Generalised likelihood ratio tests for spectral density

By Jianqing Fan, Wenyang Zhang. 2004

56 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20f130f5cf2e7acb61a4d74e31b1167ecaf0909a

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Citations in the corpus

A Bayesian test for periodic signals in red noise (2009, methodology): “This is sometimes known as the Whittle likelihood method, after Whittle (1953) and Whittle (1957), and has been discussed in detail elsewhere (e.g. Hannan 1973; Pawitan & O’Sullivan 1994; Fan & Zhang 2004; Contreras-Cristán et al. 2006).”
An updated review of Goodness-of-Fit tests for regression models (2013, unknown): “…) )2 dλ, which has asymptotic distribution-free bias and variance, h−1/2 ∫ π −π K2(x) dx, π−1 ∫ 2π −2π (∫ π −π K(u)K(u + x)du )2 dx. Focusing on the regression model in the log-spectral scale (36), Fan and Zhang (2004) proposed a testing method for assessing a parametric model for the logspectral…”
Nonparametric Transition-Based Tests for Jump Diffusions (2009, unknown): “(4.4) The biases in the asymptotic null distribution can be reduced by incorporating a bias-reduction technique (Fan and Zhang 2004).”
Statistical testing of covariate effects in conditional copula models (2013, methodology): “, 2001), spectral density (Fan and Zhang, 2004), additive models (Fan and Jiang, 2005) and single-index models (Zhang et al.”
TSclust: An R Package for Time Series Clustering (2014, background): “The ﬁrst alternative comes from the generalized likelihood ratio test approach introduced by Fan and Zhang (2004) to check whether the density of an observed time series belongs to a parametric family.”
Nonparametric Inferences for Additive Models (2005, methodology): “Recently, Fan and Zhang (2004) advocated using the bias-reduction method in the study of testing problems for spectral density.”
Nonparametric inference with generalized likelihood ratio tests (2007, methodology): “For example, Fan and Zhang (2004) studied the GLR tests using sieve empirical likelihood, which aims to construct the GLR test to adapt to unknown error distributions, including the conditional heteroscedasticity.”
Bootstrap tests for simple structures in nonparametric time series regression (2008, methodology): “In fact, we can choose w(·) ≡ 1 for testing Hp. Note that Fan and Zhang (2004) Subsection 3.1 presents an interesting interpretation of the above bias correction via smoothing in terms of reparametrization.”