Rank order statistics for time series models

DOI10.1007/BF00052341zbMath0682.62064OpenAlexW2082652310MaRDI QIDQ1824331

Lanh Tat Tran

Publication date: 1988

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00052341

zbMATH Keywords

time seriesinvariance principlesstrong mixingphi-mixingone-sample rank order statisticsestimation of the center of symmetrytests for serial dependence

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional limit theorems; invariance principles (60F17)

Related Items (8)

Order statistics for nonstationary time series ⋮ Distribution-free tests against serial dependence: Signed or unsigned ranks? ⋮ NONPARAMETRIC TESTS FOR SERIAL DEPENDENCE ⋮ Time series analysis via rank order theory: Signed-rank tests for ARMA models ⋮ Rank statistics for serial dependence ⋮ Asymptotic distribution of rank statistics under dependencies with multivariate application ⋮ A Smooth Block Bootstrap for Statistical Functionals and Time Series ⋮ Rates of convergence for linear rank statistics with dependent data

Cites Work

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• Some mixing properties of time series models
• Linear serial rank tests for randomness against ARMA alternatives
• On the \(\varphi\)-mixing condition for stationary random sequences
• Empirical distribution functions and functions of order statistics for mixing random variables
• Weak convergence of generalized empirical processes relative to \(d_q\) under strong mixing
• A functional law of the iterated logarithm for empirical distribution functions of weakly dependent random variables
• On deviations between empirical and quantile processes for mixing random variables
• RANK TESTS FOR SERIAL DEPENDENCE
• Almost sure invariance principles for partial sums of weakly dependent random variables
• Robust Estimation of the First-Order Autoregressive Parameter
• Estimates of Location Based on Rank Tests
• Some Limit Theorems for Stationary Processes

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