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Minimum distance density-based estimation

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  • Cao, Ricardo
  • Cuevas, Antonio
  • Fraiman, Ricardo

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  • Cao, Ricardo & Cuevas, Antonio & Fraiman, Ricardo, 1995. "Minimum distance density-based estimation," Computational Statistics & Data Analysis, Elsevier, vol. 20(6), pages 611-631, December.
    • Handle: RePEc:eee:csdana:v:20:y:1995:i:6:p:611-631
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    References listed on IDEAS

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    1. Christian Léger & Joseph Romano, 1990. "Bootstrap choice of tuning parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(4), pages 709-735, December.
    2. Mammen, Enno, 1994. "On Qualitative Smoothness of Kernel Density Estimates," SFB 373 Discussion Papers 1994,37, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Keewhan Choi, 1969. "Estimators for the parameters of a finite mixture of distributions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 107-116, December.
    4. Hall, Peter, 1990. "Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 177-203, February.
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    Cited by:

    1. Priebe, Carey E. & Marchette, David J., 2000. "Alternating kernel and mixture density estimates," Computational Statistics & Data Analysis, Elsevier, vol. 35(1), pages 43-65, November.
    2. Basu, Ayanendranath & Lindsay, Bruce G., 2004. "The iteratively reweighted estimating equation in minimum distance problems," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 105-124, March.
    3. Mercedes Fernandez Sau & Daniela Rodriguez, 2018. "Minimum distance method for directional data and outlier detection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 587-603, September.
    4. A. Philip Dawid & Monica Musio & Laura Ventura, 2016. "Minimum Scoring Rule Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 123-138, March.
    5. Sangyeol Lee & Junmo Song, 2009. "Minimum density power divergence estimator for GARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 316-341, August.
    6. Kang, Jiwon & Lee, Sangyeol, 2014. "Minimum density power divergence estimator for Poisson autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 44-56.
    7. Srabashi Basu & Ayanendranath Basu & M. Jones, 2006. "Robust and Efficient Parametric Estimation for Censored Survival Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 341-355, June.
    8. Sangyeol Lee & Junmo Song, 2013. "Minimum density power divergence estimator for diffusion processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(2), pages 213-236, April.
    9. Delicado, Pedro & Vieu, Philippe, 2015. "Optimal level sets for bivariate density representation," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 1-18.
    10. Gayen, Atin & Kumar, M. Ashok, 2021. "Projection theorems and estimating equations for power-law models," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    11. Chee, Chew-Seng & Wang, Yong, 2013. "Minimum quadratic distance density estimation using nonparametric mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 1-16.

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