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Saddlepoint approximation at the edges of a conditional sample space

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  • Kolassa, John E.

Abstract

Saddlepoint methods present a convenient way to approximate probabilities associated with canonical sufficient statistic vectors in generalized linear models. Implementing saddlepoint approximations requires calculating maximum likelihood estimators for the associated parameters. When the sufficient statistic vector lies at the edge of the sample space, maximum likelihood estimators may not exist. This paper describes how to modify saddlepoint approximation to work in these cases.

Suggested Citation

  • Kolassa, John E., 2000. "Saddlepoint approximation at the edges of a conditional sample space," Statistics & Probability Letters, Elsevier, vol. 50(4), pages 343-349, December.
    • Handle: RePEc:eee:stapro:v:50:y:2000:i:4:p:343-349
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    References listed on IDEAS

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    1. John E. Kolassa & Martin A. Tanner, 1999. "Small-Sample Confidence Regions in Exponential Families," Biometrics, The International Biometric Society, vol. 55(4), pages 1291-1294, December.
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    Cited by:

    1. Christopher Withers & Saralees Nadarajah, 2010. "Tilted Edgeworth expansions for asymptotically normal vectors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 1113-1142, December.

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