Bootstrap of kernel smoothing in nonlinear time series

By Jürgen Franke, Jens-Peter Kreiss, Enno Mammen. 2002

114 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ad5e1048c6ae3d844611ae0f1d591a97c263cb87

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Citations in the corpus

The local bootstrap for Markov processes (2002, methodology): “Under model class (1), there exists a residual-based procedure investigated by Franke et al. (1996) that begins with nonparametric estimation of m(·) and v(·), and a subsequent i.i.d.”
Model Checks Using Residual Marked Empirical Processes (2004, methodology): “…have been used extensively in the goodness-of-fit literature of regression models, see, e.g., Härdle and Mammen (1993), Stute, González-Manteiga and Presedo-Quindimil (1998) and Li, Hsiao and Zinn (2003) in the i.i.d. context, and Franke, Kreiss and Mammen (2002) for time series sequences.”
Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models (2006, methodology): “This series is of length 300 and is the first quarter of a longer series analyzed extensively by Finkenstädt (1995) and also studied by Fan and Yao (2003, pp.”
Bootstrap Tests for Regression Models (2009, methodology): “The results in Freedman (1984) indicate that, under regularity conditions, bootstrapmethods are asymptotically valid.”
The Impact of Bootstrap Methods on Time Series Analysis (2003, methodology): “The consistency of this procedure under some conditions has been recently shown in a highly technical paper by Franke et al. (2002).”
Bandwidth Selection in Nonparametric Kernel Testing (2008, methodology): “González-Manteiga and Cao (1993) also considered the fixed design regression case but relied on minimum distance estimation of the parametric model, seeking to minimize a weighted L2-type distance between the parametric model and a pilot nonparametric estimator.”
Time-dependent Diffusion Models for Term Structure Dynamics and the Stock Price Volatility (2003, methodology): “For dependent data, we can use the regression bootstrap (see Franke, Kreiss and Mammen (2002) ) to assess the sampling variability.”
An updated review of Goodness-of-Fit tests for regression models (2013, background): “Kreiss et al. (2008) provided an extensive review on this type of tests, justifying the consistency of a variety of resampling procedures for test calibration (see also Franke et al. 2002).”
Non‐parametric detection and estimation of structural change (2012, unknown): “We then proceed as in Franke et al. (2002) and propose the following bootstrap procedure: (a) compute residuals ε̆t = yt − β̆ ′ tXt , t = 1, .”
Bootstrap prediction intervals for linear, nonlinear, and nonparametric autoregressions (2016, methodology): “Under assumptions (AB1)-(AB12) of Franke, Kreiss and Mammen(2002) [18] we have:”
On the detection of changes in autoregressive time series, II. Resampling procedures (2008, unknown): “[10] or Hidalgo [11].”