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 Author:
 Palhazi Cuervo, Daniel; Goos, Peter; Sörensen, Kenneth
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 224249
 ISSN:
 01679473
 Subject:
 experimental design; models
 Abstract:
 ... Twostratum experiments are widely used in the event a complete randomization is not possible. In some experimental scenarios, there are constraints that limit the number of observations that can be made under homogeneous conditions. In other scenarios, there are factors whose levels are hard or expensive to change. In both of these scenarios, it is necessary to arrange the observations in differe ...
 DOI:
 10.1016/j.csda.2017.06.006

http://dx.doi.org/10.1016/j.csda.2017.06.006
 Author:
 Švendová, Vendula; Schimek, Michael G.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 122135
 ISSN:
 01679473
 Subject:
 algorithms; equipment; humans; metaanalysis
 Abstract:
 ... The ranking of objects, such as journals, institutions or biological entities, is broadly used to assess the relative quality or relevance of such objects. A multiple ranking is performed by a number of assessors (humans or machines) and inference about the nature of the observed rankings is desirable for evaluation, business or scientific purposes. The assessors’ decisions are based on some inher ...
 DOI:
 10.1016/j.csda.2017.05.010

http://dx.doi.org/10.1016/j.csda.2017.05.010
 Author:
 Lee, Keunbaik; Baek, Changryong; Daniels, Michael J.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 267280
 ISSN:
 01679473
 Subject:
 heteroskedasticity; linear models; longitudinal studies; lung neoplasms; variance covariance matrix
 Abstract:
 ... In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome these limita ...
 DOI:
 10.1016/j.csda.2017.05.001

http://dx.doi.org/10.1016/j.csda.2017.05.001
 Author:
 Leonard, Robert D.; Edwards, David J.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 7990
 ISSN:
 01679473
 Subject:
 Bayesian theory; experimental design; models; screening; statistical inference
 Abstract:
 ... Screening designs are frequently used in the initial stages of experimentation with the goal of identifying important main effects as well as to gain insight on potentially important twofactor interactions. Commonly utilized experimental designs for screening are unreplicated and as such, provide no unbiased estimate of experimental error. However, if statistical inference is to be performed as p ...
 DOI:
 10.1016/j.csda.2017.05.014

http://dx.doi.org/10.1016/j.csda.2017.05.014
 Author:
 Bilton, Penny; Jones, Geoff; Ganesh, Siva; Haslett, Steve
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 5366
 ISSN:
 01679473
 Subject:
 United Nations; children; household income; malnutrition; models; poverty; prediction; surveys; Nepal
 Abstract:
 ... Poverty mapping uses small area estimation techniques to estimate levels of deprivation (poverty, undernutrition) across small geographic domains within a country. These estimates are then displayed on a poverty map, and used by aid organizations such as the United Nations World Food Programme for the efficient allocation of aid. Current methodology employs unitlevel regression modelling of a tar ...
 DOI:
 10.1016/j.csda.2017.05.009

http://dx.doi.org/10.1016/j.csda.2017.05.009
 Author:
 Cui, Xia; Lu, Ying; Peng, Heng
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 103121
 ISSN:
 01679473
 Subject:
 birth weight; data collection; least squares; linear models; risk factors; statistical inference
 Abstract:
 ... Utilizing recent theoretical results in high dimensional statistical modeling, a flexible yet computationally simple approach is proposed to estimate the partially linear models. Motivated by the partial consistency phenomena, the nonparametric component in the partially linear model is modeled via incidental parameters and estimated by a simple local average over small partitions of the support o ...
 DOI:
 10.1016/j.csda.2017.05.004

http://dx.doi.org/10.1016/j.csda.2017.05.004
 Author:
 Bhuyan, Prajamitra; Sengupta, Debasis
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 172185
 ISSN:
 01679473
 Subject:
 data collection; models
 Abstract:
 ... In many real life scenarios, stress accumulates over time and the system fails as soon as the accumulated stress or degradation equals or exceeds a critical threshold. For some devices, it is possible to obtain measurements of degradation over time, and these measurements may contain useful information about product reliability. In this paper, we propose a semiparametric random effect (frailty) m ...
 DOI:
 10.1016/j.csda.2017.06.008

http://dx.doi.org/10.1016/j.csda.2017.06.008
 Author:
 Li, PaiLing; Chiou, JengMin; Shyr, Yu
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 2134
 ISSN:
 01679473
 Subject:
 covariance; lung neoplasms; mass spectrometry; meat quality; quality control; regression analysis
 Abstract:
 ... We propose a covariateadjusted subspace projection method for classifying functional data, where the covariate effects on the response functions influence the classification outcome. The proposed method is a subspace classifier based on functional projection, and the covariates affect the response function through the mean of a functional regression model. We assume that the response functions in ...
 DOI:
 10.1016/j.csda.2017.05.003

http://dx.doi.org/10.1016/j.csda.2017.05.003
 Author:
 Kuk, Anthony Y.C.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 281293
 ISSN:
 01679473
 Subject:
 environmental monitoring; humans; models; probability; water resources
 Abstract:
 ... To adjust the quantile function estimated using a parametric model, the parametric function is composed with the quantile function of the probability integral transformed data. One round of bandwidth selection suffices as adjustments at all quantile levels can be obtained by smoothing the same set of probability integral transformed data. This is in contrast to the customary additive adjustment wh ...
 DOI:
 10.1016/j.csda.2017.04.012

http://dx.doi.org/10.1016/j.csda.2017.04.012
 Author:
 Hoff, Peter D.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 186198
 ISSN:
 01679473
 Subject:
 algorithms; covariance; linear models
 Abstract:
 ... Using a multiplicative reparametrization, it is shown that a subclass of Lq penalties with q less than or equal to one can be expressed as sums of L2 penalties. It follows that the lasso and other normpenalized regression estimates may be obtained using a very simple and intuitive alternating ridge regression algorithm. As compared to a similarly intuitive EM algorithm for Lq optimization, the pr ...
 DOI:
 10.1016/j.csda.2017.06.007

http://dx.doi.org/10.1016/j.csda.2017.06.007
 Author:
 Ledoit, Olivier; Wolf, Michael
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 199223
 ISSN:
 01679473
 Subject:
 Monte Carlo method; algorithms; mathematical theory; variance covariance matrix
 Abstract:
 ... Certain estimation problems involving the covariance matrix in large dimensions are considered. Due to the breakdown of finitedimensional asymptotic theory when the dimension is not negligible with respect to the sample size, it is necessary to resort to an alternative framework known as largedimensional asymptotics. Recently, an estimator of the eigenvalues of the population covariance matrix h ...
 DOI:
 10.1016/j.csda.2017.06.004

http://dx.doi.org/10.1016/j.csda.2017.06.004
 Author:
 Tsai, Arthur C.; Liou, Michelle; Simak, Maria; Cheng, Philip E.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 250266
 ISSN:
 01679473
 Subject:
 DNA microarrays; analysis of variance; mathematics; variance
 Abstract:
 ... In biological and social sciences, it is essential to consider data transformations to normality for detecting structural effects and for better data representation and interpretation. An array of transformations to normality has been derived for data exhibiting skewed, leptokurtic and unimodal shapes, but is less amenable to data exhibiting platykurtic shapes, such as a nearly bimodal distributio ...
 DOI:
 10.1016/j.csda.2017.06.001

http://dx.doi.org/10.1016/j.csda.2017.06.001
 Author:
 Willems, S.J.W.; Fiocco, M.; Meulman, J.J.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 155171
 ISSN:
 01679473
 Subject:
 educational status; females; males; models; risk
 Abstract:
 ... Medical and psychological studies often involve the collection and analysis of categorical data with nominal or ordinal category levels. Nominal categories have no ordering property, e.g. gender, with the two unordered covariates male and female. Ordinal category levels, however, have an ordering, e.g. when subjects are classified according to their education level, often categorized as low, mediu ...
 DOI:
 10.1016/j.csda.2017.05.008

http://dx.doi.org/10.1016/j.csda.2017.05.008
 Author:
 Li, Song; Tso, Geoffrey K.F.; Long, Lufan
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 1120
 ISSN:
 01679473
 Subject:
 Bayesian theory; Markov chain; algorithms; equipment; researchers; statistical analysis
 Abstract:
 ... Although the Markov Chain Monte Carlo (MCMC) is very popular in parameter inference, the alleviation of the burden of calculation is crucial due to the limit of processors, memory, and disk bottleneck. This is especially true in terms of handling big data. In recent years, researchers have developed a parallel MCMC algorithm, in which full data are partitioned into subdatasets. Samples are drawn f ...
 DOI:
 10.1016/j.csda.2017.05.005

http://dx.doi.org/10.1016/j.csda.2017.05.005
 Author:
 Hayes, Timothy; McArdle, John J.
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 3552
 ISSN:
 01679473
 Subject:
 algorithms; methodology; probability; regression analysis
 Abstract:
 ... Recently, researchers have proposed a variety of new methods for employing exploratory data mining algorithms to address missing data. Two promising classes of missing data methods take advantage of classification and regression trees and random forests. A first method uses the predicted probabilities of response (vs. nonresponse) generated by a CART analysis to create inverse probability weights ...
 DOI:
 10.1016/j.csda.2017.05.006

http://dx.doi.org/10.1016/j.csda.2017.05.006
 Author:
 Wang, Dongliang; Tian, Lili; Zhao, Yichuan
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 110
 ISSN:
 01679473
 Subject:
 biomarkers; equations; graphs; models
 Abstract:
 ... For a continuous scale biomarker of binary disease status, the Youden index is a frequently used measurement of diagnostic accuracy in the context of the receiver operating characteristic curve and provides an optimal threshold for making diagnosis. The majority of existing inference methods for the Youden index are either parametric or bootstrap based. In the current paper, the empirical likeliho ...
 DOI:
 10.1016/j.csda.2017.03.014

http://dx.doi.org/10.1016/j.csda.2017.03.014
 Author:
 Xue, Yuan; Zhang, Nan; Yin, Xiangrong; Zheng, Haitao
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 6778
 ISSN:
 01679473
 Subject:
 algorithms; models
 Abstract:
 ... By using Hilbert–Schmidt Independence Criterion, a sufficient dimension reduction method is proposed to estimate the directions in multipleindex models. A projection pursuit type of sufficient searching algorithm is introduced to reduce the computational complexity, as the original problem involves nonlinear optimization over multidimensional Grassmannmanifold. A bootstrap procedure with additi ...
 DOI:
 10.1016/j.csda.2017.05.002

http://dx.doi.org/10.1016/j.csda.2017.05.002
 Author:
 Frölich, Markus; Huber, Martin; Wiesenfarth, Manuel
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 91102
 ISSN:
 01679473
 Subject:
 entropy; labor market; policy analysis; probability
 Abstract:
 ... The finite sample performance of a comprehensive set of semi and nonparametric estimators for treatment evaluation is investigated. The simulation design is based on Swiss labor market data and considers estimators based on parametric, semiparametric, and nonparametric propensity scores, as well as approaches directly controlling for covariates. Among the methods included are pair, radius, kerne ...
 DOI:
 10.1016/j.csda.2017.05.007

http://dx.doi.org/10.1016/j.csda.2017.05.007
 Author:
 Wang, Shangshan; Xiang, Liming
 Source:
 Computational statistics & data analysis 2017 v.115 pp. 136154
 ISSN:
 01679473
 Subject:
 algorithms; data collection; gene expression; models; regression analysis; system optimization
 Abstract:
 ... We advocate linear regression by modeling the error term through a finite mixture of asymmetric Laplace distributions (ALDs). The model expands the flexibility of linear regression to account for heterogeneity among data and allows us to establish the equivalence between maximum likelihood estimation of the model parameters and the composite quantile regression (CQR) estimation developed by Zou an ...
 DOI:
 10.1016/j.csda.2017.06.002

http://dx.doi.org/10.1016/j.csda.2017.06.002