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 Author:
 Lin, Lu; Sun, Jing
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 287301
 ISSN:
 01679473
 Subject:
 models; screening
 Abstract:
 ... When the correlation among the predictors is relatively strong and/or the model structures cannot be specified, the construction of adaptive feature screening remains a challenging issue. A general technique of conditional feature screening is proposed via combining a modelfree feature screening with a predetermined set of predictors. The proposed centralization technique can remove the irrelevan ...
 DOI:
 10.1016/j.csda.2015.09.002

http://dx.doi.org/10.1016/j.csda.2015.09.002
 Author:
 Lavancier, F.; Rochet, P.
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 175192
 ISSN:
 01679473
 Subject:
 data collection; models
 Abstract:
 ... A general method to combine several estimators of the same quantity is investigated. In the spirit of model and forecast averaging, the final estimator is computed as a weighted average of the initial ones, where the weights are constrained to sum to one. In this framework, the optimal weights, minimizing the quadratic loss, are entirely determined by the mean squared error matrix of the vector of ...
 DOI:
 10.1016/j.csda.2015.08.001

http://dx.doi.org/10.1016/j.csda.2015.08.001
 Author:
 Hasegawa, Takanori; Niida, Atsushi; Mori, Tomoya; Shimamura, Teppei; Yamaguchi, Rui; Miyano, Satoru; Akutsu, Tatsuya; Imoto, Seiya
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 6374
 ISSN:
 01679473
 Subject:
 Bayesian theory; circadian rhythm; differential equation; genomics; probability; rats; regulatory sequences; simulation models
 Abstract:
 ... For the evaluation of the dynamic behavior of biological processes, e.g., gene regulatory sequences, we typically utilize nonlinear differential equations within a state space model in the context of genomic data assimilation. For the estimation of the parameter values for such systems, the particle filter can be a strong approach in terms of obtaining their theoretically exact posterior distribut ...
 DOI:
 10.1016/j.csda.2015.08.003

http://dx.doi.org/10.1016/j.csda.2015.08.003
 Author:
 Lee, Sangin; Kwon, Sunghoon; Kim, Yongdai
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 275286
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... In this paper, we propose an optimization algorithm called the modified local quadratic approximation algorithm for minimizing various ℓ1penalized convex loss functions. The proposed algorithm iteratively solves ℓ1penalized local quadratic approximations of the loss function, and then modifies the solution whenever it fails to decrease the original ℓ1penalized loss function. As an extension, we ...
 DOI:
 10.1016/j.csda.2015.08.019

http://dx.doi.org/10.1016/j.csda.2015.08.019
 Author:
 Lee, Keunbaik; Sohn, Insuk; Kim, Donguk
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 363371
 ISSN:
 01679473
 Subject:
 Monte Carlo method; algorithms; clinical trials; lung neoplasms; models; quality of life
 Abstract:
 ... Marginalized models (Heagerty, 1999, 2002) are often used for short longitudinal series when population averaged effects are of interest. Lee and Daniels (2007, 2008) proposed marginalized models for the analysis of longitudinal ordinal data to permit likelihoodbased estimation of marginal mean parameters. In this paper, we extend their work to accommodate the response dependence that we have see ...
 DOI:
 10.1016/j.csda.2015.07.010

http://dx.doi.org/10.1016/j.csda.2015.07.010
 Author:
 Jiang, Depeng; Zhao, Puying; Tang, Niansheng
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 98119
 ISSN:
 01679473
 Subject:
 regression analysis
 Abstract:
 ... In a linear regression model with nonignorable missing covariates, nonnormal errors or outliers can lead to badly biased and misleading results with standard parameter estimation methods built on either least squares or likelihoodbased methods. A propensity score method with a robust and efficient regression procedure called composite quantile regression for parameter estimation of the linear r ...
 DOI:
 10.1016/j.csda.2015.07.017

http://dx.doi.org/10.1016/j.csda.2015.07.017
 Author:
 Martin, Ryan; Han, Zhen
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 7585
 ISSN:
 01679473
 Subject:
 algorithms; models; probability; regression analysis
 Abstract:
 ... To avoid specification of a particular distribution for the error in a regression model, we propose a flexible scale mixture model with a nonparametric mixing distribution. This model contains, among other things, the familiar normal and Studentt models as special cases. For fitting such mixtures, the predictive recursion method is a simple and computationally efficient alternative to existing me ...
 DOI:
 10.1016/j.csda.2015.08.005

http://dx.doi.org/10.1016/j.csda.2015.08.005
 Author:
 Zhang, Xin; Jeske, Daniel R.; Li, Jun; Wong, Vance
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 238249
 ISSN:
 01679473
 Subject:
 kidneys; patients; regression analysis
 Abstract:
 ... Making an early classification in longitudinal data is highly desirable. For this purpose, a sequential classifier that incorporates a neutral zone framework is proposed. The classification procedure evaluates each subject sequentially at each longitudinal time point. If there is not adequate confidence in making a classification at a given time point, the decision will wait until the next time po ...
 DOI:
 10.1016/j.csda.2015.08.009

http://dx.doi.org/10.1016/j.csda.2015.08.009
 Author:
 GardnerLubbe, Sugnet
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 2032
 ISSN:
 01679473
 Subject:
 discriminant analysis; multivariate analysis; support vector machines; variance covariance matrix
 Abstract:
 ... Quadratic discriminant analysis is used when the assumption of equal covariance matrices for linear discrimination does not hold. The Canonical Variate Analysis biplot is used for graphical visualisation to accompany linear discriminant analysis. However, since class specific covariance matrix estimates are needed for quadratic discrimination the canonical transformation cannot be used. An alterna ...
 DOI:
 10.1016/j.csda.2015.07.014

http://dx.doi.org/10.1016/j.csda.2015.07.014
 Author:
 Aykroyd, Robert G.; Barber, Stuart; Miller, Luke R.
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 351362
 ISSN:
 01679473
 Subject:
 models; motivation; process monitoring; regression analysis; time series analysis; tomography; wavelet
 Abstract:
 ... A general framework for regression modeling using localized frequency characteristics of explanatory variables is proposed. This novel framework can be used in any application where the aim is to model an evolving process sequentially based on multiple time series data. Furthermore, this framework allows time series to be transformed and combined to simultaneously boost important characteristics a ...
 DOI:
 10.1016/j.csda.2015.07.009

http://dx.doi.org/10.1016/j.csda.2015.07.009
 Author:
 Ng, F.C.; Li, W.K.; Yu, Philip L.H.
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 8697
 ISSN:
 01679473
 Subject:
 autocorrelation; finance; models; prices; rain; social sciences; time series analysis
 Abstract:
 ... In many situations, we may encounter time series that are nonnegative. Examples include trading duration, volume transaction and price volatility in finance, waiting time in a queue in social sciences, and daily/hourly rainfall in natural sciences. The vector multiplicative error model (VMEM) is a natural choice for modeling such time series in a multivariate framework. Despite the popularity and ...
 DOI:
 10.1016/j.csda.2015.07.012

http://dx.doi.org/10.1016/j.csda.2015.07.012
 Author:
 Tang, Qingguo; Karunamuni, Rohana J.
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 4962
 ISSN:
 01679473
 Subject:
 algorithms; computational methodology; data collection
 Abstract:
 ... Standard kernel density and regression estimators are wellknown to be computationally very slow when analyzing large data sets, and algorithms that achieve considerable computational savings are highly desirable. With this goal in mind, two fast and accurate computational methods are proposed in this paper for computation of univariate and multivariate local polynomial estimators defined on an eq ...
 DOI:
 10.1016/j.csda.2015.07.015

http://dx.doi.org/10.1016/j.csda.2015.07.015
 Author:
 Uno, Kohei; Satomura, Hironori; Adachi, Kohei
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 265274
 ISSN:
 01679473
 Subject:
 algorithms; factor analysis; models
 Abstract:
 ... In the fixed factor model for factor analysis (FA), common factor scores are treated as fixed parameters. However, they cannot be estimated jointly with the other parameters, since the maximum likelihood (ML) for the model diverges to infinity. In order to avoid the divergence so that all parameters can be jointly estimated, we propose a constrained fixed factor model. Here, observations are class ...
 DOI:
 10.1016/j.csda.2015.08.010

http://dx.doi.org/10.1016/j.csda.2015.08.010
 Author:
 Marino, Maria Francesca; Alfó, Marco
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 193209
 ISSN:
 01679473
 Subject:
 Markov chain
 Abstract:
 ... Mixed hidden Markov models represent an interesting tool for the analysis of longitudinal data. They allow to account for both timeconstant and timevarying sources of unobserved heterogeneity, which are frequent in this kind of studies. Individualspecific latent features, which may be either constant or varying over time, are included in the linear predictor and lead to a general form of depend ...
 DOI:
 10.1016/j.csda.2015.07.016

http://dx.doi.org/10.1016/j.csda.2015.07.016
 Author:
 Usset, Joseph; Staicu, AnaMaria; Maity, Arnab
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 317329
 ISSN:
 01679473
 Subject:
 computer software; linear models; prediction; regression analysis
 Abstract:
 ... A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates twoway interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or w ...
 DOI:
 10.1016/j.csda.2015.08.020

http://dx.doi.org/10.1016/j.csda.2015.08.020
 Author:
 Zang, Yangguang; Zhang, Sanguo; Li, Qizhai; Zhang, Qingzhao
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 302316
 ISSN:
 01679473
 Subject:
 chisquare distribution; chromosomes; regression analysis; rheumatoid arthritis; single nucleotide polymorphism; ttest
 Abstract:
 ... A novel way to test coefficients in highdimensional linear regression model is presented. Under the ‘large p small n’ situation, the traditional methods, like Ftest and ttest, are unsuitable or undefined. The proposed jackknife empirical likelihood test has an asymptotic chisquare distribution and the conditions are much weaker than those in the existing methods. Moreover, an extension of the ...
 DOI:
 10.1016/j.csda.2015.08.012

http://dx.doi.org/10.1016/j.csda.2015.08.012
 Author:
 Svenson, Joshua; Santner, Thomas
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 250264
 ISSN:
 01679473
 Subject:
 algorithms; computers; covariance; models; production functions
 Abstract:
 ... Many engineering design optimization problems contain multiple objective functions all of which are desired to be minimized, say. This paper proposes a method for identifying the Pareto Front and the Pareto Set of the objective functions when these functions are evaluated by expensivetoevaluate deterministic computer simulators. The method replaces the expensive function evaluations by a rapidly ...
 DOI:
 10.1016/j.csda.2015.08.011

http://dx.doi.org/10.1016/j.csda.2015.08.011
 Author:
 Benavent, Roberto; Morales, Domingo
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 372390
 ISSN:
 01679473
 Subject:
 computer software; models; poverty; prediction; statistical analysis; surveys
 Abstract:
 ... Multivariate Fay–Herriot models for estimating small area indicators are introduced. Among the available procedures for fitting linear mixed models, the residual maximum likelihood (REML) is employed. The empirical best predictor (EBLUP) of the vector of area means is derived. An approximation to the matrix of mean squared crossed prediction errors (MSE) is given and four MSE estimators are propos ...
 DOI:
 10.1016/j.csda.2015.07.013

http://dx.doi.org/10.1016/j.csda.2015.07.013
 Author:
 Kim, Minjo; Lee, Sangyeol
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 119
 ISSN:
 01679473
 Subject:
 heteroskedasticity; nonlinear models; regression analysis
 Abstract:
 ... This paper considers nonlinear expectile regression models to estimate conditional expected shortfall (ES) and ValueatRisk (VaR). In the literature, the asymmetric least squares (ALS) regression method has been widely used to estimate expectile regression models. However, no literatures rigorously investigated the asymptotic properties of the ALS estimates in nonlinear models with heteroscedasti ...
 DOI:
 10.1016/j.csda.2015.07.011

http://dx.doi.org/10.1016/j.csda.2015.07.011
 Author:
 Chesneau, Christophe; Dewan, Isha; Doosti, Hassan
 Source:
 Computational statistics & data analysis 2016 v.94 pp. 161174
 ISSN:
 01679473
 Subject:
 risk; wavelet
 Abstract:
 ... In this paper nonparametric wavelet estimators of the quantile density function are proposed. Consistency of the wavelet estimators is established under the Lp risk. A simulation study illustrates the good performance of our estimators. ...
 DOI:
 10.1016/j.csda.2015.08.006

http://dx.doi.org/10.1016/j.csda.2015.08.006