Cited by 256Statistical Distribution Estimation and ApplicationsStatistical Methods and Bayesian InferenceBayesian Methods and Mixture Models

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Goodness of prediction fit

AITCHISON, J.·1975·Biometrika

DOI 10.1093/biomet/62.3.547

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Abstract

Fitting a parametric model or estimating a parametric density function plays an important role in a number of statistical applications. Two widely-used methods, one replacing the unknown parameter by an efficient estimate and so termed estimative and the other using a mixture of the possible density functions and commonly termed predictive, are compared. On a general criterion of closeness of fit based on a discriminating information measure the predictive method is shown to be preferable. Explicit measures of the relative closeness of predictive and estimative fits are obtained for gamma and multinomial models.

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Authors: AITCHISON, J.
Affiliations: University of Glasgow (GB)
Corresponding author: AITCHISON, J.
Venue: Biometrika
Publisher: Oxford University Press (OUP)
Published: 1975
Volume: 62
Issue: 3
Pages: 547-554
ISSN: 0006-3444 (ISSN-L)
1464-3510
DOI: 10.1093/biomet/62.3.547
OpenAlex: W2034517627
Cited by: 256
References: 13
Citations by year: 2026: 2 · 2025: 8 · 2024: 5 · 2023: 6 · 2022: 2 · 2021: 11 · 2020: 12 · 2019: 10 · 2018: 5 · 2017: 8 · 2016: 3 · 2015: 5 · 2014: 9 · 2013: 9 · 2012: 8
FWCI: 8.61
Type: journal article
Language: en
Open access: closed
Locations: Biometrika (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Keywords: goodness of fit, parametric density estimation, predictive density, discriminant information, gamma distribution, multinomial distribution, model fitting

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