Abstract
Conditional inference is a fundamental part of statistical theory. However, exact conditional inference is often awkward, leading to the desire for methods which offer accurate approximations. Such a methodology is provided by small-sample likelihood asymptotics. We argue in this paper that simple, simulation-based methods also offer accurate approximations to exact conditional inference in multiparameter exponential family and ancillary statistic settings. Bootstrap simulation of the marginal distribution of an appropriate statistic provides a conceptually simple and highly effective alternative to analytic procedures of approximate conditional inference.
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Young, G.A., DiCiccio, T.J. (2010). Computer-intensive conditional inference. In: Mantovan, P., Secchi, P. (eds) Complex Data Modeling and Computationally Intensive Statistical Methods. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-1386-5_11
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DOI: https://doi.org/10.1007/978-88-470-1386-5_11
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-1385-8
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