Regression estimation from an individual stable sequence

Gusztáv Morvai, Sanjeev R. Kulkarni, Andrew B. Nobel

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

We consider univariate regression estimation from an individual (non-random) sequence (x1, y1), (x2, y2),...∈ ℝ x ℝ, which is stable in the sense that for each interval A ⊆ ℝ, (i) the limiting relative frequency of A under x1, x2,... is governed by an unknown probability distribution μ, and (ii) the limiting average of those yi with xi∈ A is governed by an unknown regression function m(·). A computationally simple scheme for estimating m(·) is exhibited, and is shown to be L2 consistent for stable sequences {(xi, yi)} such that {yi} is bounded and there is a known upper bound for the variation of m(·) on intervals of the form (-i, i], i ≥ 1. Complementing this positive result, it is shown that there is no consistent estimation scheme for the family of stable sequences whose regression functions have finite variation, even under the restriction that xi∈ [0, 1] and yi is binary-valued.

Original languageEnglish
Pages (from-to)99-118
Number of pages20
JournalStatistics
Volume33
Issue number2
DOIs
Publication statusPublished - Jan 1 1999

Keywords

  • Ergodic time series
  • Individual sequences
  • Nonparametric estimation
  • Regression estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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