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Pseudo-likelihood and quasi-likelihood estimation for complex sampling schemes

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  • C., E. A. Molina
  • Skinner, C. J.

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  • C., E. A. Molina & Skinner, C. J., 1992. "Pseudo-likelihood and quasi-likelihood estimation for complex sampling schemes," Computational Statistics & Data Analysis, Elsevier, vol. 13(4), pages 395-405, May.
    • Handle: RePEc:eee:csdana:v:13:y:1992:i:4:p:395-405
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    Cited by:

    1. Brajendra C. Sutradhar, 2022. "Multinomial Logistic Mixed Models for Clustered Categorical Data in a Complex Survey Sampling Setup," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 743-789, August.
    2. David Gunawan & William Griffths & Anatasios Panagiotelis and Duangkamon Chotikapanich, 2017. "Bayesian Weighted Inference from Surveys "Abstract: Data from large surveys are often supplemented with sampling weights that are designed to reflect unequal probabilities of response and selecti," Department of Economics - Working Papers Series 2030, The University of Melbourne.
    3. Skinner, Chris J., 2018. "Analysis of categorical data for complex surveys," LSE Research Online Documents on Economics 89707, London School of Economics and Political Science, LSE Library.
    4. Elena Castilla & Nirian Martín & Leandro Pardo, 2018. "Minimum phi-divergence estimators for multinomial logistic regression with complex sample design," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 381-411, July.

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