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We then show that such moving average processes are equivalent to state space models with stochastic design components.
This paper considers the problem of testing a sub-hypothesis in homoscedastic linear regression models where errors form long memory moving average processes and designs are non-random.
Assuming that between-subject errors are independent while within-subject errors behave according to the stationary first-order autoregressive and moving average processes, analytical optimality results for 3-period designs are established and, as an illustration, numerical details for a number of 4-period cases are tabulated.
The second formulation appears in the form of autoregressive fractionally integrated moving average processes.
Chen et al. [20] also established the following results for moving average processes under NA assumptions.
Inspired by Xiao and Yin [10], we extend this result to moving average processes under φ-mixing random variable.
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This study considers a zero-order autoregressive and integrated moving average process (ARIMA) that contains five common process disturbances.
In this paper, criteria for closed-loop identification of an autoregressive, moving average process with exogenous input (ARMAX) regulated with an arbitrary, rational, polynomial controller are derived.
In this note we show that the locally stationary wavelet process can be decomposed into a sum of signals, each of which follows a moving average process with time-varying parameters.
be the moving average process based on.
Very few results for a moving average process of an array of i.i.d.i.d
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