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引用次数: 0
摘要
在这项工作中,我们证明了 q 个变量模一分布的联合收敛性,这些变量是 i.i.d. 平方可积分随机变量序列的部分和乘以由同一序列的经验平均值的某个函数给出的公共因子得到的。极限在 \([0,1]^q\) 上均匀分布。为了处理公共因子引入的耦合,我们假定随机变量联合分布的绝对连续(关于勒贝格度量)部分非零,因此该序列的中心极限定理中的收敛在总变异距离中成立。虽然我们的结果提供了将本福德定律推广到数据适配尾数的方法,但我们的主要动机是推导分层再抽样机制的中心极限定理,这将在配套论文(Flenghi 和 Jourdain, Central limit theorem for the stratified selection mechanism, 2023, http://arxiv.org/abs/2308.02186)中进行。
Convergence to the Uniform Distribution of Vectors of Partial Sums Modulo One with a Common Factor
In this work, we prove the joint convergence in distribution of q variables modulo one obtained as partial sums of a sequence of i.i.d. square-integrable random variables multiplied by a common factor given by some function of an empirical mean of the same sequence. The limit is uniformly distributed over \([0,1]^q\). To deal with the coupling introduced by the common factor, we assume that the absolutely continuous (with respect to the Lebesgue measure) part of the joint distribution of the random variables is nonzero, so that the convergence in the central limit theorem for this sequence holds in total variation distance. While our result provides a generalization of Benford’s law to a data-adapted mantissa, our main motivation is the derivation of a central limit theorem for the stratified resampling mechanism, which is performed in the companion paper (Flenghi and Jourdain, Central limit theorem for the stratified selection mechanism, 2023, http://arxiv.org/abs/2308.02186).