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 Author:
 NuñezAntonio, Gabriel; GutiérrezPeña, Eduardo
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 506519
 ISSN:
 01679473
 Subject:
 Bayesian theory; data collection; models; normal distribution
 Abstract:
 ... The analysis of short longitudinal series of circular data may be problematic and to some extent has not been fully developed. A Bayesian analysis of a new model for such data is presented. The model is based on a radial projection onto the circle of a particular bivariate normal distribution. Inference about the parameters of the model is based on samples from the corresponding joint posterior de ...
 DOI:
 10.1016/j.csda.2012.07.025

http://dx.doi.org/10.1016/j.csda.2012.07.025
 Author:
 Georgiou, Stelios D.; Stylianou, Stella; Aggarwal, Manohar
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 11241133
 ISSN:
 01679473
 Subject:
 algorithms; models; response surface methodology
 Abstract:
 ... A class of efficient and economical response surface designs that can be constructed using known designs is introduced. The proposed class of designs is a modification of the Central Composite Designs, in which the axial points of the traditional central composite design are replaced by some edge points of the hypercube that circumscribes the sphere of zero center and radius a. An algorithm for th ...
 DOI:
 10.1016/j.csda.2013.03.010

http://dx.doi.org/10.1016/j.csda.2013.03.010
 Author:
 Polymenis, Athanase
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 107115
 ISSN:
 01679473
 Subject:
 algorithms; models
 Abstract:
 ... Modified MIR is a MonteCarlo algorithm used for bootstrapping minimum information ratios in order to assess the number of unknown components in finite mixtures. The method was proposed as a modification of the minimum information ratio (MIR) method, and was proved to outperform it. Further simulations and a comparison with some other approaches confirm that the method works well for reasonable sa ...
 DOI:
 10.1016/j.csda.2013.01.028

http://dx.doi.org/10.1016/j.csda.2013.01.028
 Author:
 Sambo, Francesco; Borrotti, Matteo; Mylona, Kalliopi
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 11931207
 ISSN:
 01679473
 Subject:
 algorithms; case studies; cost effectiveness; experimental design
 Abstract:
 ... Many industrial experiments involve one or more restrictions on the randomization. In such cases, the splitplot design structure, in which the experimental runs are performed in groups, is a commonly used costefficient approach that reduces the number of independent settings of the hardtochange factors. Several criteria can be adopted for optimizing splitplot experimental designs: the most fr ...
 DOI:
 10.1016/j.csda.2013.03.015

http://dx.doi.org/10.1016/j.csda.2013.03.015
 Author:
 Chen, Bingshu E.; Jiang, Wenyu; Tu, Dongsheng
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 324334
 ISSN:
 01679473
 Subject:
 Bayesian theory; acid phosphatase; biomarkers; blood serum; clinical trials; cost effectiveness; gene expression; models; neoplasms; patients; prediction; probability; probability distribution; statistical inference; therapeutics; toxicity
 Abstract:
 ... Some baseline patient factors, such as biomarkers, are useful in predicting patients’ responses to a new therapy. Identification of such factors is important in enhancing treatment outcomes, avoiding potentially toxic therapy that is destined to fail and improving the costeffectiveness of treatment. Many of the biomarkers, such as gene expression, are measured on a continuous scale. A threshold o ...
 DOI:
 10.1016/j.csda.2013.05.015

http://dx.doi.org/10.1016/j.csda.2013.05.015
 Author:
 Calò, Daniela G.; Montanari, Angela; Viroli, Cinzia
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 7991
 ISSN:
 01679473
 Subject:
 data collection; models; probability distribution
 Abstract:
 ... The problem of clustering probability density functions is emerging in different scientific domains. The methods proposed for clustering probability density functions are mainly focused on univariate settings and are based on heuristic clustering solutions. New aspects of the problem associated with the multivariate setting and a modelbased perspective are investigated. The novel approach relies ...
 DOI:
 10.1016/j.csda.2013.04.013

http://dx.doi.org/10.1016/j.csda.2013.04.013
 Author:
 Gaffke, N.; Graßhoff, U.; Schwabe, R.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 11131123
 ISSN:
 01679473
 Subject:
 algorithms; heteroskedasticity; models; regression analysis; variance covariance matrix
 Abstract:
 ... The basic structure of algorithms for numerical computation of optimal approximate linear regression designs is briefly summarized. First order methods are contrasted to second order methods. A first order method, also called a vertex direction method, uses a local linear approximation of the optimality criterion at the actual point. A second order method is a Newton or quasiNewton method, employ ...
 DOI:
 10.1016/j.csda.2013.07.029

http://dx.doi.org/10.1016/j.csda.2013.07.029
 Author:
 Galimberti, Giuliano; Soffritti, Gabriele
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 138150
 ISSN:
 01679473
 Subject:
 Monte Carlo method; algorithms; data collection; models; regression analysis
 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:
 Dernoncourt, David; Hanczar, Blaise; Zucker, JeanDaniel
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 681693
 ISSN:
 01679473
 Subject:
 data collection; mathematical models
 Abstract:
 ... Feature selection is an important step when building a classifier on high dimensional data. As the number of observations is small, the feature selection tends to be unstable. It is common that two feature subsets, obtained from different datasets but dealing with the same classification problem, do not overlap significantly. Although it is a crucial problem, few works have been done on the select ...
 DOI:
 10.1016/j.csda.2013.07.012

http://dx.doi.org/10.1016/j.csda.2013.07.012
 Author:
 Godolphin, J.D.; Warren, H.R.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 11341146
 ISSN:
 01679473
 Subject:
 experimental design
 Abstract:
 ... Knowledge of the cardinality and the number of minimal rank reducing observation sets in experimental design is important information which makes a useful contribution to the statistician’s toolkit to assist in the selection of incomplete block designs. Its prime function is to guard against choosing a design that is likely to be altered to a disconnected eventual design if observations are lost ...
 DOI:
 10.1016/j.csda.2013.09.025

http://dx.doi.org/10.1016/j.csda.2013.09.025
 Author:
 Gupta, Mayetri
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 375391
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; data collection; genomewide association study
 Abstract:
 ... In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering or biclustering is a more appropriate technique to use compared to onedimensional clustering. A flexible ...
 DOI:
 10.1016/j.csda.2013.07.006

http://dx.doi.org/10.1016/j.csda.2013.07.006
 Author:
 Jaspers, Stijn; Aerts, Marc; Verbeke, Geert; Beloeil, PierreAlexandre
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 3042
 ISSN:
 01679473
 Subject:
 antibiotic resistance; minimum inhibitory concentration; models; monitoring; public health
 Abstract:
 ... Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of nonwildtype isolates has therefore gained increased interest. Monitoring is performed, based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. For a given antimicrobial, it is common pr ...
 DOI:
 10.1016/j.csda.2013.01.024

http://dx.doi.org/10.1016/j.csda.2013.01.024
 Author:
 Gutman, Alex J.; White, Edward D.; Lin, Dennis K.J.; Hill, Raymond R.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 11471158
 ISSN:
 01679473
 Subject:
 Bayesian theory
 Abstract:
 ... A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible followup runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian Doptimality criterion to increase the informati ...
 DOI:
 10.1016/j.csda.2013.09.009

http://dx.doi.org/10.1016/j.csda.2013.09.009
 Author:
 Golyandina, Nina; Korobeynikov, Anton
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 934954
 ISSN:
 01679473
 Subject:
 algorithms; case studies; time series analysis
 Abstract:
 ... Singular Spectrum Analysis (SSA) is a powerful tool of analysis and forecasting of time series. The main features of the Rssa package, which efficiently implements the SSA algorithms and methodology in R, are described. Analysis, forecasting and parameter estimation are demonstrated using case studies. These studies are supplemented with accompanying code fragments. ...
 DOI:
 10.1016/j.csda.2013.04.009

http://dx.doi.org/10.1016/j.csda.2013.04.009
 Author:
 Rajala, T.; Penttinen, A.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 530541
 ISSN:
 01679473
 Subject:
 Bayesian theory; Riparia riparia; models; nests; normal distribution
 Abstract:
 ... A Bayesian solution is suggested for the modelling of spatial point patterns with inhomogeneous hardcore radius using Gaussian processes in the regularization. The key observation is that a straightforward use of the finite Gibbs hardcore process likelihood together with a logGaussian random field prior does not work without penalisation towards high local packing density. Instead, a nearest ne ...
 DOI:
 10.1016/j.csda.2012.08.014

http://dx.doi.org/10.1016/j.csda.2012.08.014
 Author:
 So, Mike K.P.; Chan, Raymond K.S.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 568587
 ISSN:
 01679473
 Subject:
 Bayesian theory; asymmetry; capital; commodity futures; economic crises; empirical research; models; oils; risk
 Abstract:
 ... A threshold extreme value distribution for modeling standardized financial returns is investigated. The main theme is tail asymmetry, which means that the left and right tails of the standardized return distribution are not identical. The peakoverthreshold idea in extreme value theory is adopted to construct the threshold extreme value distribution with two generalized Pareto tails for modeling ...
 DOI:
 10.1016/j.csda.2013.02.008

http://dx.doi.org/10.1016/j.csda.2013.02.008
 Author:
 Naranjo, L.; Martín, J.; Pérez, C.J.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 464476
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; cumulative distribution; linear models; normal distribution; regression analysis
 Abstract:
 ... A flexible Bayesian approach to a generalized linear model is proposed to describe the dependence of binary data on explanatory variables. The inverse of the exponential power cumulative distribution function is used as the link to the binary regression model. The exponential power family provides distributions with both lighter and heavier tails compared to the normal distribution, and includes t ...
 DOI:
 10.1016/j.csda.2012.07.022

http://dx.doi.org/10.1016/j.csda.2012.07.022
 Author:
 Sabourin, Anne; Naveau, Philippe
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 542567
 ISSN:
 01679473
 Subject:
 Bayesian theory; Markov chain; algorithms; models; monitoring
 Abstract:
 ... The probabilistic framework of extreme value theory is wellknown: the dependence structure of large events is characterized by an angular measure on the positive orthant of the unit sphere. The family of these angular measures is nonparametric by nature. Nonetheless, any angular measure may be approached arbitrarily well by a mixture of Dirichlet distributions. The semiparametric Dirichlet mixt ...
 DOI:
 10.1016/j.csda.2013.04.021

http://dx.doi.org/10.1016/j.csda.2013.04.021
 Author:
 Abebe, Haftom T.; Tan, Frans E.S.; Van Breukelen, Gerard J.P.; Berger, Martijn P.F.
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 10661076
 ISSN:
 01679473
 Subject:
 Bayesian theory; covariance; models; regression analysis
 Abstract:
 ... Bayesian optimal designs for binary longitudinal responses analyzed with mixed logistic regression describing a linear time effect are considered. In order to find the optimal number and allocations of time points, for different priors, cost constraints and covariance structures of the random effects, a scalar function of the approximate information matrix based on the first order penalized quasi ...
 DOI:
 10.1016/j.csda.2013.07.040

http://dx.doi.org/10.1016/j.csda.2013.07.040
 Author:
 Bekiros, Stelios D.; Paccagnini, Alessia
 Source:
 Computational statistics & data analysis 2014 v.71 pp. 298323
 ISSN:
 01679473
 Subject:
 Bayesian theory; Consumer Price Index; capital; data collection; gross domestic product; models; monetary policy; policy analysis; prediction; prices; time series analysis; United States
 Abstract:
 ... Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estim ...
 DOI:
 10.1016/j.csda.2013.09.018

http://dx.doi.org/10.1016/j.csda.2013.09.018