Computing highly accurate frequentist confidence limits from discrete data

Host Institution:

La Trobe University

Title of Seminar:

Computing highly accurate frequentist confidence limits from discrete data

Speaker's Name:

Professor Chris J. Lloyd

Speaker's Institution:

Melbourne Business School

Time and Date:

Friday 25 October, 12.00pm (AEDT)

Seminar Abstract:

For discrete data, frequentist confidence limits based on a normal approximation to standard likelihood based pivotal quantities can perform poorly even for quite large sample sizes. To construct exact limits requires the probability of a suitable tail set as a function of the unknown parameters. In this paper, importance sampling is used to estimate this surface and hence the confidence limits. The technology is simple and straightforward to implement. Unlike the recent methodology of Garthwaite and Jones (2009), the new method allows for nuisance parameters; is an order of magnitude more efficient than the Robbins-Monro bound; does not require any simulation phases or tuning constants; gives a straightforward simulation standard error for the target limit; includes a simple diagnostic for simulation breakdown.

 

 

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