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Statistical classification procedures for analyisng functional data
Thesis (MCom)--Stellenbosch University, 2016.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2016
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| _version_ | 1867613782464790528 |
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| access_status_str | Open Access |
| author | Orsmond, Chane |
| author2 | Steel, Sarel J. |
| author_browse | Orsmond, Chane Steel, Sarel J. |
| author_facet | Steel, Sarel J. Orsmond, Chane |
| author_sort | Orsmond, Chane |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2016. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/100163 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:41:36.774Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/100163 Statistical classification procedures for analyisng functional data Orsmond, Chane Steel, Sarel J. Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics & Actuarial Science. Mathematical statistics -- Data processing Spectrum analysis -- Data processing Functional support vector machines Fused lasso Sparse partial least squares UCTD Thesis (MCom)--Stellenbosch University, 2016. ENGLISH SUMMARY : Functional data are obtained through the measurement of one or more variables at a set of discrete evaluation points over a continuum such as time, wavelength or values of a spatial variable. Functional extensions of traditional statistical methods are considered in the analyses of such data sets, which are typically comprised of a sample of functions. Linear discriminant analysis for functional data and functional support vector machines are investigated in this thesis as binary functional classification procedures. To address the high correlations which typically exist amongst the input features of a functional data set, the fused lasso, which selects contiguous intervals of variables, is discussed. In addition, a sparse equivalent of partial least squares (SPLS), which achieves simultaneous variable selection and dimension reduction, is considered in a functional context. An infrared spectroscopy data set is considered for practical implementation of the fore mentioned functional data analysis techniques. The procedures are compared in terms of classification accuracy and variable selection properties, reported in the results of an empirical study. AFRIKAANSE OPSOMMING : Geen opsomming beskikbaar. Masters 2016-12-22T13:22:14Z 2016-12-22T13:22:14Z 2016-12 Thesis http://hdl.handle.net/10019.1/100163 en_ZA Stellenbosch University vi, 107 pages ; pages ; illustrations, includes annexures application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Mathematical statistics -- Data processing Spectrum analysis -- Data processing Functional support vector machines Fused lasso Sparse partial least squares UCTD Orsmond, Chane Statistical classification procedures for analyisng functional data |
| title | Statistical classification procedures for analyisng functional data |
| title_full | Statistical classification procedures for analyisng functional data |
| title_fullStr | Statistical classification procedures for analyisng functional data |
| title_full_unstemmed | Statistical classification procedures for analyisng functional data |
| title_short | Statistical classification procedures for analyisng functional data |
| title_sort | statistical classification procedures for analyisng functional data |
| topic | Mathematical statistics -- Data processing Spectrum analysis -- Data processing Functional support vector machines Fused lasso Sparse partial least squares UCTD |
| url | http://hdl.handle.net/10019.1/100163 |
| work_keys_str_mv | AT orsmondchane statisticalclassificationproceduresforanalyisngfunctionaldata |