Nonparametric Predictive Category Selection for Multinomial Data

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

doi:10.1080/15598608.2010.10412000

Cited by 3 publications

(12 citation statements)

References 11 publications

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“…For unordered categorical data, NPI has been developed (Coolen and Augustin 2009), and it has already been applied to category selection (Baker and Coolen 2010). There is an interesting opportunity to apply that method to problems involving diagnostic tests, particularly if a loss function can be identified that quantifies the consequences of wrong diagnoses.…”

Section: Discussionmentioning

confidence: 99%

Nonparametric Predictive Inference for Accuracy of Ordinal Diagnostic Tests

Elkhafifi

Coolen

2012

Journal of Statistical Theory and Practice

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We introduce nonparametric predictive inference (NPI) for accuracy of diagnostic tests with ordinal outcomes, with the inferences based on data for a disease group and a non-disease group. We introduce empirical and NPI lower and upper receiver operating characteristic (ROC) curves and the corresponding areas under the curves, and we prove that these are nested, with the latter equal to the NPI lower and upper probabilities for correctly ordered future observations from the non-disease and disease groups. We discuss the use of the Youden index related to the NPI lower and upper ROC curves in order to determine the optimal cutoff point for the test.

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Section: Discussionmentioning

confidence: 99%

Nonparametric Predictive Inference for Accuracy of Ordinal Diagnostic Tests

Elkhafifi

Coolen

2012

Journal of Statistical Theory and Practice

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“…Then, the majority vote is taken for classification. There are also several other works on classification trees from imprecise probability perspective, and based on Nonparametric Predictive Inference [3,14,35]. Abellán et al [3] and Baker [14] show that applying the NPI-M to classification trees leads to slightly better classification accuracy than the IDM.…”

Section: Imprecise Probabilitiesmentioning

confidence: 99%

“…There are also several other works on classification trees from imprecise probability perspective, and based on Nonparametric Predictive Inference [3,14,35]. Abellán et al [3] and Baker [14] show that applying the NPI-M to classification trees leads to slightly better classification accuracy than the IDM. In this thesis, we also introduce a new application of classification trees with imprecise probability, and based on the Nonparametric Predictive Inference approach.…”

Section: Imprecise Probabilitiesmentioning

confidence: 99%

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Direct Nonparametric Predictive Inference Classification Trees

Atiah

Coolen

Coolen‐Maturi

2023

Preprint

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“…A (n) is not su cient to derive precise probabilities for many events of interest, but optimal bounds for probabilities for all events of interest can be derived via the 'fundamental theorem of probability' [16]. These optimal bounds are lower and upper probabilities [1,2], and are applied in NPI to a range of statistical applications, where through the use of latent variable representations also methods for Bernoulli and multinomial data have been developed [3,6,7,9,10]. A generalization of A (n) in order to deal with right-censored observations has also been presented [12] and was e.g.…”

Section: Preliminariesmentioning

confidence: 99%

Nonparametric predictive inference for stock returns

Baker¹,

Coolen‐Maturi

Coolen

2016

Journal of Applied Statistics

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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. In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalise the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the inferences to adapt to the available data and that the level of imprecision in inferences can reflect the amount of data on which these are based. This paper introduces nonparametric predictive inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their future aggregate returns. The proposed NPI methods are illustrated using historical stock market data.

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Nonparametric Predictive Category Selection for Multinomial Data

Cited by 3 publications

References 11 publications

Nonparametric Predictive Inference for Accuracy of Ordinal Diagnostic Tests

Nonparametric Predictive Inference for Accuracy of Ordinal Diagnostic Tests

Direct Nonparametric Predictive Inference Classification Trees

Nonparametric predictive inference for stock returns

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