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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6783
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dc.creatorHutchinson, James M.-
dc.date2004-10-20T14:45:36Z-
dc.date2004-10-20T14:45:36Z-
dc.date1993-12-01-
dc.date.accessioned2013-10-09T02:46:51Z-
dc.date.available2013-10-09T02:46:51Z-
dc.date.issued2013-10-09-
dc.identifierAITR-1457-
dc.identifierhttp://hdl.handle.net/1721.1/6783-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionNonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.-
dc.format160 p.-
dc.format681549 bytes-
dc.format2849290 bytes-
dc.formatapplication/octet-stream-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAITR-1457-
dc.subjectradial basis functions-
dc.subjectoption pricing-
dc.subjectparametersestimation-
dc.subjecttime series prediction-
dc.subjectconfidence-
dc.subjectstock market-
dc.titleA Radial Basis Function Approach to Financial Time Series Analysis-
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