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Marginal density estimation from incomplete bivariate data

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  • Hazelton, Martin L.

Abstract

The problem of estimating a marginal density from incomplete bivariate data is considered. A kernel estimator is proposed. Strong consistency of the estimator is proved, and asymptotic formulae for its mean and variance derived. A method of bandwidth selection is suggested. Application of the estimator is then illustrated on example data sets. Possible extensions and improvements are discussed.

Suggested Citation

  • Hazelton, Martin L., 2000. "Marginal density estimation from incomplete bivariate data," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 75-84, March.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:1:p:75-84
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    Cited by:

    1. Levon Demirdjian & Majid Mojirsheibani, 2019. "Kernel classification with missing data and the choice of smoothing parameters," Statistical Papers, Springer, vol. 60(5), pages 1487-1513, October.
    2. Subhadip Bandyopadhyay & Arup Bose & Debasis Sengupta, 2010. "Nonparametric estimation of multivariate density with direct and auxiliary data and application," Indian Journal of Pure and Applied Mathematics, Springer, vol. 41(1), pages 251-274, February.
    3. Mojirsheibani, Majid & Montazeri, Zahra, 2007. "On nonparametric classification with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1051-1071, May.
    4. Mohsen Arefi & Reinhard Viertl & S. Taheri, 2012. "Fuzzy density estimation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(1), pages 5-22, January.
    5. Majid Mojirsheibani & Zahra Montazeri, 2007. "Statistical classification with missing covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 839-857, November.

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