A maximum entropy estimate of a wine rating’s distribution: results of tests using large samples of blind triplicates

Bodington, Jeff

doi:10.1080/09571264.2024.2310323

Journal of Wine Research

2024

DOI: 10.1080/09571264.2024.2310323

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A maximum entropy estimate of a wine rating’s distribution: results of tests using large samples of blind triplicates

Jeff Bodington

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2024

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Context and cross-section data improve analyses of wine ratings

Bodington

2024

J Wine Econ

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Much research shows that the ratings that critics, judges, and consumers assign to wines are heteroscedastic. A rating observed is one draw from a latent distribution that is wine- and judge-specific. Estimating the shape of a rating’s distribution by minimizing a sum of cross entropies has been proposed and tested. This article proposes a method of improving the accuracy of that estimate by using information about the context of a wine competition or cross-section ratings data. Tests using the distributions implied by 90 blind triplicate ratings show that the sum of squared errors for the solution using context or cross-section information is 50% more accurate than not using such information and over 99% more accurate than ignoring the uncertainty about a rating.

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Context and cross-section data improve analyses of wine ratings

Bodington

2024

J Wine Econ

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A maximum entropy estimate of a wine rating’s distribution: results of tests using large samples of blind triplicates

Cited by 1 publication

References 7 publications

Context and cross-section data improve analyses of wine ratings

Context and cross-section data improve analyses of wine ratings

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