Non parametric estimation of the structural expectation of a stochastic increasing function

2008/12/17 by Jean-François Dupuy, Jean‐François Dupuy, Jean-Michel Loubes +6 · 1 citation
Decision Sciences · Mathematics · #62G05 #62G20 #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #FOS: Mathematics #Fuzzy Systems and Optimization #Statistics Theory (math.ST) #math.ST #msc:62G05 #msc:62G20 #stat.TH

paper · pdf · doi:10.48550/arxiv.0812.3252

arxiv created 2008/12/17 · openalex publication_date 2008/12/17 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This article introduces a non parametric warping model for functional data. When the outcome of an experiment is a sample of curves, data can be seen as realizations of a stochastic process, which takes into account the small variations between the different observed curves. The aim of this work is to define a mean pattern which represents the main behaviour of the set of all the realizations. So we define the structural expectation of the underlying stochastic function. Then we provide empirical estimators of this structural expectation and of each individual warping function. Consistency and asymptotic normality for such estimators are proved.

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