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Computational statistics & data analysis
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2013
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2013 v.65
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- Author:
- Wang, B.; Wertelecki, W.
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 4-12
- ISSN:
- 0167-9473
- Subject:
- statistical analysis; Ukraine
- Abstract:
- ... Rounding of data is common in practice. The problem of estimating the underlying density function based on data with rounding errors is addressed. A parametric maximum likelihood estimator and a nonparametric bootstrap kernel density estimator are proposed. Simulations indicate that the maximum likelihood approach performs well when prior information on the functional form of the underlying distri ...
- DOI:
- 10.1016/j.csda.2012.02.016
- http://dx.doi.org/10.1016/j.csda.2012.02.016

- Author:
- Hubert, Mia; Dierckx, Goedele; Vanpaemel, Dina
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 13-28
- ISSN:
- 0167-9473
- Subject:
- data analysis; equations; estimation
- Abstract:
- ... Pareto-type distributions are extreme value distributions for which the extreme value index γ>0. Classical estimators for γ>0, like the Hill estimator, tend to overestimate this parameter in the presence of outliers. The empirical influence function plot, which displays the influence that each data point has on the Hill estimator, is introduced. To avoid a masking effect, the empirical influence f ...
- DOI:
- 10.1016/j.csda.2012.07.011
- http://dx.doi.org/10.1016/j.csda.2012.07.011

- Author:
- Cerioli, Andrea; Farcomeni, Alessio; Riani, Marco
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 29-45
- ISSN:
- 0167-9473
- Subject:
- ingredients
- Abstract:
- ... Robust distances are mainly used for the purpose of detecting multivariate outliers. The precise definition of cut-off values for formal outlier testing assumes that the “good” part of the data comes from a multivariate normal population. Robust distances also provide valuable information on the units not declared to be outliers and, under mild regularity conditions, they can be used to test the p ...
- DOI:
- 10.1016/j.csda.2012.03.008
- http://dx.doi.org/10.1016/j.csda.2012.03.008

- Author:
- Muler, Nora; Yohai, V´ictor J.
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 68-79
- ISSN:
- 0167-9473
- Subject:
- Monte Carlo method; models; normal distribution
- Abstract:
- ... A new class of robust estimators for VAR models is introduced. These estimators are an extension to the multivariate case of the MM-estimators based on a bounded innovation propagation AR model. They have a filtering mechanism that avoids the propagation of the effect of one outlier to the residuals of the subsequent periods. Besides, they are consistent and have the same asymptotic normal distrib ...
- DOI:
- 10.1016/j.csda.2012.02.011
- http://dx.doi.org/10.1016/j.csda.2012.02.011

- Author:
- Maronna, Ricardo A.; Yohai, Victor J.
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 46-55
- ISSN:
- 0167-9473
- Subject:
- algorithms; regression analysis
- Abstract:
- ... Many existing methods for functional regression are based on the minimization of an L2 norm of the residuals and are therefore sensitive to atypical observations, which may affect the predictive power and/or the smoothness of the resulting estimate. A robust version of a spline-based estimate is presented, which has the form of an MM estimate, where the L2 loss is replaced by a bounded loss functi ...
- DOI:
- 10.1016/j.csda.2011.11.014
- http://dx.doi.org/10.1016/j.csda.2011.11.014

- Author:
- Lind, John C.; Wiens, Douglas P.; Yohai, Victor J.
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 98-112
- ISSN:
- 0167-9473
- Subject:
- algorithms; case studies; covariance; electroencephalography; motivation
- Abstract:
- ... Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample–typically of covariance or cross-spectrum matrices–with the subsample chosen to minimize a function of its average. In one case this function is the Kullback–Leibler discrimination information loss incurred when the subsample is summarized by its average; in t ...
- DOI:
- 10.1016/j.csda.2012.06.011
- http://dx.doi.org/10.1016/j.csda.2012.06.011

- Author:
- Bianco, Ana M.; Boente, Graciela; Rodrigues, Isabel M.
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 80-97
- ISSN:
- 0167-9473
- Subject:
- data collection; linear models; statistics
- Abstract:
- ... In many situations, data follow a generalized linear model in which the mean of the responses is modelled, through a link function, linearly on the covariates. Robust estimators for the regression parameter in order to build test statistics for this parameter, when missing data occur in the responses, are considered. The asymptotic behaviour of the robust estimators for the regression parameter is ...
- DOI:
- 10.1016/j.csda.2012.05.008
- http://dx.doi.org/10.1016/j.csda.2012.05.008

- Author:
- Guo, Jie; Tang, Manlai; Tian, Maozai; Zhu, Kai
- Source:
- Computational statistics & data analysis 2013 v.65 pp. 56-67
- ISSN:
- 0167-9473
- Subject:
- linear models; regression analysis
- Abstract:
- ... A new estimation procedure based on the composite quantile regression is proposed for the semiparametric additive partial linear models, of which the nonparametric components are approximated by polynomial splines. The proposed estimation method can simultaneously estimate both the parametric regression coefficients and nonparametric components without any specification of the error distributions. ...
- DOI:
- 10.1016/j.csda.2013.03.017
- http://dx.doi.org/10.1016/j.csda.2013.03.017