Nonparametric regression estimation for dependent functional data: asymptotic normality

By Elias Masry. 2005

244 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/abc57b4c7a2ce34481d5d16ba2d185e36a2304e9

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Citations in the corpus

Nonparametric time series prediction: A semi-functional partial linear modeling (2008, methodology): “Remark 5 Theorem 3 extends the asymptotic normality of the regression function estimator in the pure nonparametric functional regression model (see Masry [13]) to the estimator of the nonparametric part in a SFPLR model.”
Curse of dimensionality and related issues in nonparametric functional regression (2011, background): “[20, 23, 2, 22, 6, 3, 4, 7, 13] or [14] for a few paths in that direction.”
On the Validity of the Bootstrap in Non‐Parametric Functional Regression (2009, background): “Asymptotic normality has been established in Masry (2005) , Delsol (2008) and Ferraty”
Asymptotic normality of a nonparametric estimator of the conditional mode function for functional data (2008, methodology): “This idea was adopted by Masry [22] who reformulated it from Gasser et al. [16] one’s, as a condition related to the functional distribution of W1.”
Advances on asymptotic normality in non-parametric functional time series analysis (2009, background): “More recently, Masry 10 showed the asymptotic normality of the kernel estimator with dependent data.”
Nonparametric modelling for functional data: selected survey and tracks for future (2018, background): “First studies are based on mixing conditions on the sample: complete convergence results of type (4) were firstly presented for basic kernel estimate (3) in [60] and extended in [4,61,62], while uniform rates of type (5) were presented in [63] (see [64]) and asymptotic normality was stated in [65] a”
Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting (2018, methodology): “The most common procedure is to use the Nadaraya-Watson kernel estimator, as in [29] while other methodologies are based on wavelets [30] applied to electricity load forecasting.”
Empirical likelihood confidence intervals for nonparametric functional data analysis (2009, background): “ sequence data as dealt with in our current paper, rates of almost complete convergence (slightly stronger than almost sure convergence) were obtained in Ferraty and Vieu (2004, 2006). Later works by Masry (2005); Ferraty et al. (2007) showed that q nφ(h)(ˆr(x 0) − r(x 0) − b(x 0)) is asymptotically”
NONPARAMETRIC REGRESSION ON FUNCTIONAL DATA: INFERENCE AND PRACTICAL ASPECTS (2007, background): “…the rates of convergence without specification of the exact constants (see Chapter 6 in Ferraty & Vieu, 2006), or abstract expressions of these constants, which are unusable in practice (e.g. in the recent work by Masry (2005), which was published during the reviewing process of the present paper).”
Asymptotic Results of a Nonparametric Conditional Quantile Estimator for Functional Time Series (2008, methodology): “In order to prove (32), we need the Volkonskii and Rozanov’s Lemma (see the Appendix in Masry, 2005).”
Mean estimation with data missing at random for functional covariables (2013, unknown): “This fact has been highlighted by Masry [22], Ferraty et al. [23] and Delsol [24], but extension to the MAR problem and to the averaged estimates is still an open question.”