Simultaneous confidence bands for the distribution function of a finite population in stratified sampling

Gu, Lei; Wang, Suojin; Yang, Lijian

doi:10.1007/s10463-018-0668-7

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…But the method requires that predictors are independent and identically distributed. For recent theoretical developments and applications of SCBs in various contexts, see, for instance, Krivobokova et al (2010), Cao et al (2012), Wang et al (2013), Gu et al (2014), Wang, J. et al (2014), Gu and Yang (2015), Wang et al (2016), Zheng et al (2016), Degras (2017), Zhang and Yang (2018), Cai et al (2019), Gu et al (2019) and Li and Yang (2022). In this paper, we provide kernel‐based SCBs for nonparametric component function with correlated data within‐clusters which have the same size.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous confidence bands for nonparametric regression with equal cluster size

Oremathilaka

Liu

2022

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Repeated/clustered data are becoming increasingly popular in many research studies. While there is a considerable literature in the estimation of the nonparametric component functions, the problem has been addressed by very few in statistical inference such as simultaneous confidence bands (SCBs). Based on the efficient kernel estimation incorporating within‐cluster error correlation, we provide asymptotic Wald‐type‐based SCBs for the nonparametric function when the repeated measures have the equal cluster size. The performance of the SCBs is evaluated by simulation studies which support the asymptotic theory. The proposed method is also applied to a dataset on the Junior School Project of test scores.

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