Memory-universal prediction of stationary random processes
From MaRDI portal
Publication:4392439
DOI10.1109/18.650998zbMath0938.62106OpenAlexW2137633226MaRDI QIDQ4392439
Elias Masry, Dharmendra S. Modha
Publication date: 13 June 2000
Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)
Full work available at URL: https://semanticscholar.org/paper/829ad0fe3b10ff389d34b17a855fdfae3f8b8a84
neural networksmodel selectionMarkov processesBernstein inequalityLegendre polynomialstime series predictionmixing processescomplexity regularizationleast-squares lossmemory-universal prediction
Inference from stochastic processes and prediction (62M20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items
Limit theorems for a class of identically distributed random variables. ⋮ Model selection for (auto-)regression with dependent data ⋮ Simpler PAC-Bayesian bounds for hostile data ⋮ A CONSISTENT NONPARAMETRIC TEST FOR CAUSALITY IN QUANTILE ⋮ Discrepancy-based theory and algorithms for forecasting non-stationary time series ⋮ Model selection in reinforcement learning ⋮ Model selection for weakly dependent time series forecasting ⋮ Model selection and sharp asymptotic minimaxity ⋮ Regularized least-squares regression: learning from a sequence ⋮ Empirical risk minimization and complexity of dynamical models ⋮ Application of data compression methods to nonparametric estimation of characteristics of discrete-time stochastic processes ⋮ Learning from dependent observations ⋮ Finite sample properties of system identification of ARX models under mixing conditions ⋮ Prediction of time series by statistical learning: general losses and fast rates ⋮ Nonlinear set membership prediction of river flow ⋮ Accumulative prediction error and the selection of time series models ⋮ Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise ⋮ Adaptive estimation in autoregression or \(\beta\)-mixing regression via model selection
