Uniform ergodicity and strong mixing

DOI10.1007/BF00532465zbMath0231.60050OpenAlexW2029134630MaRDI QIDQ5639101

Murray Rosenblatt

Publication date: 1972

Published in: Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00532465

Mathematics Subject Classification ID

Stationary stochastic processes (60G10) Measure-preserving transformations (28D05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Continuous-time Markov processes on discrete state spaces (60J27)

Related Items (13)

Some Characteristics of the Conditional Set-Indexed Empirical Process Involving Functional Ergodic Data ⋮ Real-time estimation for functional stochastic regression models ⋮ Generalization performance of Lagrangian support vector machine based on Markov sampling ⋮ Online regularized pairwise learning with non-i.i.d. observations ⋮ Generalization and learning rate of multi-class support vector classification and regression ⋮ Nonparametric \(M\)-estimation for right censored regression model with stationary ergodic data ⋮ Central limit theorem by moments ⋮ Generalization ability of online pairwise support vector machine ⋮ Optimal asymptotic MSE of kernel regression estimate for continuous time processes with missing at random response ⋮ Uniform limit theorems for a class of conditional \(Z\)-estimators when covariates are functions ⋮ Optimal rate for support vector machine regression with Markov chain samples ⋮ Misspecified models with dependent observations ⋮ Generalization performance of Gaussian kernels SVMC based on Markov sampling

Cites Work

• Unnamed Item
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• Conditional Probability Operators
• Some Limit Theorems for Stationary Processes

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