基于递归沃尔弗顿-瓦格纳估计的统一规范中多维密度的置信区域

arXiv - STAT - Statistics Theory Pub Date : 2024-09-02 DOI:arxiv-2409.01451

Maria Rosaria Formica, Eugeny Ostrovsky, Leonid Sirota

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引用次数: 0

摘要

我们根据著名的递归沃尔弗顿-瓦格纳密度估计，在随机向量样本的基础上，为未知的 Lebesgue-Riesz 和 Uniform} 规范分布密度构建了一个最优指数尾部递减置信区。

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Confidence regions for the multidimensional density in the uniform norm based on the recursive Wolverton-Wagner estimation

We construct an optimal exponential tail decreasing confidence region for an unknown density of distribution in the Lebesgue-Riesz as well as in the uniform} norm, built on the sample of the random vectors based of the famous recursive Wolverton-Wagner density estimation.

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arXiv - STAT - Statistics Theory

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