Maximising entropy on the nonparametric predictive inference model for multinomial data

Abellán, Joaquín; Baker, Rebecca M.; Coolen, Frank P. A.

doi:10.1016/j.ejor.2011.01.020

Cited by 26 publications

(31 citation statements)

References 29 publications

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“…NPI for system reliability using the signature has also been presented, for systems consisting of only one type of components [4,5,15]. NPI has also been presented for a variety of other problems in operational research and statistics, including predictive analysis for queueing problems [17], replacement problems [24], decision making under uncertain utilities [30] and classification with decision trees using maximum entropy [1,2] (see also www.npi-statistics.com).…”

Section: Nonparametric Predictive Inference For System Failure Timementioning

confidence: 99%

“…Case 1 only involved an initial assessment for rather trivial values of (l 1 , l 2 , l 3 ) for which the system either functions or not with certainty. Without further calculations, the survival signature is only known to be in [0,1] at all other (l 1 , l 2 , l 3 ). Case 2 shows the effect of calculating Φ(0, 0, 3) = 1/2, Case 3 of the additional calculations Φ(0, 1, 0) = Φ(0, 1, 1) = 1/4, these are all pretty trivial to derive.…”

Section: Examplementioning

confidence: 99%

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Nonparametric predictive inference for system reliability using the survival signature

Coolen

Coolen‐Maturi

Al-Nefaiee

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe survival signature has recently been presented as an attractive concept to aid quantification of system reliability. It has similar characteristics as the system signature, which is well established, but contrary to the latter it is easily applicable to systems with multiple types of components. We present an introductory overview of the survival signature together with new results to aid computation. We develop nonparametric predictive inference for system reliability using the survival signature. The focus is on the failure time of a system, given failure times of tested components of the same types as used in the system.

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Section: Nonparametric Predictive Inference For System Failure Timementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Nonparametric predictive inference for system reliability using the survival signature

Coolen

Coolen‐Maturi

Al-Nefaiee

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…For this aim, algorithms to attain the maximum entropy probability are required; these are presented in Abellán et al [8]. We have used 40 data sets with the common characteristic that the class variable has a known number K ≥ 3 of cases or categories, as was considered in the model presented in Coolen and Augustin [17].…”

Section: Introductionmentioning

confidence: 99%

Classification with decision trees from a nonparametric predictive inference perspective

Abellán¹,

Baker²,

Coolen³

et al. 2014

Computational Statistics & Data Analysis

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

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“…Further extensions include [8] where NPI was considered for cases of two finitely exchangeable populations (where exchangeability is only assumed within populations and not between them), and [1] where algorithms were developed for obtaining maximum entropy for multinomial data. In an Operations Research context, NPI was previously applied to problems of decision modelling in multiple queues situations, e.g., [9], and to support replacement decisions e.g., [12], where the fact that the NPI approach adapts fully to the available data is an attractive feature.…”

Section: Nonparametric Predictive Inferencementioning

confidence: 99%

Nonparametric predictive utility inference

Houlding

Coolen

2012

European Journal of Operational Research

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We consider the natural combination of two strands of recent statistical research, i.e., that of decision making with uncertain utility and that of Nonparametric Predictive Inference (NPI). In doing so we present the idea of Nonparametric Predictive Utility Inference (NPUI), which is suggested as a possible strategy for the problem of utility induction in cases of extremely vague prior information. An example of the use of NPUI within a motivating sequential decision problem is also considered for two extreme selection criteria, i.e., a rule that is based on an attitude of extreme pessimism and a rule that is based on an attitude of extreme optimism.

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Maximising entropy on the nonparametric predictive inference model for multinomial data

Cited by 26 publications

References 29 publications

Nonparametric predictive inference for system reliability using the survival signature

Nonparametric predictive inference for system reliability using the survival signature

Classification with decision trees from a nonparametric predictive inference perspective

Nonparametric predictive utility inference

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