Nonparametric regression

dc.contributor.author	Swanepoel, C.J.	en
dc.date.accessioned	2015-01-31T16:53:48Z
dc.date.available	2015-01-31T16:53:48Z
dc.date.issued	2015-01-31
dc.identifier.uri	http://hdl.handle.net/11400/5252
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Smoothing (Statistics)
dc.subject	Απαραμετρικές παλινδρόμησης
dc.subject	Nonparametric regression
dc.subject	Εξομάλυνση
dc.title	Nonparametric regression	en
heal.type	conferenceItem
heal.secondaryTitle	a brief overview and developments	en
heal.generalDescription	ISSN Σειράς Πρακτικών: 1791 – 8499	el
heal.generalDescription	Proceedings Series: 1791 - 8499	en
heal.classification	Statistics
heal.classification	Social sciences--Statistical methods
heal.classification	Στατιστική
heal.classification	Κοινωνικές επιστήμες Στατιστικές μέθοδοι
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh99001414
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85124018
heal.classificationURI	N/A-Στατιστική
heal.classificationURI	N/A-Κοινωνικές επιστήμες Στατιστικές μέθοδοι
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85123709
heal.contributorName	Φράγκος, Χρήστος Κ. (1949-) (επιμ.)	el
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Διοίκησης και Οικονομίας. Τμήμα Διοίκησης Επιχειρήσεων. Κατεύθυνση Διοίκησης Επιχειρήσεων.	el
heal.publicationDate	2009-05
heal.bibliographicCitation	Swanepoel, C.J. (2009). Nonparametric regression: a brief overview and developments. In Proceedings of the 2nd International Conference: Quantitative and Qualitative Methodologies in the Economic and Administrative Sciences. Athens, 25th to 27th May 2009. Athens: TEI of Athens. pp. 445-463.	en
heal.abstract	A regression curve describes a general relationship between two or more quantitative variables. In a multivariate situation vectors of explanatory variables as well as response variables may be present. For the simple case of one explanatory variable and one response variable, n data points S : = [(X1, Y1), i = 1, 2, …., n] are collected. The regression relationship can be modeled by Y1, = m(X1)+el where m(x)=E(Y\X=x) is the unknown regression function and the εi's are independent random errors with mean 0 and unknown variance σ2. Nonparametric methods (i.e. smoothing methods) to obtain consistent estimators m(x) of m(x) are revised. Nonparametric methods relax on traditional assumptions and usually only assumes that m belongs to an infinite¬ dimensional collection of smooth functions. Several nonparametric estimators are discussed, mostly of a weighted average form. Several kernel and nearest neighbour approaches to the weight functions are considered. Each of these estimators depends on a smoothing parameter and the critical issue of estimating it is discussed briefly. The performance of m(x) is assessed via methods involving the mean squared error (MSE) and the mean integrated squared error (MISE). Two methods of improving the performance of m(x) are "boosting" and ”bagging", which are respectively an iterative computer intensive method, and an averaging method involving the generation of bootstrap samples. These methods are briefly introduced.	en
heal.publisher	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας	el
heal.publisher	TEI of Athens	en
heal.fullTextAvailability	true
heal.conferenceName	2nd International Conference: Quantitative and Qualitative Methodologies in the Economic and Administrative Sciences	en
heal.conferenceItemType	full paper