SmeeactWeb/) are described that enable investigators to incorporate historical control data into analysis of randomized clinical trials using Bayesian hierarchical modeling as well as implement adaptive designs that balance posterior effective sample sizes among the study arms and thus maximize power. With balanced allocati ...
 DOI:
 10.1016/j.csda.2018.05.002

http://dx.doi.org/10.1016/j.csda.2018.05.002