Complete convergence for \(\alpha{}\)-mixing sequences
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Publication:1209704
DOI10.1016/0167-7152(93)90131-2zbMath0787.60039OpenAlexW2072002510MaRDI QIDQ1209704
Publication date: 16 May 1993
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0167-7152(93)90131-2
Related Items (22)
Bootstrap for the sample mean and forU-statistics of mixing and near-epoch dependent processes ⋮ Asymptotic properties for LS estimators in EV regression model with dependent errors ⋮ Asymptotic Normality of Estimators in Heteroscedastic Semi-Parametric Model with Strong Mixing Errors ⋮ The consistency and convergence rate for the nearest neighbor density estimator based on φ-mixing random samples ⋮ General theorems on exponential and Rosenthal's inequalities and on complete convergence ⋮ The bootstrap for empirical processes based on stationary observations ⋮ Weighted sums of strongly mixing random variables with an application to nonparametric regression ⋮ CONVERGENCE RATES OF SUMS OF α-MIXING TRIANGULAR ARRAYS: WITH AN APPLICATION TO NONPARAMETRIC DRIFT FUNCTION ESTIMATION OF CONTINUOUS-TIME PROCESSES ⋮ Central limit theorems for LS estimators in the EV regression model with dependent measure\-ments ⋮ Complete convergence theorems for \(L^{p}\)-mixingales. ⋮ Convergence rates in the law of large numbers for arrays of martingale differences ⋮ Jackknife empirical likelihood of error variance in partially linear varying-coefficient errors-in-variables models ⋮ On complete convergence for strong mixing sequences ⋮ Wavelet Estimator in Nonparametric Regression Model with Dependent Error’s Structure ⋮ Optimal change-point estimation in time series ⋮ Asymptotic properties for the estimators in heteroscedastic semiparametric EV models with α-mixing errors ⋮ Convergence rates in the law of large numbers for arrays of Banach valued martingale differences ⋮ A theory of hyperfinite processes: The complete removal of individual uncertainty via exact LLN ⋮ Bootstrapping the sample means for stationary mixing sequences ⋮ Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model ⋮ Complete convergence theorems for extended negatively dependent random variables ⋮ Self-normalized central limit theorem for sums of weakly dependent random variables
Cites Work
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- Convergence rates in the strong law for bounded mixing sequences
- Central limit theorems under weak dependence
- Convergence rates and r-quick versions of the strong law for stationary mixing sequences
- A functional central limit theorem for strongly mixing sequences of random variables
- SLLN and Convergence Rates for Nearly Orthogonal Sequences of Random Variables
- Convergence rates of the strong law for stationary mixing sequences
- Some Results on the Complete and Almost Sure Convergence of Linear Combinations of Independent Random Variables and Martingale Differences
- The Invariance Principle for Stationary Processes
- Complete Convergence and the Law of Large Numbers
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