Inferences for hierarchical models with partial prior information
Saved in:
| Title: | Inferences for hierarchical models with partial prior information |
|---|---|
| Authors: | Deely, John J.1 jdeely@stat.purdue.edu, Johnson, Wesley O.2 |
| Source: | Journal of Statistical Planning & Inference. Jul2006, Vol. 136 Issue 7, p2327-2339. 13p. |
| Subjects: | Bayesian analysis, Mathematics, Statistical decision making, Mathematical models |
| Abstract: | Abstract: Suppose items can be purchased from one of k-suppliers and it is required to purchase from the one with the smaller failure rate or equivalently from the one with the larger mean-time-to-failure. It is assumed that data , in the form of the times-to-failure for items from suppliers , respectively is available. There are two suggested selection criteria studied in this paper and when comparing only two suppliers they reduce towhere b and c are prespecified practical constants; and are the respective mean failure rates; and are the predicted times to failure for individual items purchased from each supplier. In addition partial prior information about the -suppliers collectively is assumed to have been elicited. This situation is modelled using the hierarchical Bayesian approach, which easily facilitates interpreting the elicited partial prior information as constraints on the hyperpriors, i.e. hyperpriors that are known only to be contained in families with specified properties. In this paper these properties are assumed to be in the form of specifying certain quantiles arising from the elicited information. Minimum and maximum values of the above selection criteria are obtained and are used to indicate whether or not the elicited prior information is useful. Specific examples are given for comparing two suppliers but generalisation to k-suppliers follows easily. [Copyright &y& Elsevier] |
| Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 20185068 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Inferences for hierarchical models with partial prior information – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Deely%2C+John+J%2E%22">Deely, John J.</searchLink><relatesTo>1</relatesTo><i> jdeely@stat.purdue.edu</i><br /><searchLink fieldCode="AR" term="%22Johnson%2C+Wesley+O%2E%22">Johnson, Wesley O.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Statistical+Planning+%26+Inference%22">Journal of Statistical Planning & Inference</searchLink>. Jul2006, Vol. 136 Issue 7, p2327-2339. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Suppose items can be purchased from one of k-suppliers and it is required to purchase from the one with the smaller failure rate or equivalently from the one with the larger mean-time-to-failure. It is assumed that data , in the form of the times-to-failure for items from suppliers , respectively is available. There are two suggested selection criteria studied in this paper and when comparing only two suppliers they reduce towhere b and c are prespecified practical constants; and are the respective mean failure rates; and are the predicted times to failure for individual items purchased from each supplier. In addition partial prior information about the -suppliers collectively is assumed to have been elicited. This situation is modelled using the hierarchical Bayesian approach, which easily facilitates interpreting the elicited partial prior information as constraints on the hyperpriors, i.e. hyperpriors that are known only to be contained in families with specified properties. In this paper these properties are assumed to be in the form of specifying certain quantiles arising from the elicited information. Minimum and maximum values of the above selection criteria are obtained and are used to indicate whether or not the elicited prior information is useful. Specific examples are given for comparing two suppliers but generalisation to k-suppliers follows easily. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=20185068 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jspi.2005.08.021 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 2327 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Mathematics Type: general – SubjectFull: Statistical decision making Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Inferences for hierarchical models with partial prior information Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Deely, John J. – PersonEntity: Name: NameFull: Johnson, Wesley O. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2006 Type: published Y: 2006 Identifiers: – Type: issn-print Value: 03783758 Numbering: – Type: volume Value: 136 – Type: issue Value: 7 Titles: – TitleFull: Journal of Statistical Planning & Inference Type: main |
| ResultId | 1 |
