Application of data compression methods to nonparametric estimation of characteristics of discrete-time stochastic processes
type: Item
instance of: scholarly article
title: Application of data compression methods to nonparametric estimation of characteristics of discrete-time stochastic processes
zbMATH Open document ID: 1213.62058
DOI: 10.1134/S0032946007040096
published in: Problems of Information Transmission
publication date: 2008-09-02T00:00:00Z
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62M09
Mathematics Subject Classification ID: 94B99
zbMATH DE Number: 5319702
author: B. Ya. Ryabko
describes a project that uses: NIST Statistical Test Suite
MaRDI profile type: Publication
cites work: Q3260839
cites work: Using information theory approach to randomness testing
cites work: Universal codes as a basis for time series testing
cites work: Experimental investigation of forecasting methods based on data compression algorithms
cites work: Prediction of random sequences and universal coding
cites work: Universal schemes for learning the best nonlinear predictor given the infinite past and side information
cites work: Limits to consistent on-line forecasting for ergodic time series
cites work: A universal predictor based on pattern matching
cites work: Memory-universal prediction of stationary random processes
cites work: Weakly convergent nonparametric forecasting of stationary time series
cites work: Q3214803
cites work: Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series
cites work: Using literal and grammatical statistics for authorship attribution
cites work: Clustering by Compression
cites work: Q5798359
cites work: Fast adaptive coding algorithm
cites work: The strong ergodic theorem for densities: Generalized Shannon-McMillan- Breiman theorem
cites work: Entropy expressions for multivariate continuous distributions
description: scientific article; zbMATH DE number 5319702