Inference in stochastic processes-III

DOI10.1007/BF00533944zbMath0209.20404MaRDI QIDQ5609809

M. M. Rao

Publication date: 1967

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

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20)

Related Items

Harmonizable processes and inference: Unbiased prediction for stochastic flows, Martingale Representation in Uniformly Convex Spaces, A Factorization problem and the problem of predicting non-stationary vector-valued stochastic processes, Sampling Theorems for Nonstationary Random Processes, Contractive projections and prediction operators, Some sampling properties of empirical characteristic functions viewed as harmonizable stochastic processes, Unnamed Item, Almost everywhere convergence of prediction sequence in L p (1 < p < ?), Characterization of Conditional Expectation Operators for Banach-Valued Functions, Best approximants in L ? -spaces, Counterexamples to results of M.M. Rao, Connection between the differentL s -predictions with applications, Best ?- and N ?-approximants in Orlicz spaces of vector valued functions, The life and work of M. M. Rao, Nonlinear prediction of generalized random processes, Bimeasures and sampling theorems for weakly harmonizable processes, \(L_ p\)-linear regression, consistency and significative regression in median

Cites Work

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• Abstract Lebesgue-Radon-Nikodym theorems
• Contractive projections in \(L_ p\)-spaces
• Abstract martingale convergence theorems
• A Problem in Optimum Filtering with Finite Data
• Martingale Formulation of Ergodic Theorems
• A Sampling Theorem for Stationary (Wide Sense) Stochastic Processes
• On the eigenvalues of positive operators
• Inference in stochastic processes. II
• Bayes Estimation with Convex Loss
• Inference in Stochastic Processes. I
• Sampling theorems and bases in a Hilbert space
• Stationary Processes and Prediction Theory. (AM-44)
• Extraction and Detection Problems and Reproducing Kernel Hilbert Spaces
• On linear statistical problems in stochastic processes
• Linear Functionals on Certain Banach Spaces
• Quasi-Complements and Closed Projections in Reflexive Banach Spaces
• Stochastic processes and statistical inference