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 Author:
 Leiva, Víctor; Saulo, Helton; Leão, Jeremias; Marchant, Carolina
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 175191
 ISSN:
 01679473
 Subject:
 Monte Carlo method; models; stock exchange
 Abstract:
 ... The Birnbaum–Saunders distribution is receiving considerable attention due to its good properties. One of its extensions is the class of scalemixture Birnbaum–Saunders (SBS) distributions, which shares its good properties, but it also has further properties. The autoregressive conditional duration models are the primary family used for analyzing highfrequency financial data. We propose a methodo ...
 DOI:
 10.1016/j.csda.2014.05.016

http://dx.doi.org/10.1016/j.csda.2014.05.016
 Author:
 Wu, Huiping; Yuen, KaVeng; Leung, ShingOn
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 261276
 ISSN:
 01679473
 Subject:
 Bayesian theory; data collection; diagnostic techniques; entropy; models
 Abstract:
 ... Limited information statistics have been recommended as the goodnessoffit measures in sparse 2k contingency tables, but the pvalues of these test statistics are computationally difficult to obtain. A Bayesian model diagnostic tool, Relative Entropy–Posterior Predictive Model Checking (RE–PPMC), is proposed to assess the global fit for latent trait models in this paper. This approach utilizes th ...
 DOI:
 10.1016/j.csda.2014.06.004

http://dx.doi.org/10.1016/j.csda.2014.06.004
 Author:
 Zhang, Junni L.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 277291
 ISSN:
 01679473
 Subject:
 Bayesian theory; models
 Abstract:
 ... Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predicti ...
 DOI:
 10.1016/j.csda.2014.05.012

http://dx.doi.org/10.1016/j.csda.2014.05.012
 Author:
 Lunardon, Nicola; Ronchetti, Elvezio
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 8090
 ISSN:
 01679473
 Subject:
 statistical models; statistics
 Abstract:
 ... The class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute. However, the strength of the composite likelihood approach is dimmed when considering hypothesis testing about a multidimensional parameter because the finite sample ...
 DOI:
 10.1016/j.csda.2014.05.014

http://dx.doi.org/10.1016/j.csda.2014.05.014
 Author:
 Marés, Jordi; Shlomo, Natalie
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 113
 ISSN:
 01679473
 Subject:
 algorithms; data collection; risk
 Abstract:
 ... Dissemination of data with sensitive information has an implicit risk of unauthorized disclosure. Several masking methods have been developed in order to protect the data without the loss of too much information. One such method is the Post Randomization Method (PRAM) based on perturbations of a categorical variable according to a Markov probability transition matrix. The method has the drawback t ...
 DOI:
 10.1016/j.csda.2014.05.002

http://dx.doi.org/10.1016/j.csda.2014.05.002
 Author:
 Gumedze, Freedom N.; Chatora, Tinashe D.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 192202
 ISSN:
 01679473
 Subject:
 binomial distribution; data collection; linear models; variance
 Abstract:
 ... Count data are usually modeled using the Poisson generalized linear model. The Poisson model requires that the variance be a deterministic function of the mean. This assumption may not be met for a particular data set, that is, the model may not adequately capture the variability in the data. The extravariability in the data may be accommodated using overdispersion models, such as the negative bi ...
 DOI:
 10.1016/j.csda.2014.05.018

http://dx.doi.org/10.1016/j.csda.2014.05.018
 Author:
 Niu, Cuizhen; Guo, Xu; Xu, Wangli; Zhu, Lixing
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 91112
 ISSN:
 01679473
 Subject:
 confidence interval; models; regression analysis
 Abstract:
 ... Parameter estimation for nonignorable nonresponse data is a challenging issue as the missing mechanism is unverified in practice and the parameters of response probabilities need to be estimated. This article aims at applying the empirical likelihood to construct the confidence intervals for the parameters of interest in linear regression models with nonignorable missing response data and the noni ...
 DOI:
 10.1016/j.csda.2014.05.005

http://dx.doi.org/10.1016/j.csda.2014.05.005
 Author:
 Costa, D.R.; Lachos, V.H.; Bazan, J.L.; Azevedo, C.L.N.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 248260
 ISSN:
 01679473
 Subject:
 Bayesian theory; Monte Carlo method; algorithms; data collection; diagnostic techniques; education; factor analysis; models; selection criteria
 Abstract:
 ... Tobit confirmatory factor analysis is particularly useful in analysis of multivariate data with censored information. Two methods for estimating multivariate Tobit confirmatory factor analysis models with covariates from a Bayesian and likelihoodbased perspectives are proposed. In contrast with previous likelihoodbased developments that consider Monte Carlo simulations for maximum likelihood est ...
 DOI:
 10.1016/j.csda.2014.05.021

http://dx.doi.org/10.1016/j.csda.2014.05.021
 Author:
 Hirose, Kei; Yamamoto, Michio
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 120132
 ISSN:
 01679473
 Subject:
 Monte Carlo method; algorithms; factor analysis; models
 Abstract:
 ... The problem of sparse estimation via a lassotype penalized likelihood procedure in a factor analysis model is considered. Typically, model estimation assumes that the common factors are orthogonal (i.e., uncorrelated). However, if the common factors are correlated, the lassotype penalization method based on the orthogonal model frequently estimates an erroneous model. To overcome this problem, f ...
 DOI:
 10.1016/j.csda.2014.05.011

http://dx.doi.org/10.1016/j.csda.2014.05.011
 Author:
 Kao, ChiunHow; Nakano, Junji; Shieh, SheauHue; Tien, YinJing; Wu, HanMing; Yang, Chuankai; Chen, Chunhouh
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 1429
 ISSN:
 01679473
 Subject:
 multidimensional scaling; principal component analysis
 Abstract:
 ... Symbolic data analysis (SDA) has gained popularity over the past few years because of its potential for handling data having a dependent and hierarchical nature. Amongst many methods for analyzing symbolic data, exploratory data analysis (EDA: Tukey, 1977) with graphical presentation is an important one. Recent developments of graphical and visualization tools for SDA include zoom star, closed sha ...
 DOI:
 10.1016/j.csda.2014.04.012

http://dx.doi.org/10.1016/j.csda.2014.04.012
 Author:
 Yamamoto, Michio; Terada, Yoshikazu
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 133148
 ISSN:
 01679473
 Subject:
 algorithms
 Abstract:
 ... A new procedure for simultaneously finding the optimal cluster structure of multivariate functional objects and finding the subspace to represent the cluster structure is presented. The method is based on the kmeans criterion for projected functional objects on a subspace in which a cluster structure exists. An efficient alternating leastsquares algorithm is described, and the proposed method is ...
 DOI:
 10.1016/j.csda.2014.05.010

http://dx.doi.org/10.1016/j.csda.2014.05.010
 Author:
 Li, Xinxin; Mo, Lili; Yuan, Xiaoming; Zhang, Jianzhong
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 203221
 ISSN:
 01679473
 Subject:
 data collection; models; multipliers; regression analysis
 Abstract:
 ... The least absolute shrinkage and selection operator (LASSO) has been playing an important role in variable selection and dimensionality reduction for linear regression. In this paper we focus on two general LASSO models: Sparse Group LASSO and Fused LASSO, and apply the linearized alternating direction method of multipliers (LADMM for short) to solve them. The LADMM approach is shown to be a very ...
 DOI:
 10.1016/j.csda.2014.05.017

http://dx.doi.org/10.1016/j.csda.2014.05.017
 Author:
 Ndao, Pathé; Diop, Aliou; Dupuy, JeanFrançois
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 6379
 ISSN:
 01679473
 Subject:
 data collection
 Abstract:
 ... The estimation of the tail index and extreme quantiles of a heavytailed distribution is addressed when some covariate information is available and the data are randomly rightcensored. Several estimators are constructed by combining a movingwindow technique (for tackling the covariate information) and the inverse probabilityofcensoring weighting method. The asymptotic normality of these estima ...
 DOI:
 10.1016/j.csda.2014.05.007

http://dx.doi.org/10.1016/j.csda.2014.05.007
 Author:
 MartínezCamblor, Pablo
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 3043
 ISSN:
 01679473
 Subject:
 algorithms; probability analysis
 Abstract:
 ... Multipletesting problems have received much attention. Different strategies have been considered in order to deal with this problem. The false discovery rate (FDR) is, probably, the most studied criterion. On the other hand, the sequential goodness of fit (SGoF), is a recently proposed approach. Most of the developed procedures are based on the independence among the involved tests; however, in s ...
 DOI:
 10.1016/j.csda.2014.05.006

http://dx.doi.org/10.1016/j.csda.2014.05.006
 Author:
 Gu, Wei; Wu, Hulin; Miao, Hongyu; Xue, Hongqi
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 113119
 ISSN:
 01679473
 Subject:
 computer software; differential equation; prediction; statistical analysis; stochastic processes
 Abstract:
 ... An improved filtering method is provided to estimate the parameter for a type of nonlinear multivariate stochastic differential equations (SDEs) with multiplicative noise, when discrete observations contaminated with measurement error are given. First, a transformation is used to transform the diffusion terms of the SDEs into unit diffusion such that the improved filtering method can be used. Afte ...
 DOI:
 10.1016/j.csda.2014.05.013

http://dx.doi.org/10.1016/j.csda.2014.05.013
 Author:
 Bourel, M.; Fraiman, R.; Ghattas, B.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 149164
 ISSN:
 01679473
 Subject:
 density; estimation; graphs
 Abstract:
 ... A new density estimator called RASH, for Random Average Shifted Histogram, obtained by averaging several histograms as proposed in average shifted histograms, is presented. The principal difference between the two methods is that in RASH each histogram is built over a grid with random shifted breakpoints. The asymptotic behavior of this estimator is established for the onedimensional case and its ...
 DOI:
 10.1016/j.csda.2014.05.004

http://dx.doi.org/10.1016/j.csda.2014.05.004
 Author:
 Xiao, L.Q.; Hou, B.; Wang, Z.F.; Wu, Y.H.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 235247
 ISSN:
 01679473
 Subject:
 econometrics; psychology; regression analysis; sociology
 Abstract:
 ... Longitudinal data arise naturally in medical studies, psychology, sociology and so on. Due to some lower detection limits the responses are often left censored, which are called Tobit responses in econometrics. For Tobit response regression models with longitudinal data, quantile estimators of regression parameters and Mtest statistics for linear hypotheses are constructed. In addition, distribut ...
 DOI:
 10.1016/j.csda.2014.05.020

http://dx.doi.org/10.1016/j.csda.2014.05.020
 Author:
 O’Brien, Travis A.; Collins, William D.; Rauscher, Sara A.; Ringler, Todd D.
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 222234
 ISSN:
 01679473
 Subject:
 data collection; models; probability distribution
 Abstract:
 ... A nonuniform, fast Fourier transform can be used to reduce the computational cost of the empirical characteristic function (ECF) by a factor of 100. This fast ECF calculation method is applied to a new, objective, and robust method for estimating the probability distribution of univariate data, which effectively modulates and filters the ECF of a dataset in a way that yields an optimal estimate of ...
 DOI:
 10.1016/j.csda.2014.06.002

http://dx.doi.org/10.1016/j.csda.2014.06.002
 Author:
 Zhao, Weihua; Zhang, Riquan; Liu, Jicai
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 4462
 ISSN:
 01679473
 Subject:
 data collection; models; regression analysis
 Abstract:
 ... A varying coefficient model with categorical effect modifiers is an effective modeling strategy when the data set includes categorical variables. With categorial predictors the number of parameters can become very large. This paper focuses on the model selection problem for varying coefficient model with categorical effect modifiers under the framework of quantile regression. After distinguishing ...
 DOI:
 10.1016/j.csda.2014.05.003

http://dx.doi.org/10.1016/j.csda.2014.05.003
 Author:
 Lam, K.F.; Wong, KinYau
 Source:
 Computational statistics & data analysis 2014 v.79 pp. 165174
 ISSN:
 01679473
 Subject:
 National Aeronautics and Space Administration; models
 Abstract:
 ... A generalization of the semiparametric Cox’s proportional hazards model by means of a random effect or frailty approach to accommodate clustered survival data with a cure fraction is considered. The frailty serves as a quantification of the health condition of the subjects under study and may depend on some observed covariates like age. One single individualspecific frailty that acts on the hazar ...
 DOI:
 10.1016/j.csda.2014.05.019

http://dx.doi.org/10.1016/j.csda.2014.05.019