Sequential design for nonparametric inference

Zhibiao Zhao, Weixin Yao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The performance of nonparametric function estimates often depends on the choice of design points. Based on the mean integrated squared error criterion, we propose a sequential design procedure that updates the model knowledge and optimal design density sequentially. The methodology is developed under a general framework covering a wide range of nonparametric inference problems, such as conditional mean and variance functions, the conditional distribution function, the conditional quantile function in quantile regression, functional coefficients in varying coefficient models and semiparametric inferences. Based on our empirical studies, nonparametric inference based on the proposed sequential design is more efficient than the uniform design and its performance is close to the true but unknown optimal design.

Original languageEnglish (US)
Pages (from-to)362-377
Number of pages16
JournalCanadian Journal of Statistics
Issue number2
StatePublished - Jun 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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