Nonparametric predictive utility inference

Houlding, Brett; Coolen, Frank P. A.

doi:10.1016/j.ejor.2012.03.024

Cited by 10 publications

(17 citation statements)

References 34 publications

(49 reference statements)

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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%

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%

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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“…They may instead provide lower and upper utility bounds that they believe their true utility (which will only be derived after multiple experiences) lies within. Note that this subtly differs from NPUI (Houlding & Coolen, 2012) as we consider a DM witnessing noisy realisations of a true utility rather than interpolating from utilities deemed similar. We hope to develop a decision scheme incorporating both imprecise probability (i.e., increased uncertainty over returns from decisions) and imprecise utility (i.e., increased uncertainty about their associated merits).…”

Section: Imprecision Of Probabilities and Utilitiesmentioning

confidence: 99%

“…In the absence of these abilities elicitation methods exist to help discover these unknowns, e.g., the techniques of O'Hagan (1998) are often used for belief elicitation. Regarding utility, a method by which preferences can be ascertained over time is adaptive utility, discussed in, e.g., Cyert and DeGroot (1975), and Houlding & Coolen (2011), and modified to incorporate extreme vagueness in the priors over parameters in Houlding & Coolen (2012). Chajewska et al (2000) also contains information on suitable utility elicitation methods.…”

Section: Decisionsmentioning

confidence: 99%

“…Motivation exists for updating utilities in light of phenomena experienced, i.e., adaptive utility (Cyert & DeGroot, 1975, Houlding & Coolen 2007, 2011. A specialisation of this is Nonparametric Utility Inference (NPUI, Houlding & Coolen, 2012) which contains elements of Nonparametric Inference (Coolen, 2006, Roelofs et al, 2011 and uncertain utility. NPUI relies upon Hill's A (n) assumption (Hill, 1968(Hill, , 1988(Hill, , 1993 which states that if the real line is partitioned into distinct, disjoint subintervals, the probability of θ lying within any of these intervals is equal regardless of their respective widths.…”

Section: Nonparametric Utility Inferencementioning

confidence: 99%

“…The concept of Nonparametric Utility Inference (Houlding & Coolen, 2012) is introduced. Arguments are provided regarding how the rules of this method may be augmented for an opinion updating scenario, where the opinions of a DM over the expectation of the uncertain quantity are represented simply by intervals, referred to as Nonparametric Prevision Intervals (NPPI).…”

Section: Outline Of Thesis Chaptersmentioning

confidence: 99%

See 2 more Smart Citations

Reliability updating in linear opinion pooling for multiple decision makers

Bolger

Houlding

2016

Proceedings of the Institution of Mechanical Engineers, Part O:

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Accurate information sources are vital prerequisites for good decision making. In this thesis we consider a multiple participant setting, where all decision makers (DMs) have a collection of neighbours with whom they share their beliefs about some common relevant uncertain quantity. When determining which course of action to follow a DM takes into account all the information received from her neighbours. Over time, in light of the returns observed from choices made, DMs update their own beliefs over the uncertain event, and also adjust the degree of consideration that they afford to the opinions of each neighbour based on the level of reliability that the information they provide is ascertained to have. Much of this thesis is concerned with constructing a method that incorporates both of these learning facets in a dynamic fashion. This technique, termed the Plug-in approach, is motivated and derived, and attempts are made to justify its use by consideration of some attractive properties it obeys, in addition to studies conducted using both simulated and real data which compared its performance to some rational alternatives. Generalisations of this method are also provided to a setting where DMs specify their opinions nonparametrically rather than using probability distributions, as well as in a group setting where utilities as well as opinions must be amalgamated. Two subjective approaches are also briefly discussed, before we conclude with numerous suggestions for further research in this field.

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On Some Statistical Aspects of Software Testing and Reliability

Coolen

2013

Complex Systems and Dependability

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Nonparametric predictive utility inference

Cited by 10 publications

References 34 publications

Nonparametric predictive inference for system reliability using the survival signature

Nonparametric predictive inference for system reliability using the survival signature

Reliability updating in linear opinion pooling for multiple decision makers

On Some Statistical Aspects of Software Testing and Reliability

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