Abstract
Often inference for a subset of model parameters is desired, and the others are treated as nuisance parameters. Among the many methods for attacking this problem is conditional inference, in which sufficient statistics for nuisance parameters are conditioned on. Calculations involving these conditional distributions are often quite difficult. This chapter will develop methods for approximating densities and distribution functions for conditional distributions.
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© 1997 Springer Science+Business Media New York
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Kolassa, J.E. (1997). Conditional Distribution Approximations. In: Series Approximation Methods in Statistics. Lecture Notes in Statistics, vol 88. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-4277-0_7
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DOI: https://doi.org/10.1007/978-1-4757-4277-0_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98224-3
Online ISBN: 978-1-4757-4277-0
eBook Packages: Springer Book Archive