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 Author:
 Marbac, Matthieu; Sedki, Mohammed
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 130145
 ISSN:
 01679473
 Subject:
 algorithms; computer software; data collection; models; probability
 Abstract:
 ... A new family of onefactor distributions for modeling highdimensional binary data is introduced. The model provides an explicit probability for each event, thus avoiding the numeric approximations often made by existing methods. Model interpretation is easy, because each variable is described by two continuous parameters (corresponding to the marginal probability and to the strength of dependency ...
 DOI:
 10.1016/j.csda.2017.04.010

http://dx.doi.org/10.1016/j.csda.2017.04.010
 Author:
 Yu, Chang; Zelterman, Daniel
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 105118
 ISSN:
 01679473
 Subject:
 data collection; gene expression; microarray technology; models
 Abstract:
 ... Microarray studies generate a large number of pvalues from many gene expression comparisons. The estimate of the proportion of the pvalues sampled from the null hypothesis draws broad interest. The twocomponent mixture model is often used to estimate this proportion. If the data are generated under the null hypothesis, the pvalues follow the uniform distribution. What is the distribution of p ...
 DOI:
 10.1016/j.csda.2017.04.008

http://dx.doi.org/10.1016/j.csda.2017.04.008
 Author:
 Gorynin, Ivan; Derrode, Stéphane; Monfrini, Emmanuel; Pieczynski, Wojciech
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 3846
 ISSN:
 01679473
 Subject:
 algorithms; models
 Abstract:
 ... Statistical smoothing in general nonlinear nonGaussian systems is a challenging problem. A new smoothing method based on approximating the original system by a recent switching model has been introduced. Such switching model allows fast and optimal smoothing. The new algorithm is validated through an application on stochastic volatility and dynamic beta models. Simulation experiments indicate it ...
 DOI:
 10.1016/j.csda.2017.04.007

http://dx.doi.org/10.1016/j.csda.2017.04.007
 Author:
 Yu, Philip L.H.; Wang, Xiaohang; Zhu, Yuanyuan
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 1225
 ISSN:
 01679473
 Subject:
 genomics; variance covariance matrix
 Abstract:
 ... It is well known that when the dimension of the data becomes very large, the sample covariance matrix S will not be a good estimator of the population covariance matrix Σ. Using such estimator, one typical consequence is that the estimated eigenvalues from S will be distorted. Many existing methods tried to solve the problem, and examples of which include regularizing Σ by thresholding or banding. ...
 DOI:
 10.1016/j.csda.2017.04.004

http://dx.doi.org/10.1016/j.csda.2017.04.004
 Author:
 Jeon, JongJune; Kwon, Sunghoon; Choi, Hosik
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 6174
 ISSN:
 01679473
 Subject:
 algorithms; linear models; regression analysis
 Abstract:
 ... We propose to use a penalized estimator for detecting homogeneity of the highdimensional generalized linear model. Here, the homogeneity is a specific model structure where regression coefficients are grouped having exactly the same value in each group. The proposed estimator achieves weak oracle property under mild regularity conditions and is invariant to the choice of reference levels when the ...
 DOI:
 10.1016/j.csda.2017.04.001

http://dx.doi.org/10.1016/j.csda.2017.04.001
 Author:
 Baddeley, Adrian; Hardegen, Andrew; Lawrence, Thomas; Milne, Robin K.; Nair, Gopalan; Rakshit, Suman
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 7587
 ISSN:
 01679473
 Subject:
 Monte Carlo method
 Abstract:
 ... A major weakness of the classical Monte Carlo test is that it is biased when the null hypothesis is composite. This problem persists even when the number of simulations tends to infinity. A standard remedy is to perform a double bootstrap test involving two stages of Monte Carlo simulation: under suitable conditions, this test is asymptotically exact for any fixed significance level. However, the ...
 DOI:
 10.1016/j.csda.2017.04.003

http://dx.doi.org/10.1016/j.csda.2017.04.003
 Author:
 Shults, Justine
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 111
 ISSN:
 01679473
 Subject:
 algorithms; equations; mathematical theory; sampling
 Abstract:
 ... The ability to simulate correlated binary data is important for sample size calculation and comparison of methods for analyzing clustered and longitudinal data with dichotomous outcomes. One available approach for simulating vectors of length n of dichotomous random variables is to sample them from multinomial distribution of all possible length n permutations of zeros and ones. However, the multi ...
 DOI:
 10.1016/j.csda.2017.04.002

http://dx.doi.org/10.1016/j.csda.2017.04.002
 Author:
 Wang, Chunlin; Marriott, Paul; Li, Pengfei
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 146157
 ISSN:
 01679473
 Subject:
 models
 Abstract:
 ... The question of testing the homogeneity of distributions is studied when there is an excess of zeros in the data. In this situation, the distribution of each sample is naturally characterized by a nonstandard mixture of a singular distribution at zero and a positive component. To model the positive components, a semiparametric multiplesample density ratio model is employed. Under this setup, a n ...
 DOI:
 10.1016/j.csda.2017.04.011

http://dx.doi.org/10.1016/j.csda.2017.04.011
 Author:
 Sahoo, Shyamsundar; Sengupta, Debasis
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 119129
 ISSN:
 01679473
 Subject:
 models
 Abstract:
 ... Tests designed to detect increasing hazard ratio against the proportional hazards hypothesis are generally consistent for other alternatives also. This article provides a test of the null hypothesis of increasing hazard ratio. The test is based on the separation between an empirical version of the relative trend function and its greatest convex minorant. The proportional hazards model, the least f ...
 DOI:
 10.1016/j.csda.2017.04.009

http://dx.doi.org/10.1016/j.csda.2017.04.009
 Author:
 Sugasawa, Shonosuke; Kubokawa, Tatsuya
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 4760
 ISSN:
 01679473
 Subject:
 models; prediction; surveys
 Abstract:
 ... In real applications of small area estimation, one often encounters data with positive response values. The use of a parametric transformation for positive response values in the Fay–Herriot model is proposed for such a case. An asymptotically unbiased small area predictor is derived and a secondorder unbiased estimator of the mean squared error is established using the parametric bootstrap. Thro ...
 DOI:
 10.1016/j.csda.2017.03.017

http://dx.doi.org/10.1016/j.csda.2017.03.017
 Author:
 Li, Xingxiang; Cheng, Guosheng; Wang, Liming; Lai, Peng; Song, Fengli
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 88104
 ISSN:
 01679473
 Subject:
 Monte Carlo method; linear models; screening
 Abstract:
 ... This work is concerned with feature screening for linear model with multivariate responses and ultrahigh dimensional covariates. Instead of utilizing the correlation between every response and covariate, the linear space spanned by the multivariate responses is considered in this paper. Based on the projection theory, each covariate is projected on the linear space spanned by the multivariate resp ...
 DOI:
 10.1016/j.csda.2017.04.006

http://dx.doi.org/10.1016/j.csda.2017.04.006
 Author:
 Carzolio, Marcos; Leman, Scotland
 Source:
 Computational statistics & data analysis 2017 v.114 pp. 2637
 ISSN:
 01679473
 Subject:
 algorithms; breast neoplasms; case studies; models; tempering
 Abstract:
 ... The application of Bayesian methods often requires Metropolis–Hastings or related algorithms to sample from an intractable posterior distribution. In especially challenging cases, such as with strongly correlated parameters or multimodal posteriors, exotic forms of Metropolis–Hastings are preferred for generating samples within a reasonable time. These algorithms require nontrivial and often prohi ...
 DOI:
 10.1016/j.csda.2017.04.005

http://dx.doi.org/10.1016/j.csda.2017.04.005