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 Author:
 Zhao, Haibing; Fung, Wing Kam
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 6070
 ISSN:
 01679473
 Subject:
 data analysis
 Abstract:
 ... A powerful test procedure is proposed for multiple hypotheses for the false discovery rate (FDR) control. The proposed procedure is a weighted pvalue procedure which explores false null hypotheses information. It is theoretically shown to control the FDR and be more powerful than the widely used plugin BH procedure. When there are unknown parameters estimated from the data, the asymptotic proper ...
 DOI:
 10.1016/j.csda.2015.12.013

http://dx.doi.org/10.1016/j.csda.2015.12.013
 Author:
 Gallardo, Diego I.; Bolfarine, Heleno; PedrosodeLima, Antonio Carlos
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 3145
 ISSN:
 01679473
 Subject:
 Bayesian theory; models; statistical analysis
 Abstract:
 ... In this paper, random effects are included in the destructive weighted Poisson cure rate model. For parameter estimation we implemented a classical approach based on the restricted maximum likelihood (REML) methodology and a Bayesian approach based on Dirichlet process priors. A small scale simulation study is conducted to discuss parameter recovery and the performance of the proposed methodology ...
 DOI:
 10.1016/j.csda.2015.12.006

http://dx.doi.org/10.1016/j.csda.2015.12.006
 Author:
 Park, Myung Hyun; Kim, Joseph H.T.
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 91104
 ISSN:
 01679473
 Subject:
 data collection; least squares; models; risk
 Abstract:
 ... The generalized Pareto distribution (GPD) has been widely used in modelling heavy tail phenomena in many applications. The standard practice is to fit the tail region of the dataset to the GPD separately, a framework known as the peaksoverthreshold (POT) in the extreme value literature. In this paper we propose a new GPD parameter estimator, under the POT framework, to estimate common tail risk ...
 DOI:
 10.1016/j.csda.2015.12.008

http://dx.doi.org/10.1016/j.csda.2015.12.008
 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:
 Bak, Britta Anker; Jensen, Jens Ledet
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 4659
 ISSN:
 01679473
 Subject:
 mathematics
 Abstract:
 ... A binary classification problem is imbalanced when the number of samples from the two groups differs. For the high dimensional case, where the number of variables is much larger than the number of samples, imbalance leads to a bias in the classification. The independence classifier is studied theoretically and based on the analysis two new classifiers are suggested that can handle any imbalance ra ...
 DOI:
 10.1016/j.csda.2015.12.009

http://dx.doi.org/10.1016/j.csda.2015.12.009
 Author:
 Takahashi, Akihito; Kurosawa, Takeshi
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 7178
 ISSN:
 01679473
 Subject:
 linear models; normal distribution; regression analysis
 Abstract:
 ... This study examines measures of predictive power for a generalized linear model (GLM). Although many measures of predictive power for GLMs have been proposed, most have limitations. Hence, we focus on the regression correlation coefficient (RCC) (Zheng and Agresti, 2000), which satisfies the four requirements of (i) interpretability, (ii) applicability, (iii) consistency, and (iv) affinity. The RC ...
 DOI:
 10.1016/j.csda.2015.12.012

http://dx.doi.org/10.1016/j.csda.2015.12.012
 Author:
 Ikemoto, Hiroki; Adachi, Kohei
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 118
 ISSN:
 01679473
 Subject:
 algorithms; data collection; least squares; models; principal component analysis
 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:
 Oedekoven, C.S.; King, R.; Buckland, S.T.; Mackenzie, M.L.; Evans, K.O.; Burger, L.W.
 Source:
 Computational statistics & data analysis 2016 v.98 pp. 7990
 ISSN:
 01679473
 Subject:
 Markov chain; Passerina cyanea; algorithms; case studies; models; standard deviation
 Abstract:
 ... Hierarchical centering has been described as a reparameterization method applicable to random effects models. It has been shown to improve mixing of models in the context of Markov chain Monte Carlo (MCMC) methods. A hierarchical centering approach is proposed for reversible jump MCMC (RJMCMC) chains which builds upon the hierarchical centering methods for MCMC chains and uses them to reparameteri ...
 DOI:
 10.1016/j.csda.2015.12.010

http://dx.doi.org/10.1016/j.csda.2015.12.010