Heteroscedastic linear models for analysing process data
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| Title: | Heteroscedastic linear models for analysing process data |
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| Authors: | Ilmari Juutilainen, Juha Röning |
| Contributors: | The Pennsylvania State University CiteSeerX Archives |
| Source: | http://www.ee.oulu.fi/mvg/files/pdf/pdf_573.pdf. |
| Collection: | CiteSeerX |
| Subject Terms: | Key-Words, Heteroscedastic linear model, Model selection, Dual response surface, Dispersion modelling, Process data analysis, Validation, Predictive modelling |
| Description: | In this paper the guidelines for applying heteroscedastic linear models for analysing industrial process data is presented. Heteroscedastic linear models are considered as a good model family for the joint modelling of dispersion and mean. The model selection of heteroscedastic linear model is discussed considering the special features of industrial data. A procedure for dispersion model selection based on the validation deviance related to the gamma model on the squared residuals of the mean model is presented. The model selection procedure is tested using simulated data and also in a real industrial application. The estimation and model selection procedures are relatively simple and can be implemented using standard statistical software. |
| Document Type: | text |
| File Description: | application/pdf |
| Language: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.7835; http://www.ee.oulu.fi/mvg/files/pdf/pdf_573.pdf |
| Availability: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.7835 http://www.ee.oulu.fi/mvg/files/pdf/pdf_573.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Accession Number: | edsbas.21F707A4 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | In this paper the guidelines for applying heteroscedastic linear models for analysing industrial process data is presented. Heteroscedastic linear models are considered as a good model family for the joint modelling of dispersion and mean. The model selection of heteroscedastic linear model is discussed considering the special features of industrial data. A procedure for dispersion model selection based on the validation deviance related to the gamma model on the squared residuals of the mean model is presented. The model selection procedure is tested using simulated data and also in a real industrial application. The estimation and model selection procedures are relatively simple and can be implemented using standard statistical software. |
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