Locally adaptive fitting of semiparametric models to nonstationary time series.

DOI10.1016/S0304-4149(00)00060-0zbMath1046.62032OpenAlexW2148847609WikidataQ127176372 ScholiaQ127176372MaRDI QIDQ1879516

Michael H. Neumann, Rainer Dahlhaus

Publication date: 22 September 2004

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-4149(00)00060-0

zbMATH Keywords

time serieslocally stationary processesnonparametric curve estimationwavelet estimatorsnonlinear thresholdingpreperiodogram

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Inference from stochastic processes and spectral analysis (62M15) Numerical methods for wavelets (65T60)

Related Items (12)

Semiparametric Estimation by Model Selection for Locally Stationary Processes ⋮ Local inference for locally stationary time series based on the empirical spectral measure ⋮ Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes ⋮ Modelling time-varying first and second-order structure of time series via wavelets and differencing ⋮ Empirical spectral processes for locally stationary time series ⋮ Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity ⋮ Trend locally stationary wavelet processes ⋮ Locally stationary long memory estimation ⋮ A Scale‐space Approach for Detecting Non‐stationarities in Time Series ⋮ Frequency Domain Tests of Semiparametric Hypotheses for Locally Stationary Processes ⋮ Multiscale spectral analysis for detecting short and long range change points in time series ⋮ A likelihood approximation for locally stationary processes

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