Multiple outliers detection: application to research & development spending and productivity growth

dc.contributor.author	Nurunnabi, A. A. M.
dc.contributor.author	Naser, Mohammed
dc.date.accessioned	2010-10-10T06:48:59Z
dc.date.available	2010-10-10T06:48:59Z
dc.date.issued	2008
dc.identifier.uri	http://hdl.handle.net/10361/421
dc.description.abstract	Multiple outliers are frequently encountered in applied studies in business and economics. Most of the practitioners depend on ordinary least squares (OLS) method for parameter estimation in regression analysis without identifying outliers properly. It is evident that OLS totally fails even in presence of single outlying observation. Single observation outlier detection methods are failed to numerically compare the sensitivity of the most popular diagnostic statistics. Data set from Griliches and Lichtenberg (1984) is used to show that we need to take extra care for model building process in presence of multiple outliers.	en_US
dc.language.iso	en	en_US
dc.publisher	BRAC University	en_US
dc.relation.ispartofseries	BRAC University Journal, BRAC University;Vol.5, No.2,pp. 31-39
dc.subject	Influential observation	en_US
dc.subject	Masking	en_US
dc.subject	Outlier	en_US
dc.subject	Regression diagnostics	en_US
dc.subject	Swamping	en_US
dc.title	Multiple outliers detection: application to research & development spending and productivity growth	en_US
dc.type	Article	en_US