Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis
The non-parametric smoothing of the location model proposed by Asparoukhov and Krzanowski (2000) for allocating objects with mixtures of variables into two groups is studied. The strategy for selecting the smoothing parameter through the maximisation of the pseudo-likelihood function is reviewed. Pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Canadian Center of Science and Education
2009
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| Online Access: | https://repo.uum.edu.my/id/eprint/4610/1/Str.pdf |
| _version_ | 1825740348597469184 |
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| author | Mahat, Nor Idayu Krzanowski, W.J. Hernandez, A. |
| author_facet | Mahat, Nor Idayu Krzanowski, W.J. Hernandez, A. |
| author_sort | Mahat, Nor Idayu |
| collection | UUM |
| description | The non-parametric smoothing of the location model proposed by Asparoukhov and Krzanowski (2000) for allocating objects with mixtures of variables into two groups is studied. The strategy for selecting the smoothing parameter through the maximisation of the pseudo-likelihood function is reviewed. Problems with previous methods are highlighted, and two alternative strategies are proposed. Some investigations into other possible smoothing procedures for estimating cell probabilities are discussed. A leave-one-out method is proposed for constructing the allocation rule and evaluating its performance by estimating the true error rate. Results of a numerical study on simulated data highlight the feasibility of the proposed allocation rule as well as its advantages over previous methods, and an example using real data is presented. |
| first_indexed | 2024-07-04T05:25:32Z |
| format | Article |
| id | uum-4610 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:25:32Z |
| publishDate | 2009 |
| publisher | Canadian Center of Science and Education |
| record_format | eprints |
| spelling | uum-46102012-02-25T03:06:55Z https://repo.uum.edu.my/id/eprint/4610/ Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis Mahat, Nor Idayu Krzanowski, W.J. Hernandez, A. QA76 Computer software The non-parametric smoothing of the location model proposed by Asparoukhov and Krzanowski (2000) for allocating objects with mixtures of variables into two groups is studied. The strategy for selecting the smoothing parameter through the maximisation of the pseudo-likelihood function is reviewed. Problems with previous methods are highlighted, and two alternative strategies are proposed. Some investigations into other possible smoothing procedures for estimating cell probabilities are discussed. A leave-one-out method is proposed for constructing the allocation rule and evaluating its performance by estimating the true error rate. Results of a numerical study on simulated data highlight the feasibility of the proposed allocation rule as well as its advantages over previous methods, and an example using real data is presented. Canadian Center of Science and Education 2009-01 Article PeerReviewed application/pdf en cc_by https://repo.uum.edu.my/id/eprint/4610/1/Str.pdf Mahat, Nor Idayu and Krzanowski, W.J. and Hernandez, A. (2009) Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis. Modern Applied Science, 3 (1). pp. 151-163. ISSN 1913-1852 http://www.ccsenet.org/journal/index.php/mas/article/view/843 |
| spellingShingle | QA76 Computer software Mahat, Nor Idayu Krzanowski, W.J. Hernandez, A. Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis |
| title | Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis |
| title_full | Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis |
| title_fullStr | Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis |
| title_full_unstemmed | Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis |
| title_short | Strategies for non-parametric smoothing of the location model in mixed-variable discriminant analysis |
| title_sort | strategies for non parametric smoothing of the location model in mixed variable discriminant analysis |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/4610/1/Str.pdf |
| work_keys_str_mv | AT mahatnoridayu strategiesfornonparametricsmoothingofthelocationmodelinmixedvariablediscriminantanalysis AT krzanowskiwj strategiesfornonparametricsmoothingofthelocationmodelinmixedvariablediscriminantanalysis AT hernandeza strategiesfornonparametricsmoothingofthelocationmodelinmixedvariablediscriminantanalysis |