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Non-Parametric Conditional U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design

Author

Listed:
  • Salim Bouzebda

    (Laboratory of Applied Mathematics of Compiègne(LMAC), Université de Technologie de Compiègne, CEDEX, 60 203 Compiègne, France
    These authors contributed equally to this work.)

  • Inass Soukarieh

    (Laboratory of Applied Mathematics of Compiègne(LMAC), Université de Technologie de Compiègne, CEDEX, 60 203 Compiègne, France
    These authors contributed equally to this work.)

Abstract

Stute presented the so-called conditional U -statistics generalizing the Nadaraya–Watson estimates of the regression function. Stute demonstrated their pointwise consistency and the asymptotic normality. In this paper, we extend the results to a more abstract setting. We develop an asymptotic theory of conditional U -statistics for locally stationary random fields { X s , A n : s in R n } observed at irregularly spaced locations in R n = [ 0 , A n ] d as a subset of R d . We employ a stochastic sampling scheme that may create irregularly spaced sampling sites in a flexible manner and includes both pure and mixed increasing domain frameworks. We specifically examine the rate of the strong uniform convergence and the weak convergence of conditional U -processes when the explicative variable is functional. We examine the weak convergence where the class of functions is either bounded or unbounded and satisfies specific moment conditions. These results are achieved under somewhat general structural conditions pertaining to the classes of functions and the underlying models. The theoretical results developed in this paper are (or will be) essential building blocks for several future breakthroughs in functional data analysis.

Suggested Citation

  • Salim Bouzebda & Inass Soukarieh, 2022. "Non-Parametric Conditional U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 11(1), pages 1-69, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:16-:d:1009378
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    References listed on IDEAS

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