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dc.contributor.authorCai, Zongwu
dc.contributor.authorNaik, Prasad A.
dc.contributor.authorTsai, Chih-Ling
dc.date.accessioned2015-01-23T19:25:53Z
dc.date.available2015-01-23T19:25:53Z
dc.date.issued2000-01-01
dc.identifier.citationCai, Zongwu; Naik, Prasad A.; Tsai, Chih-Ling. (2000). "Denoised least squares estimators: An application to estimating advertising effectiveness." Statistica Sinica, 10(4):1231-1241. http://www3.stat.sinica.edu.tw/statistica/j10n4/j10n412/j10n412.htmen_US
dc.identifier.issn1017-0405
dc.identifier.urihttp://hdl.handle.net/1808/16367
dc.descriptionThis is the publisher's version, also available electronically from http://www3.stat.sinica.edu.tw/statistica/j10n4/j10n412/j10n412.htm.en_US
dc.description.abstractIt is known in marketing science that an advertiser under- or overspends millions of dollars on advertising because the estimation of advertising effectiveness is biased. This bias is induced by measurement noise in advertising variables, such as awareness and television rating points, which are provided by commercial market research firms based on small-sample surveys of consumers. In this paper, we propose a denoised regression approach to deal with the problem of noisy variables. We show that denoised least squares estimators are consistent. Simulation results indicate that the denoised regression approach outperforms the classical regression approach. A marketing example is presented to illustrate the use of denoised least squares estimators.en_US
dc.publisherAcademia Sinica, Institute of Statistical Scienceen_US
dc.relation.isversionofhttp://www3.stat.sinica.edu.tw/statistica/j10n4/j10n412/j10n412.htmen_US
dc.titleDenoised least squares estimators: An application to estimating advertising effectivenessen_US
dc.typeArticle
kusw.kuauthorCai, Zongwu
kusw.kudepartmentEconomicsen_US
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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