This article utilizes bootstrap quasi-likelihood (QL) to model sparse functional data. The proposed method combines parallel block bootstrap and QL to fit the functional data. The parameter space is considered as a finite-dimensional space through a certain optimization rule. Statistical errors of the proposed method are discussed. Some asymptotic properties of the method are established under several mild conditions as well. Several simulations are conducted to examine the finite-sample performance of the method. The performance is also demonstrated by analysing real data.

中文翻译：

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稀疏功能数据中拟似然的块引导程序

本文利用自举拟似然 (QL) 对稀疏功能数据进行建模。所提出的方法结合了并行块引导程序和 QL 来拟合功能数据。通过一定的优化规则，参数空间被认为是一个有限维空间。讨论了所提出方法的统计误差。该方法的一些渐近特性也在几个温和的条件下建立。进行了几次模拟以检查该方法的有限样本性能。性能也通过分析真实数据来证明。