Local linear quantile estimation for nonstationary time series
By Zhou Zhou, Wei Biao Wu. 2009
151 citations recorded by the source corpus.
We consider estimation of quantile curves for a general class of nonstationary processes. Consistency and central limit results are obtained for local linear quantile estimates under a mild short-range dependence condition. Our results are applied to environmental data sets. In particular, our results can be used to address the problem of whether climate variability has changed, an important problem raised by IPCC (Intergovernmental Panel on Climate Change) in 2001.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d780858602290704b30c3ecbdb5057af0adc20c2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- Zhou Zhou
- Michael Vogt
- Holger Dette
- Stanislav Volgushev
- Wei Biao Wu
- Marc Hallin
- Tobias Kley
- Jiahua Chen
In Statistics and Probability
- Michael Vogt
- Stanislav Volgushev
- Wei Biao Wu
- Zhou Zhou
- Marc Hallin
- Holger Dette
- Tobias Kley
- Jiahua Chen
Citations in the corpus
- Quantile spectral analysis for locally stationary time series (2014, background): “of Dahlhaus and Subba Rao (2006) or Dahlhaus and Polonik (2006) are locally strictly stationary in the new sense; see Section 5.1 for details. Similar results certainly also could be obtained for the Zhou and Wu (2009a, b) concept, but they are less obvious and, in order to not overload the paper, w”
- Nonparametric regression for locally stationary time series (2012, methodology): “Before we turn to the analysis of the tvNAR process, we compare it to the framework of Zhou and Wu [24] and Zhou [23].”
- Heteroscedasticity and Autocorrelation Robust Structural Change Detection (2013, background): “Observe that if r = 0, then {ei} is a locally stationary times series in the sense of Zhou and Wu (2009).”
- Simultaneous inference of linear models with time varying coefficients (2010, background): “(b) Following the chaining argument in lemma 6 of Zhou and Wu (2009) as well as the proof of lemma 3, we have ‖ sup t∈I |Λ̂.t/−E{Λ̂.t/}|‖=O.m1=2n−1=2τ−1n /: It is easily seen that lemmas 4 and 5 hold uniformly on I.”
- Quantile and quantile-function estimations under density ratio model (2013, background): “See Wu (2005) and Zhou and Wu (2009) for recent examples.”
- Locally Stationary Processes (2011, unknown): “(2009) and Zhou and Wu (2009).”