Model uncertainty, data mining and statistical inference

Chatfield, Chris

doi:10.2307/2983440

Cited by 1,103Statistical Methods and InferenceStatistical Methods and Bayesian InferenceStatistical Methods and Applications

Model uncertainty, data mining and statistical inference

Chatfield, Chris·1995·Journal of the Royal Statistical Society. Series A (Statistics in Society)

DOI 10.2307/2983440

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Abstract

This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding the model structure. This implicitly assumes the existence of a 'true' model, which many would regard as a fiction. In practice model uncertainty is a fact of life and likely to be more serious than other sources of uncertainty which have received far more attention from statisticians. This is true whether the model is specified on subject-matter grounds or, as is increasingly the case, when a model is formulated, fitted and checked on the same data set in an iterative, interactive way. Modern computing power allows a large number of models to be considered and data-dependent specification searches have become the norm in many areas of statistics. The term data mining may be used in this context when the analyst goes to great lengths to obtain a good fit. This paper reviews the effects of model uncertainty, such as too narrow prediction intervals, and the non-trivial biases in parameter estimates which can follow data-based modelling. Ways of assessing and overcoming the effects of model uncertainty are discussed, including the use of simulation and resampling methods, a Bayesian model averaging approach and collecting additional data wherever possible. Perhaps the main aim of the paper is to ensure that statisticians are aware of the problems and start addressing the issues even if there is no simple, general theoretical fix.

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In reality, the true model is unknown, may not have a generalized linear form, or may not even exist.
“in reality, the true model is unknown, may not have a generalized linear form, or may not even exist [22].”
From A calibration hierarchy for risk models was defined: from utopia to empirical data (2016) · cites this paper as [22]

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Authors: Chatfield, C.
Affiliations: University of Bath (GB)
Corresponding author: Chatfield, Chris
Venue: Journal of the Royal Statistical Society. Series A (Statistics in Society)
Publisher: Oxford University Press (OUP)
Published: 1995
Volume: 158
Issue: 3
Pages: 419
ISSN: 0964-1998 (ISSN-L)
DOI: 10.2307/2983440
OpenAlex: W2060050130
Cited by: 1,103
References: 117
Citations by year: 2026: 18 · 2025: 34 · 2024: 38 · 2023: 33 · 2022: 35 · 2021: 33 · 2020: 44 · 2019: 28 · 2018: 47 · 2017: 35 · 2016: 37 · 2015: 51 · 2014: 52 · 2013: 28 · 2012: 63
FWCI: 24.46
Type: journal article
Language: en
Open access: closed
Locations: Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: model uncertainty, data mining, statistical inference, model selection, prediction intervals, estimation bias, Bayesian model averaging, resampling, simulation, model misspecification

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