### Abstract

We consider univariate regression estimation from an individual (non-random) sequence (x_{1}, y_{1}), (x_{2}, y_{2}),...∈ ℝ x ℝ, which is stable in the sense that for each interval A ⊆ ℝ, (i) the limiting relative frequency of A under x_{1}, x_{2},... is governed by an unknown probability distribution μ, and (ii) the limiting average of those y_{i} with x_{i}∈ A is governed by an unknown regression function m(·). A computationally simple scheme for estimating m(·) is exhibited, and is shown to be L_{2} consistent for stable sequences {(x_{i}, y_{i})} such that {y_{i}} 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 x_{i}∈ [0, 1] and y_{i} is binary-valued.

Original language | English (US) |
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Pages (from-to) | 99-118 |

Number of pages | 20 |

Journal | Statistics |

Volume | 33 |

Issue number | 2 |

DOIs | |

State | Published - Jan 1 1999 |

### All Science Journal Classification (ASJC) codes

- Statistics and Probability
- Statistics, Probability and Uncertainty

### Keywords

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

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## Cite this

*Statistics*,

*33*(2), 99-118. https://doi.org/10.1080/02331889908802686