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Estimation Problems with Data from a Mixture

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  • G. D. Murray
  • D. M. Titterington

Abstract

The problem of estimating density functions using data from different distributions, and a mixture of them, is considered. Maximum likelihood and Bayesian parametric techniques are summarized and various approaches using distribution‐free kernel methods are expounded. A comparative study is made using the halibut data of Hosmer (1973) and the problem of incomplete data is briefly discussed.

Suggested Citation

  • G. D. Murray & D. M. Titterington, 1978. "Estimation Problems with Data from a Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 325-334, November.
    • Handle: RePEc:bla:jorssc:v:27:y:1978:i:3:p:325-334
    DOI: 10.2307/2347169
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    Cited by:

    1. Jing Qin & Denis H. Y. Leung, 2004. "A Semi-parametric Two-component “Compound” Mixture Model and Its Application to Estimating Malaria Attributable Fractions," Working Papers 17-2004, Singapore Management University, School of Economics.
    2. Yufan Wang & Xingzhong Xu, 2023. "A Posterior p -Value for Homogeneity Testing of the Three-Sample Problem," Mathematics, MDPI, vol. 11(18), pages 1-25, September.
    3. Glenn Heller & Jing Qin, 2001. "Pairwise Rank-Based Likelihood for Estimation and Inference on the Mixture Proportion," Biometrics, The International Biometric Society, vol. 57(3), pages 813-817, September.

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