The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Goodness of prediction fit
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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“It states that the posterior predictive distribution is the distribution (from the set of all possible distributions Q ) that maximizes the log-likelihood of hypothetical future observations when averaged over the data-generating distribution p ( µ, x 1: t +1 ) = p ( µ ) p ( x 1: t +1 | µ ):”
From Meta-learned models of cognition (2024) · cites this paper as Aitchison (1975)
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Metadata
- 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
- 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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