Countable alphabet stationary processes with at least one memory word and intermittent estimation with universal rates

DOI10.30757/alea.v21-42MaRDI QIDQ6634799

Gusztáv Morvai, Benjamin Weiss

Publication date: 8 November 2024

Published in: ALEA. Latin American Journal of Probability and Mathematical Statistics (Search for Journal in Brave)

zbMATH Keywords

stationary processesnonparametric estimation

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Stationary stochastic processes (60G10) Prediction theory (aspects of stochastic processes) (60G25)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Weakly universally consistent static forecasting of stationary and ergodic time series via local averaging and least squares estimates
• Nonparametric estimation of conditional probability densities and expectations of stationary processes: Strong consistency and rates
• The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
• On estimating the memory for finitarily Markovian processes
• Efficient prediction for linear and nonlinear autoregressive models
• Discrimination between B-processes is impossible
• Prediction of random sequences and universal coding
• Nonparametric curve estimation from time series
• Guessing the next output of a stationary process
• On prediction of individual sequences
• Prediction for discrete time series
• The consistency of the BIC Markov order estimator.
• Variable length Markov chains
• On universal algorithms for classifying and predicting stationary processes
• Introduction to empirical processes and semiparametric inference
• Limitations on intermittent forecasting
• On classifying processes
• Forward estimation for ergodic time series
• Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
• ON SEQUENTIAL ESTIMATION AND PREDICTION FOR DISCRETE TIME SERIES
• Context tree estimation for not necessarily finite memory processes, via BIC and MDL
• Order Estimation of Markov Chains
• Sequential Prediction of Unbounded Stationary Time Series
• Sequential Procedures for Aggregating Arbitrary Estimators of a Conditional Mean
• Estimating the Lengths of Memory Words
• Limits to consistent on-line forecasting for ergodic time series
• Large-scale typicality of Markov sample paths and consistency of MDL order estimators
• On optimal sequential prediction for general processes
• Universal prediction
• Universal schemes for learning the best nonlinear predictor given the infinite past and side information
• The strong law of large numbers for sequential decisions under uncertainty
• The optimal error exponent for Markov order estimation
• Intermittent estimation for finite alphabet finitarily Markovian processes with exponential tails
• Weakly Universally Consistent Forecasting of Stationary and Ergodic Time Series
• Computational Limits to Nonparametric Estimation for Ergodic Processes
• Inequalities for the $r$th Absolute Moment of a Sum of Random Variables, $1 \leqq r \leqq 2$
• Universal Tests for Memory Words
• Asymptotic Nonparametric Statistical Analysis of Stationary Time Series

This page was built for publication: Countable alphabet stationary processes with at least one memory word and intermittent estimation with universal rates