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Information Journal Paper

Title

EXPONENTIAL MODELS: APPROXIMATIONS FOR PROBABILITIES

Pages

  95-107

Abstract

 Welch & Peers (1963) used a root-information prior to obtain posterior probabilities for a scalar parameter EXPONENTIAL MODEL and showed that these Bayes probabilities had the confidence property to second order asymptotically. An important undercurrent of this indicates that the constant information reparameterization provides location model structure, for which the confidence property was and is well known. This paper examines the role of the scalar-parameter EXPONENTIAL MODEL for obtaining APPROXIMATE PROBABILITIES and approximate confidence levels, and then addresses the extension for the vector-parameter EXPONENTIAL MODEL.

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    • Cite

    APA: Copy

    FRASER, D.A.S., NADERI, A., KEXIN, JI, WEI, LIN, & JIE, SU. (2011). EXPONENTIAL MODELS: APPROXIMATIONS FOR PROBABILITIES. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), 10(2), 95-107. SID. https://sid.ir/paper/592494/en

    Vancouver: Copy

    FRASER D.A.S., NADERI A., KEXIN JI, WEI LIN, JIE SU. EXPONENTIAL MODELS: APPROXIMATIONS FOR PROBABILITIES. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS)[Internet]. 2011;10(2):95-107. Available from: https://sid.ir/paper/592494/en

    IEEE: Copy

    D.A.S. FRASER, A. NADERI, JI KEXIN, LIN WEI, and SU JIE, “EXPONENTIAL MODELS: APPROXIMATIONS FOR PROBABILITIES,” JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), vol. 10, no. 2, pp. 95–107, 2011, [Online]. Available: https://sid.ir/paper/592494/en

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