Regression function estimation as a partly inverse problem


This paper is about nonparametric regression function estimation. Our estimator is a one-step projection estimator obtained by least-squares contrast minimization. The specificity of our work is to consider a new model selection procedure including a cutoff for the underlying matrix inversion, and to provide theoretical risk bounds that apply to non-compactly supported bases, a case which was specifically excluded of most previous results. Upper and lower bounds for resulting rates are provided.

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  1. 1.

    If \(b_A\) is a combination of \( \Gamma \)-type functions, then the bias term \(\inf _{t\in S_m}\Vert b_A-t\Vert ^2\) is much smaller (exponentially decreasing) and the rate \(\log (n)/n\) can be reached by the adaptive estimator (see e.g. Mabon 2017).

  2. 2.

    In Cohen et al. (2013), the condition \(K(m)<+\infty \) is not clearly stated; it is implicit as the result does not hold otherwise. Actually all examples of the paper are for A compact, in which case \(K(m)<+\infty \). If A is not compact, then K(m) may be \(+\infty \). Therefore, our condition (7) and Lemma 4 clarify Cohen et al.’s result.


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Comte, F., Genon-Catalot, V. Regression function estimation as a partly inverse problem. Ann Inst Stat Math 72, 1023–1054 (2020).

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  • Hermite basis
  • Laguerre basis
  • Model selection
  • Nonparametric estimation
  • Regression function