A new wavelet-based estimation of conditional density via block threshold method

Esmaeil Shirazi and Olivier Paul Faugeras
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Esmaeil Shirazi: Faculty of Science, Gonbad Kavous university
Olivier Paul Faugeras: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: A new wavelet-based estimator of the conditional density is investigated. The estimator is constructed by combining a special ratio technique and applying a non negative estimator to the density function in the denominator. We used a wavelet shrinkage technique to find an adaptive estimator for this problem. In particular, a block thresholding estimator is proposed, and we prove that it enjoys powerful mean integrated squared error properties over Besov balls. Moreover, it is shown that convergence rates for the mean integrated squared error (MISE) of the adaptive estimator are optimal under some mild assumptions. Finally, a numerical example has been considered to illustrate the performance of the estimator.

Keywords: Besov space; Block thresholdestimator; Conditionaldensity; Non parametricestimation; Wavelet methods (search for similar items in EconPapers)
Date: 2023-11
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Published in Communications in Statistics - Theory and Methods, 2023, ⟨10.1080/03610926.2023.2279917⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04315135

DOI: 10.1080/03610926.2023.2279917

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