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pro vyhledávání: '"Roy E. Welsch"'
Autor:
Roy E. Welsch, Zhe Zhu
Publikováno v:
Ann. Appl. Stat. 12, no. 2 (2018), 1228-1249
A very important problem in finance is the construction of portfolios of assets that balance risk and reward in an optimal way. A critical issue in portfolio development is how to address data outliers that reflect very unusual, generally non-recurri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df2d21326c4d93c2efb5e64c46217e32
https://projecteuclid.org/euclid.aoas/1532743492
https://projecteuclid.org/euclid.aoas/1532743492
Autor:
Roy E. Welsch, Tri-Dzung Nguyen
Publikováno v:
Advances in Data Analysis and Classification. 4:301-334
The outlier detection problem and the robust covariance estimation problem are often interchangeable. Without outliers, the classical method of maximum likelihood estimation (MLE) can be used to estimate parameters of a known distribution from observ
Autor:
Roy E. Welsch
Publikováno v:
Springer Berlin Heidelberg
Agostinelli, Leung, Yohai and Zamar (Agostinelli et al. in the remainder) consider the difficult problem of robust estimation based on high-dimensional data. If outlying values can appear independently in the variables, then it can easily occur that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c798d87da58d04c650d3de0c05a339e
https://lirias.kuleuven.be/handle/123456789/517639
https://lirias.kuleuven.be/handle/123456789/517639
Publikováno v:
Developments in Robust Statistics ISBN: 9783642632419
We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a44e41e47e069873d7c1d4acb21557d
https://doi.org/10.1007/978-3-642-57338-5_20
https://doi.org/10.1007/978-3-642-57338-5_20
Autor:
Roy E. Welsch, William S. Krasker
Publikováno v:
Journal of the American Statistical Association. 77:595-604
The least squares estimator for β in the classical linear regression model is strongly efficient under certain conditions. However, in the presence of heavy-tailed errors and/or anomalous data, the least squares efficiency can be markedly reduced. I
Autor:
Roy E. Welsch
This paper describes the results of a Monte Carlo study of certain aspects of robust regression confidence region estimation for linear models with one, five, and seven parameters. One-step sine estimators (c = l.42) were used with design matrices co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f944eeb80fb94704e317e1ee8f07c1d8
https://doi.org/10.3386/w0111
https://doi.org/10.3386/w0111
Autor:
Roy E. Welsch, A. M. Samarov
Publikováno v:
COMPSTAT 1982 5th Symposium held at Toulouse 1982 ISBN: 9783705100022
The ordinary least-squares (OLS) estimator of the coefficients in the classical linear regression model is efficient when the appropriate assumptions are valid. However, even small departures from the standard assumptions on the model may seriously d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4426aa9a074aee46a20feddaf32ba250
https://doi.org/10.1007/978-3-642-51461-6_63
https://doi.org/10.1007/978-3-642-51461-6_63
Autor:
William S. Krasker, Roy E. Welsch
Publikováno v:
Econometrica. 53:1475
IN THIS PAPER we present a weighted-instrumental-variables estimator that is resistant2 to heavy-tailed errors, aberrant data in either the endogenous or exogenous variables, and certain other model failures. The estimator is analogous to the weighte