Inference for modulated stationary processes

DOI10.3150/11-BEJ399zbMath1259.62077arXiv1302.0114WikidataQ36714461 ScholiaQ36714461MaRDI QIDQ1940756

Xiaoye Li, Zhibiao Zhao

Publication date: 7 March 2013

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1302.0114

zbMATH Keywords

confidence intervalslong-run variancewild bootstrapself-normalizationchange-point analysisstrong invariance principle

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Non-Markovian processes: estimation (62M09) Point estimation (62F10) Nonparametric statistical resampling methods (62G09) Monte Carlo methods (65C05) Functional limit theorems; invariance principles (60F17) Non-Markovian processes: hypothesis testing (62M07)

Related Items (4)

STUDENTIZING WEIGHTED SUMS OF LINEAR PROCESSES ⋮ Rank-based change-point analysis for long-range dependent time series ⋮ Change-point inference for high-dimensional heteroscedastic data ⋮ A distribution free test for changes in the trend function of locally stationary processes

Uses Software

• Resampling Stats
• astsa

Cites Work

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