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Merging information for semiparametric density estimation

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  • Konstantinos Fokianos

Abstract

Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well‐established techniques to choose the smoothing parameter for the density estimators proposed.

Suggested Citation

  • Konstantinos Fokianos, 2004. "Merging information for semiparametric density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 941-958, November.
    • Handle: RePEc:bla:jorssb:v:66:y:2004:i:4:p:941-958
    DOI: 10.1111/j.1467-9868.2004.05480.x
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    Cited by:

    1. Zhang, Archer Gong & Chen, Jiahua, 2022. "Density ratio model with data-adaptive basis function," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    2. Yu-Min Huang, 2019. "Binary surrogates with stratified samples when weights are unknown," Computational Statistics, Springer, vol. 34(2), pages 653-682, June.
    3. Jiang, Shan & Tu, Dongsheng, 2012. "Inference on the probability P(T1," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1069-1078.
    4. Ori Davidov & Konstantinos Fokianos & George Iliopoulos, 2010. "Order-Restricted Semiparametric Inference for the Power Bias Model," Biometrics, The International Biometric Society, vol. 66(2), pages 549-557, June.
    5. 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.
    6. Aubin, Jean-Baptiste & Leoni-Aubin, Samuela, 2008. "Projection density estimation under a m-sample semiparametric model," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2451-2468, January.
    7. Martin L. Hazelton & Berwin A. Turlach, 2010. "Semiparametric Density Deconvolution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 91-108, March.
    8. Xuze Zhang & Saumyadipta Pyne & Benjamin Kedem, 2020. "Estimation of residential radon concentration in Pennsylvania counties by data fusion," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(6), pages 1094-1110, November.
    9. OrI Davidov & Konstantinos Fokianos & George Iliopoulos, 2014. "Semiparametric Inference for the Two-way Layout Under Order Restrictions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 622-638, September.

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