Nonparametric Statistics for Stochastic Processes: Estimation and Prediction

By John Odenckantz. Technometrics, 2000

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Nonparametric Estimation of Copulas for Time Series (2002, methodology): “We end up this section with the description of how to build the sample analogues of the dependence measures (3)-(6).”
Nonparametric regression for locally stationary time series (2012, methodology): “In order to control the variance part, we generalize results on uniform convergence rates for kernel estimates as provided for example in Bosq [4], Masry [22], and Hansen [13].”
Limit theorems for iterated random functions (2004, background): “Exponential inequalities play important roles in stochastic processes; see Chapter 1 of Bosq (1996) for an extensive treatment, where applications to nonparametric inference are discussed.”
Asymptotic theory for stationary processes (2011, background): “…and Eubank (1999) among others, and for strong mixing processes in Robinson (1983), Singh and Ullah (1985), Castellana and Leadbetter (1986), Györfi et al (1989) and Bosq (1996), Yu (1993), Neumann (1998), Kreiss and Neumann (1998), Härdle et al (1997), Tjostheim (1994) and Fan and Yao (2003).”
Asset Allocation and Risk Assessment with Gross Exposure Constraints for Vast Portfolios (2008, unknown): “Then, by Theorem 1.3 of Bosq (1998), for any integer q ≤ n/2, we have P (|ξ̄n| > ε) ≤ 4 exp(− qε2 8B2 ) + 22(1 + 4B/ε)1/2qα ([n/(2q)]) , (A.7) where ξ̄n = n −1 ∑n t=1 ξt.”