Discrimination of Locally Stationary Time Series Based on the Excess Mass Functional

DOI10.1198/016214505000000899zbMath1118.62358OpenAlexW1994374170MaRDI QIDQ5754935

Gabriel Chandler, Wolfgang Polonik

Publication date: 20 August 2007

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/016214505000000899

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

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Robust functional supervised classification for time series ⋮ Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes ⋮ Testing for Stationarity in Multivariate Locally Stationary Processes ⋮ An efficient estimator for locally stationary Gaussian long-memory processes ⋮ Order selection for heteroscedastic autoregression: a study on concentration ⋮ Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity ⋮ A generalized ARFIMA model with smooth transition fractional integration parameter ⋮ Time-varying persistence of inflation: evidence from a wavelet-based approach ⋮ A similarity measure for second order properties of non-stationary functional time series with applications to clustering and testing ⋮ Large-deviation results for discriminant statistics of Gaussian locally stationary processes ⋮ Mode Identification of Volatility in Time-Varying Autoregression ⋮ Estimation of regression contour clusters -- an application of the excess mass approach to regression ⋮ Detecting relevant changes in the mean of nonstationary processes -- a mass excess approach ⋮ Estimation of slowly time-varying trend function in long memory regression models ⋮ Minimum distance estimation of locally stationary moving average processes

Uses Software

• SLEX