Quantile-slicing estimation for dimension reduction in regression

Hyungwoo Kim, Yichao Wu, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Sufficient dimension reduction (SDR) has recently received much attention due to its promising performance under less stringent model assumptions. We propose a new class of SDR approaches based on slicing conditional quantiles: quantile-slicing mean estimation (QUME) and quantile-slicing variance estimation (QUVE). Quantile-slicing is particularly useful when the quantile function is more efficient to capture underlying model structure than the response itself, for example, when heteroscedasticity exists in a regression context. Both simulated and real data analysis results demonstrate promising performance of the proposed quantile-slicing SDR estimation methods.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalJournal of Statistical Planning and Inference
Publication statusPublished - 2019 Jan


  • Heteroscedasticity
  • Kernel quantile regression
  • Quantile-slicing estimation
  • Sufficient dimension reduction

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


Dive into the research topics of 'Quantile-slicing estimation for dimension reduction in regression'. Together they form a unique fingerprint.

Cite this