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 Author:
 Zhou, Ruoyu; Shu, Lianjie; Su, Yan
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 134146
 ISSN:
 01679473
 Subject:
 statistical analysis
 Abstract:
 ... The clustering methodologies based on minimum spanning tree (MST) have been widely discussed due to their simplicity and efficiency in signaling irregular clusters. However, most of the MSTbased clustering methods estimate the most likely cluster based on the maximum likelihood ratio from the resulting subtrees after the removal of edges of the MST. They can only estimate one cluster even if ther ...
 DOI:
 10.1016/j.csda.2015.03.008

http://dx.doi.org/10.1016/j.csda.2015.03.008
 Author:
 Shen, Yanfeng; Lin, Zhengyan
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 2538
 ISSN:
 01679473
 Subject:
 covariance
 Abstract:
 ... The problem of testing the mean vector in a highdimensional setting is considered. Up to date, most highdimensional tests for the mean vector only make use of the marginal information from the variables, and do not incorporate the correlation information into the test statistics. A new testing procedure is proposed, which makes use of the covariance information between the variables. The new app ...
 DOI:
 10.1016/j.csda.2015.03.004

http://dx.doi.org/10.1016/j.csda.2015.03.004
 Author:
 Lee, Sangin; Pawitan, Yudi; Lee, Youngjo
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 147157
 ISSN:
 01679473
 Subject:
 algorithms; models; prediction; regression analysis
 Abstract:
 ... We consider regression models with a group structure in explanatory variables. This structure is commonly seen in practice, but it is only recently realized that taking the information into account in the modeling process may improve both the interpretability and accuracy of the model. In this paper, we study a new approach to group variable selection using randomeffect models. Specific distribut ...
 DOI:
 10.1016/j.csda.2015.02.020

http://dx.doi.org/10.1016/j.csda.2015.02.020
 Author:
 Ye, Peng; Zhao, Xingqiu; Sun, Liuquan; Xu, Wei
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 3950
 ISSN:
 01679473
 Subject:
 equations; models; observational studies
 Abstract:
 ... Multivariate recurrent event data arise in many clinical and observational studies, in which subjects may experience multiple types of recurrent events. In some applications, event times can be always observed, but types for some events may be missing. In this article, a semiparametric additive rates model is proposed for analyzing multivariate recurrent event data when event categories are missin ...
 DOI:
 10.1016/j.csda.2015.03.002

http://dx.doi.org/10.1016/j.csda.2015.03.002
 Author:
 González, Jorge; Barrientos, Andrés F.; Quintana, Fernando A.
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 222244
 ISSN:
 01679473
 Subject:
 Bayesian theory; models
 Abstract:
 ... Equating is an important step in the process of collecting, analyzing, and reporting test scores in any program of assessment. Methods of equating utilize functions to transform scores on two or more versions of a test, so that they can be compared and used interchangeably. In common practice, traditional methods of equating use either parametric or semiparametric models where, apart from the tes ...
 DOI:
 10.1016/j.csda.2015.03.012

http://dx.doi.org/10.1016/j.csda.2015.03.012
 Author:
 Lazariv, Taras; Okhrin, Yarema; Schmid, Wolfgang
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 115125
 ISSN:
 01679473
 Subject:
 monitoring; variance
 Abstract:
 ... A general family of EWMA charts is considered for monitoring an arbitrary parameter of the target process. The distribution of the run length is analysed for the case when the smoothing parameter tends to zero. The key impact on the results from the use of the exact variance of the control statistics vs. the asymptotic one and the presence of a head start. For fixed head start, the run lengths for ...
 DOI:
 10.1016/j.csda.2015.03.010

http://dx.doi.org/10.1016/j.csda.2015.03.010
 Author:
 Yue, Chen; Chen, Shaojie; Sair, Haris I.; Airan, Raag; Caffo, Brian S.
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 126133
 ISSN:
 01679473
 Subject:
 Markov chain; algorithms; data collection; models
 Abstract:
 ... Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intraclass correlation coefficient (I2C2) is generalized and the graphical intraclass correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probitlinear m ...
 DOI:
 10.1016/j.csda.2015.02.012

http://dx.doi.org/10.1016/j.csda.2015.02.012
 Author:
 Tian, Yuzhu; Zhu, Qianqian; Tian, Maozai
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 8596
 ISSN:
 01679473
 Subject:
 Monte Carlo method; Weibull statistics; algorithms; data collection; models
 Abstract:
 ... The typeII progressively hybrid censoring scheme can be deemed as a mixture of typeII progressive and hybrid censoring schemes, which has been utilized to analyze lifetime data in the literature for exponential distribution and Weibull distribution and so on, where the experiment terminates at a prespecified time. However, little attention has been paid to parametric estimation under this censo ...
 DOI:
 10.1016/j.csda.2015.03.003

http://dx.doi.org/10.1016/j.csda.2015.03.003
 Author:
 JiménezGamero, M. Dolores; Kim, HyoungMoon
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 172191
 ISSN:
 01679473
 Subject:
 statistical analysis
 Abstract:
 ... A class of goodnessoffit tests whose test statistic is an L2 norm of the difference of the empirical characteristic function of the sample and a parametric estimate of the characteristic function in the null hypothesis, is considered. The null distribution is usually estimated through a parametric bootstrap. Although very easy to implement, the parametric bootstrap can become very computationall ...
 DOI:
 10.1016/j.csda.2015.03.015

http://dx.doi.org/10.1016/j.csda.2015.03.015
 Author:
 Tian, GuoLiang; Ma, Huijuan; Zhou, Yong; Deng, Dianliang
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 97114
 ISSN:
 01679473
 Subject:
 algorithms; binomial distribution; confidence interval; data collection; models; regression analysis; statistical inference
 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
 Author:
 Wang, Qing; Lindsay, Bruce G.
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 5171
 ISSN:
 01679473
 Subject:
 methodology; risk
 Abstract:
 ... Crossvalidation methodologies have been widely used as a means of selecting tuning parameters in nonparametric statistical problems. In this paper we focus on a new method for improving the reliability of crossvalidation. We implement this method in the context of the kernel density estimator, where one needs to select the bandwidth parameter so as to minimize L2 risk. This method is a twostage ...
 DOI:
 10.1016/j.csda.2015.03.005

http://dx.doi.org/10.1016/j.csda.2015.03.005
 Author:
 Torabi, Mahmoud; Lele, Subhash R.; Prasad, Narasimha G.N.
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 158171
 ISSN:
 01679473
 Subject:
 Bayesian theory; Markov chain; algorithms; data analysis; issues and policy; models; prediction; statistical analysis; surveys
 Abstract:
 ... Policy decisions regarding allocation of resources to subgroups in a population, called small areas, are based on reliable predictors of their underlying parameters. However, in sample surveys, the information to estimate reliable predictors is often insufficient at the level of the small areas. Hence, parameters of the subgroups are often predicted based on the coarser scale data. In view of this ...
 DOI:
 10.1016/j.csda.2015.03.013

http://dx.doi.org/10.1016/j.csda.2015.03.013
 Author:
 Swihart, Bruce J.; Punjabi, Naresh M.; Crainiceanu, Ciprian M.
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 111
 ISSN:
 01679473
 Subject:
 breathing; case studies; heart health; models; sleep
 Abstract:
 ... Methods are introduced for the analysis of large sets of sleep study data (hypnograms) using a 5state 20transitiontype structure defined by the American Academy of Sleep Medicine. Application of these methods to the hypnograms of 5598 subjects from the Sleep Heart Health Study provide: the first analysis of sleep hypnogram data of such size and complexity in a community cohort with a range of s ...
 DOI:
 10.1016/j.csda.2015.03.001

http://dx.doi.org/10.1016/j.csda.2015.03.001
 Author:
 Hino, Hideitsu; Koshijima, Kensuke; Murata, Noboru
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 7284
 ISSN:
 01679473
 Subject:
 entropy; probability; probability distribution; regression analysis
 Abstract:
 ... Estimators for differential entropy are proposed. The estimators are based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Simple linear regression is utilized to estimate the values of density function and its second derivative at a point. After estimating the values of the probability density function at each of the g ...
 DOI:
 10.1016/j.csda.2015.03.011

http://dx.doi.org/10.1016/j.csda.2015.03.011
 Author:
 Zhao, YanYong; Lin, JinGuan; Xu, PeiRong; Ye, XuGuo
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 204221
 ISSN:
 01679473
 Subject:
 data collection; heteroskedasticity; models; variance
 Abstract:
 ... This paper is concerned with the estimation in semivarying coefficient models with heteroscedastic errors. An iterated twostage orthogonalityprojectionbased estimation is proposed. This method can easily be used to estimate the model parametric and nonparametric parts, as well as the variance function, and in the estimators the parametric part and nonparametric part do not affect each other. U ...
 DOI:
 10.1016/j.csda.2015.03.018

http://dx.doi.org/10.1016/j.csda.2015.03.018
 Author:
 Kawano, Shuichi; Fujisawa, Hironori; Takada, Toyoyuki; Shiroishi, Toshihiko
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 192203
 ISSN:
 01679473
 Subject:
 Monte Carlo method; algorithms; regression analysis; system optimization
 Abstract:
 ... Principal component regression (PCR) is a twostage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To address this problem, we propose the sparse principal component regression (SPCR) tha ...
 DOI:
 10.1016/j.csda.2015.03.016

http://dx.doi.org/10.1016/j.csda.2015.03.016
 Author:
 Vu, Duy; Aitkin, Murray
 Source:
 Computational statistics & data analysis 2015 v.89 pp. 1224
 ISSN:
 01679473
 Subject:
 algorithms; microarray technology; models; statistical analysis
 Abstract:
 ... Biclustering is an important tool in exploratory statistical analysis which can be used to detect latent row and column groups of different response patterns. However, few studies include covariate data directly into their biclustering models to explain these variations. A novel biclustering framework that considers both stochastic block structures and covariate effects is proposed to address this ...
 DOI:
 10.1016/j.csda.2015.02.015

http://dx.doi.org/10.1016/j.csda.2015.02.015