Results 361 to 370 of about 21,619,172 (379)

IDENTIFICATION IN MODELS WITH AUTOREGRESSIVE ERRORS

Journal of Econometrics, 1981
Abstract Consider the model A(L)x t =B(L)y t +C(L)z t =u t , t=1,…,T , where A(L)=(B(L):C(L)) is a matrix of polynomials in the lag operator so that L r x t =x t−r , and y t is a vector of n endogenous variables, B(L)= ∑ s=0 k B s L s B 0 I
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Autoregressive model fitting for control

Annals of the Institute of Statistical Mathematics, 1971
The use of a multidimensional extension of the minimum final prediction error (FPE) criterion which was originally developed for the decision of the order of one-dimensional autoregressive process [1] is discussed from the standpoint of controller design.
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Testing Normality in Autoregressive Models

Biometrika, 1985
SUMMARY It is shown that any test of normality computed from autoregressive residuals has the same limiting null distribution as for the standard case of independent, identically distributed observations with estimated parameters. Some numerical results are given to indicate that this approximation is acceptable for sample size 20 in first- and second-
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Autoregressive Models

2021
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Estimation of partially linear single-index spatial autoregressive model

Statistical Papers, 2019
Suli Cheng, Jianbao Chen
semanticscholar   +1 more source

Provably Secure Generative Steganography Based on Autoregressive Model

International Workshop on Digital Watermarking, 2018
Kuan Yang, Kejiang Chen, Weiming Zhang, Nenghai Yu   +3 more
semanticscholar   +1 more source

A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances

, 1998
H. Kelejian, I. Prucha
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Autoregressive Models

2016
David L. Olson, Desheng Wu
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Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition

Neural Processing Letters, 2016
Yong Zhang, Bo Liu, Xiaomin Ji, Dan Huang   +3 more
semanticscholar   +1 more source

Autoregressive Probability Models

2003
Proceeding from the interpretation of a given sample as a realization from some Haavelmo distribution,discussed the role of probability models as a basic tool for modelling this unknown distribution. To be empirically acceptable, such a probability model should summarize all relevant sample information, i.e., account for important statistical ...
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