Sequential design for nonparametric inference

Zhibiao Zhao, Weixin Yao

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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
Volume40
Issue number2
DOIs
StatePublished - Jun 1 2012

Fingerprint

Sequential Design
Nonparametric Inference
Semiparametric Inference
Uniform Design
Conditional Quantiles
Mean Integrated Squared Error
Varying Coefficient Model
Quantile Function
Variance Function
Quantile Regression
Conditional Distribution
Empirical Study
Distribution Function
Covering
Update
Unknown
Methodology
Coefficient
Estimate
Range of data

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Zhao, Zhibiao ; Yao, Weixin. / Sequential design for nonparametric inference. In: Canadian Journal of Statistics. 2012 ; Vol. 40, No. 2. pp. 362-377.
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Sequential design for nonparametric inference. / Zhao, Zhibiao; Yao, Weixin.

In: Canadian Journal of Statistics, Vol. 40, No. 2, 01.06.2012, p. 362-377.

Research output: Contribution to journalArticle

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