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Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/17819Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tosetti, E | - |
| dc.contributor.author | Vinciotti, V | - |
| dc.date.accessioned | 2019-04-01T08:58:45Z | - |
| dc.date.available | 2019-04-01T08:58:45Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Journal of the Royal Statistical Society: Series C | en_US |
| dc.identifier.issn | 0035-9254 | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/17819 | - |
| dc.description.sponsorship | The authors acknowledge nancial support from EPSRC [EP/L021250/1]. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Wiley | en_US |
| dc.subject | mixed probit | en_US |
| dc.subject | graphical modelling | en_US |
| dc.subject | EM algorithm | en_US |
| dc.subject | credit risk modelling | en_US |
| dc.title | A computationally efficient correlated mixed probit model for credit risk inference | en_US |
| dc.type | Article | en_US |
| dc.relation.isPartOf | Journal of the Royal Statistical Society: Series C | - |
| pubs.publication-status | Accepted | - |
| Appears in Collections: | Dept of Mathematics Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | 1.18 MB | Adobe PDF | View/Open |
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