Simultaneous quantile inference for non-stationary long-memory time series

DOI10.3150/17-BEJ951zbMath1414.62385MaRDI QIDQ1708990

Weichi Wu, Zhou Zhou

Publication date: 27 March 2018

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bj/1522051231

zbMATH Keywords

heterogeneitylong memorysimultaneous confidence bandslocal linear quantile estimation

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05) Order statistics; empirical distribution functions (62G30)

Related Items (2)

Comparing time varying regression quantiles under shift invariance ⋮ Minimum distance estimation of locally stationary moving average processes

Uses Software

• longmemo

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonparametric estimation of time varying parameters under shape restrictions
• Locally stationary long memory estimation
• Empirical spectral processes for locally stationary time series
• An efficient estimator for locally stationary Gaussian long-memory processes
• On linear models with long memory and heavy-tailed errors
• Inference of time-varying regression models
• Nonparametric estimates of regression quantiles and their local Bahadur representation
• Local linear quantile estimation for nonstationary time series
• The empirical process of some long-range dependent sequences with an application to U-statistics
• Reduction principles for quantile and Bahadur-Kiefer processes of long-range dependent linear sequences
• Bahadur representation of sample quantiles for a functional of Gaussian dependent sequences under a minimal assumption
• Nonparametric inference of quantile curves for nonstationary time series
• On the sample mean of locally stationary long-memory processes
• On parameter estimation for locally stationary long-memory processes
• A useful estimate in the multidimensional invariance principle
• Strong invariance principles for partial sums of independent random vectors
• Asymptotic maximal deviation of M-smoothers
• Extensions of results of Komlós, Major, and Tusnády to the multivariate case
• Strong approximation theorems for independent random variables and their applications
• A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
• Limit theorems for functionals of moving averages
• Strong approximation for long memory processes with applications
• Simultaneous confidence bands for linear regression and smoothing
• Statistical inference for time-inhomogeneous volatility models.
• Fractional ARIMA with stable innovations
• Long memory processes and fractional integration in econometrics
• On some global measures of the deviations of density function estimates
• Tail probabilities of the maxima of Gaussian random fields
• Asymptotics of partial sums of linear processes with changing memory parameter
• Normalized least-squares estimation in time-varying ARCH models
• Strong invariance principles for dependent random variables
• Statistical inference for time-varying ARCH processes
• Gaussian approximations for non-stationary multiple time series
• MACROECONOMICS OF SUBSISTENCE POINTS
• An approximation of partial sums of independent RV's, and the sample DF. II
• An approximation of partial sums of independent RV'-s, and the sample DF. I
• Estimation in the Presence of Stochastic Parameter Variation
• ON GENERALIZED FRACTIONAL PROCESSES
• Bias-corrected Confidence Bands in Nonparametric Regression
• INFINITE VARIANCE STABLE ARMA PROCESSES
• Multidimensional Version of a Result of Sakhanenko in the Invariance Principle for Vectors with Finite Exponential Moments. I
• Multidimensional Version of a Result of Sakhanenko in the Invariance Principle for Vectors with Finite Exponential Moments. II
• Multidimensional Version of a Result of Sakhanenko in the Invariance Principle for Vectors with Finite Exponential Moments. III
• Heteroscedasticity and Autocorrelation Robust Structural Change Detection
• Maximum and High Level Excursion of a Gaussian Process with Stationary Increments

This page was built for publication: Simultaneous quantile inference for non-stationary long-memory time series