scholarly journals Moderate deviations for the Durbin–Watson statistic related to the first-order autoregressive process

2014 ◽  
Vol 18 ◽  
pp. 308-331 ◽  
Author(s):  
S. Valère Bitseki Penda ◽  
Hacène Djellout ◽  
Frédéric Proïa
Keyword(s):  
Autoregressive Process ◽  
Moderate Deviations ◽  
First Order ◽  
Watson Statistic

scholarly journals Robust Trend Estimation for AR(1) Disturbances

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Roland Fried ◽  
Ursula Gather
Keyword(s):  
Robust Estimation ◽  
Linear Trend ◽  
Autoregressive Process ◽  
Trend Estimation ◽  
First Order ◽  
Repeated Median

We discuss the robust estimation of a linear trend if the noise follows an autoregressive process of first order. We find the ordinary repeated median to perform well except for negative correlations. In this case it can be improved by a Prais-Winsten transformation using a robust autocorrelation estimator.

A new mixed first-order integer-valued autoregressive process with Poisson innovations

2020 ◽  
Author(s):  
Daniel L. R. Orozco ◽  
Lucas O. F. Sales ◽  
Luz M. Z. Fernández ◽  
André L. S. Pinho
Keyword(s):  
Autoregressive Process ◽  
First Order

Effect of Preview on Digital Pursuit Control Performance

1978 ◽  
Vol 20 (3) ◽  
pp. 371-377 ◽  
Author(s):  
Tarald O. Kvålseth
Keyword(s):  
Human Performance ◽  
Autoregressive Process ◽  
Control Task ◽  
Error Performance ◽  
First Order ◽  
Pursuit Control ◽  
Significant Performance ◽  
Rms Error ◽  
Different Characteristics ◽  
Reference Input

The effect of preview on human performance during a digital pursuit control task was analyzed for different preview spans and different characteristics of the reference input. The data from eight subjects revealed that the RMS error performance improved substantially from the case of no preview to that of one preview point, while the use of additional preview points did not result in any further significant performance improvement. The benefit of preview was most clearly established when the reference input was generated by a purely random process as opposed to a first-order autoregressive process (with the parameter α = 0.95). The RMS error increased when the variance of the reference input increased. The error appeared to be normally distributed with a tendency towards a negative bias.

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