On Nonparametric Predictive Inference and Objective Bayesianism

Coolen, Frank P. A.

doi:10.1007/s10849-005-9005-7

Cited by 83 publications

(112 citation statements)

References 48 publications

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“…Yet, it may be that this is too strong a requirement for some DMs to commit to, and instead imprecise or nonparametric extensions may be more reasonable. Augustin and Coolen (2004) and Coolen (2006) discuss nonparametric predictive inference as an alternative to a precise parametric probability distribution for quantifying uncertainty, and we are currently exploring the use of this statistical inference technique for use in situations of preference uncertainty.…”

Section: Future Directionsmentioning

confidence: 99%

Adaptive utility and trial aversion

Houlding¹,

Coolen²

2011

Journal of Statistical Planning and Inference

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Decision making with adaptive utility provides a generalisation to classical Bayesian decision theory, allowing the creation of a normative theory for decision selection when preferences are initially uncertain. In this paper we address some of the foundational issues of adaptive utility as seen from the perspective of a Bayesian statistician. The implications that such a generalisation has upon the traditional utility concepts of value of information and risk aversion are also explored, with a new concept of trial aversion introduced that is similar to risk aversion, but which concerns a decision maker's aversion to selecting decisions with high uncertainty over resulting utility.

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Section: Future Directionsmentioning

confidence: 99%

Adaptive utility and trial aversion

Houlding¹,

Coolen²

2011

Journal of Statistical Planning and Inference

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“…NPI is based on the assumption n, proposed by Hill's [1], which gives a direct conditional probability for a future real-valued random quantity, conditional on observed values of n related random quantities [2,3]. Effectively, it assumes that the rank of the future observation among the observed values is equally likely to have each possible value .…”

Section: Introductionmentioning

confidence: 99%

Predictive inference for bivariate data with nonparametric copula

Muhammad

Coolen

Coolen‐Maturi

2016

AIP Conference Proceedings

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Abstract. This study presents a new nonparametric method for prediction of a future bivariate observation, by combining non-parametric predictive inference (NPI) for the marginals with nonparametric copula. In this paper we specifically use kernel method to take dependence structure into account. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only few modelling assumptions. While, copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. We estimate the copula density using kernel method and investigate the performance of this method via simulations. We discuss the simulation results to show how our method performs for different sample sizes and apply the method to data sets from the literature and briefly outline related challenges and opportunities for future research.

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“…Nonparametric predictive inference (NPI) is a statistical method based on Hill's assumption A (n) (Hill, 1968), which gives a direct conditional probability for a future observable random quantity, conditional on observed values of related random quantities (Augustin and Coolen, 2004;Coolen, 2006). A (n) does not assume anything else, and can be interpreted as a post-data assumption related to exchangeability (De Finetti, 1974).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric predictive inference for combined competing risks data

Coolen‐Maturi

Coolen

2014

Reliability Engineering & System Safety

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. (2014) 'Nonparametric predictive inference for combined competing risks data.', Reliability engineering system safety., 126 . pp. 87-97. Further information on publisher's website:http://dx.doi.org/10.1016/j.ress. 2014.01.007 Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Reliability Engineering System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Reliability Engineering System Safety, 126, 2014, 10.1016/j.ress.2014.01.007. Additional information: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-pro t 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 nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies.

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On Nonparametric Predictive Inference and Objective Bayesianism

Cited by 83 publications

References 48 publications

Adaptive utility and trial aversion

Adaptive utility and trial aversion

Predictive inference for bivariate data with nonparametric copula

Nonparametric predictive inference for combined competing risks data

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