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 Author:
 CondeAmboage, Mercedes; SánchezSellero, César; GonzálezManteiga, Wenceslao
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 128138
 ISSN:
 01679473
 Subject:
 economic development; heteroskedasticity; regression analysis
 Abstract:
 ... A new lackoffit test for quantile regression models, that is suitable even with highdimensional covariates, is proposed. The test is based on the cumulative sum of residuals with respect to unidimensional linear projections of the covariates. To approximate the critical values of the test, a wild bootstrap mechanism convenient for quantile regression is used. An extensive simulation study was u ...
 DOI:
 10.1016/j.csda.2015.02.016

http://dx.doi.org/10.1016/j.csda.2015.02.016
 Author:
 Ling, Bingo WingKuen; Ho, Charlotte YukFan; Siu, WanChi; Dai, Qingyun
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 111118
 ISSN:
 01679473
 Subject:
 computers; least squares; mathematical models
 Abstract:
 ... When the exact unbiasedness condition is relaxed to a near unbiasedness condition, this short communication shows that the best linear near unbiased estimation problem is actually a semiinfinite programming problem. Our recently developed dual parameterization method is applied for solving the problem. Computer numerical simulation results show that the semiinfinite programming approach outperfo ...
 DOI:
 10.1016/j.csda.2015.01.020

http://dx.doi.org/10.1016/j.csda.2015.01.020
 Author:
 Stöber, Jakob; Hong, Hyokyoung Grace; Czado, Claudia; Ghosh, Pulak
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 2839
 ISSN:
 01679473
 Subject:
 chronic diseases; comorbidity; data collection; elderly; longitudinal studies; model validation; models; patients; probability; statistical analysis
 Abstract:
 ... Joint modeling of multiple health related random variables is essential to develop an understanding for the public health consequences of an aging population. This is particularly true for patients suffering from multiple chronic diseases. The contribution is to introduce a novel model for multivariate data where some response variables are discrete and some are continuous. It is based on pair cop ...
 DOI:
 10.1016/j.csda.2015.02.001

http://dx.doi.org/10.1016/j.csda.2015.02.001
 Author:
 Lin, Yanzhu; Zhang, Min; Zhang, Dabao
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 119127
 ISSN:
 01679473
 Subject:
 algorithms; least squares; linear models
 Abstract:
 ... The algorithm, generalized orthogonal components regression (GOCRE), is proposed to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the ...
 DOI:
 10.1016/j.csda.2015.02.006

http://dx.doi.org/10.1016/j.csda.2015.02.006
 Author:
 Rajesh, G.; AbdulSathar, E.I.; Maya, R.
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 114
 ISSN:
 01679473
 Subject:
 Monte Carlo method; data collection; entropy
 Abstract:
 ... Local linear estimators for the conditional residual entropy function in the case of complete and censored samples are proposed. The resulting estimators are shown to be consistent and asymptotically normally distributed under certain regularity conditions. The performance of the estimator is compared by using a real data set and simulation studies are carried out by using the MonteCarlo method. ...
 DOI:
 10.1016/j.csda.2015.02.002

http://dx.doi.org/10.1016/j.csda.2015.02.002
 Author:
 Zhao, Jianhua; Jin, Libin; Shi, Lei
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 139153
 ISSN:
 01679473
 Subject:
 Bayesian theory; data collection; empirical research; models
 Abstract:
 ... The Bayesian information criterion (BIC) is one of the most popular criteria for model selection in finite mixture models. However, it implausibly penalizes the complexity of each component using the whole sample size and completely ignores the clustered structure inherent in the data, resulting in overpenalization. To overcome this problem, a novel criterion called hierarchical BIC (HBIC) is pro ...
 DOI:
 10.1016/j.csda.2015.01.019

http://dx.doi.org/10.1016/j.csda.2015.01.019
 Author:
 Ankinakatte, Smitha; Edwards, David
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 4052
 ISSN:
 01679473
 Subject:
 algorithms; data collection; genomics; models; molecular genetics; social sciences
 Abstract:
 ... Acyclic probabilistic finite automata (APFA) constitute a rich family of models for discrete longitudinal data. An APFA may be represented as a directed multigraph, and embodies a set of contextspecific conditional independence relations that may be read off the graph. A model selection algorithm to minimize a penalized likelihood criterion such as AIC or BIC is described. This algorithm is compa ...
 DOI:
 10.1016/j.csda.2015.02.009

http://dx.doi.org/10.1016/j.csda.2015.02.009
 Author:
 Chan, Moontong; Yu, Dalei; Yau, Kelvin K.W.
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 173186
 ISSN:
 01679473
 Subject:
 attitudes and opinions; data collection; models; regression analysis; surveys; variance
 Abstract:
 ... A multilevel model for ordinal data in generalized linear mixed models (GLMM) framework is developed to account for the inherent dependencies among observations within clusters. Motivated by a data set from the British Social Attitudes Panel Survey (BSAPS), the random district effects and respondent effects are incorporated into the linear predictor to accommodate the nested clusterings. The fixed ...
 DOI:
 10.1016/j.csda.2015.02.018

http://dx.doi.org/10.1016/j.csda.2015.02.018
 Author:
 Zhang, Lingsong; Lu, Shu; Marron, J.S.
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 100110
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... Motivated by the analysis of nonnegative data objects, a novel Nested Nonnegative Cone Analysis (NNCA) approach is proposed to overcome some drawbacks of existing methods. The application of traditional PCA/SVD method to nonnegative data often cause the approximation matrix leave the nonnegative cone, which leads to noninterpretable and sometimes nonsensical results. The nonnegative matrix factor ...
 DOI:
 10.1016/j.csda.2015.01.008

http://dx.doi.org/10.1016/j.csda.2015.01.008
 Author:
 Paul, Subhadeep; Basu, Ayanendranath
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 187207
 ISSN:
 01679473
 Subject:
 statistics
 Abstract:
 ... General strategies for constructing second order efficient robust distances from suitable properties of the residual adjustment functions (RAF) are discussed. Based on those properties families of estimators are constructed using the truncated polynomial, negative exponential and sigmoidal functions as RAFs and their efficiency and robustness properties are investigated. The estimators have full a ...
 DOI:
 10.1016/j.csda.2015.02.008

http://dx.doi.org/10.1016/j.csda.2015.02.008
 Author:
 Xie, Shangyu; Wan, Alan T.K.; Zhou, Yong
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 154172
 ISSN:
 01679473
 Subject:
 algorithms; models; probability; regression analysis; variance
 Abstract:
 ... Considerable intellectual progress has been made to the development of various semiparametric varyingcoefficient models over the past ten to fifteen years. An important advantage of these models is that they avoid much of the curse of dimensionality problem as the nonparametric functions are restricted only to some variables. More recently, varyingcoefficient methods have been applied to quantil ...
 DOI:
 10.1016/j.csda.2015.02.011

http://dx.doi.org/10.1016/j.csda.2015.02.011
 Author:
 Mahani, Alireza S.; Sharabiani, Mansour T.A.
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 7599
 ISSN:
 01679473
 Subject:
 Bayesian theory; Markov chain; algorithms; artificial intelligence; cost effectiveness; data analysis; energy efficiency; graphs; linear models
 Abstract:
 ... Computational intensity and sequential nature of estimation techniques for Bayesian methods in statistics and machine learning, combined with their increasing applications for big data analytics, necessitate both the identification of potential opportunities to parallelize techniques such as Monte Carlo Markov Chain (MCMC) sampling, and the development of general strategies for mapping such parall ...
 DOI:
 10.1016/j.csda.2015.02.010

http://dx.doi.org/10.1016/j.csda.2015.02.010
 Author:
 Yu, Wenbao; Park, Taesung
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 1527
 ISSN:
 01679473
 Subject:
 algorithms; biomarkers; data collection; diagnostic techniques; graphs; regression analysis
 Abstract:
 ... In clinical practices, it is common that several biomakers are related to a specific disease and each single marker does not have enough diagnostic power. An effective way to improve the diagnostic accuracy is to combine multiple markers. It is known that the area under the receiver operating characteristic curve (AUC) is very popular for evaluation of a diagnostic tool. Su and Liu (1993) derived ...
 DOI:
 10.1016/j.csda.2014.12.002

http://dx.doi.org/10.1016/j.csda.2014.12.002
 Author:
 Fu, Wei; Simonoff, Jeffrey S.
 Source:
 Computational statistics & data analysis 2015 v.88 pp. 5374
 ISSN:
 01679473
 Subject:
 algorithms; linear models; prediction; regression analysis
 Abstract:
 ... A new version of the RE–EM regression tree method for longitudinal and clustered data is presented. The RE–EM tree is a methodology that combines the structure of mixed effects models for longitudinal and clustered data with the flexibility of treebased estimation methods. The RE–EM tree is less sensitive to parametric assumptions and provides improved predictive power compared to linear models w ...
 DOI:
 10.1016/j.csda.2015.02.004

http://dx.doi.org/10.1016/j.csda.2015.02.004