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Stochastic processes --- Mathematical statistics --- Random fields --- Champs aléatoires --- Champs aléatoires
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Random fields --- Stochastic processes --- Champs aléatoires --- Processus stochastiques --- Series (mathématiques). --- Processus stochastiques. --- Random fields. --- Stochastic processes. --- Champs aléatoires
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Stochastic processes --- Stochastic partial differential equations. --- Wave equation. --- Random fields. --- Équations aux dérivées partielles stochastiques --- Equation d'onde --- Champs aléatoires --- 51 <082.1> --- Mathematics--Series --- Équations aux dérivées partielles stochastiques. --- Équations d'onde. --- Champs aléatoires. --- Équations aux dérivées partielles stochastiques --- Champs aléatoires --- Random fields --- Stochastic partial differential equations --- Wave equation --- Differential equations, Partial --- Wave-motion, Theory of --- Banach spaces, Stochastic differential equations in --- Hilbert spaces, Stochastic differential equations in --- SPDE (Differential equations) --- Stochastic differential equations in Banach spaces --- Stochastic differential equations in Hilbert spaces --- Fields, Random
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Much of this book is concerned with autoregressive and moving av erage linear stationary sequences and random fields. These models are part of the classical literature in time series analysis, particularly in the Gaussian case. There is a large literature on probabilistic and statistical aspects of these models-to a great extent in the Gaussian context. In the Gaussian case best predictors are linear and there is an extensive study of the asymptotics of asymptotically optimal esti mators. Some discussion of these classical results is given to provide a contrast with what may occur in the non-Gaussian case. There the prediction problem may be nonlinear and problems of estima tion can have a certain complexity due to the richer structure that non-Gaussian models may have. Gaussian stationary sequences have a reversible probability struc ture, that is, the probability structure with time increasing in the usual manner is the same as that with time reversed. Chapter 1 considers the question of reversibility for linear stationary sequences and gives necessary and sufficient conditions for the reversibility. A neat result of Breidt and Davis on reversibility is presented. A sim ple but elegant result of Cheng is also given that specifies conditions for the identifiability of the filter coefficients that specify a linear non-Gaussian random field.
Time-series analysis --- Random fields --- Gaussian processes --- Série chronologique --- Champs aléatoires --- Processus gaussiens --- Série chronologique --- Champs aléatoires --- Probabilities. --- Statistics . --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics
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Random fields --- Champs aléatoires --- Random fields. --- 519.23 --- Fields, Random --- Stochastic processes --- 519.213 --- 519.213 Probability distributions and densities. Normal distribution. Characteristic functions. Measures of dependence. Infinitely divisible laws. Stable laws --- Probability distributions and densities. Normal distribution. Characteristic functions. Measures of dependence. Infinitely divisible laws. Stable laws --- Champs aléatoires
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