Two-stage procedure in a first-order autoregressive process and comparison with a purely sequential procedure

2022 ◽  
pp. 1-16
Author(s):  
Soudabe Sajjadipanah ◽  
Eisa Mahmoudi ◽  
Mohammadsadegh Zamani
Keyword(s):  
Autoregressive Process ◽  
Sequential Procedure ◽  
Two Stage ◽  
First Order ◽  
Purely Sequential

Evaluation of sampling alternatives to quantify tree leaf area

2003 ◽  
Vol 33 (1) ◽  
pp. 82-95 ◽  
Author(s):  
H Temesgen
Keyword(s):  
Leaf Area ◽  
Poor Performance ◽  
Tree Crown ◽  
Two Stage ◽  
Zero Bias ◽  
Probability Sampling ◽  
First Order ◽  
Unequal Probability Sampling ◽  
Unequal Probability ◽  
Tree Leaf

High within- and among-tree crown variation have contributed to the difficulty of tree-crown sampling and single-tree leaf area (area available for photosynthesis) estimation. Using reconstructed trees, simulations were used to compare five sampling designs for bias, mean square error (MSE), and distribution of the estimates. All sampling designs showed nearly zero bias. For most sample trees, stratified random sampling resulted in the lowest MSE values, followed by ellipsoidal, two-stage systematic, simple random, and then by two-stage unequal probability sampling. The poor performance of two-stage unequal probability sampling can be ascribed to the unequal probability of inclusion of first-order branches and twigs.

A Test of a Two-Stage Model of the Evaluation of Hypotheses from Quantified First-Order Predicate Logic in Information Use Tasks

1992 ◽  
Vol 71 (3_suppl) ◽  
pp. 1091-1104 ◽  
Author(s):  
Peter E. Langford ◽  
Robert Hunting
Keyword(s):  
Predicate Logic ◽  
Information Use ◽  
Stage Model ◽  
Two Stage ◽  
First Order ◽  
Alternative Hypotheses ◽  
Age Range ◽  
Hypothesis Evaluation ◽  
Universe Of Discourse ◽  
First Order Predicate Logic

480 adolescents and young adults between the ages of 12 and 29 years participated in an experiment in which they were asked to evaluate hypotheses from quantified first-order predicate logic specifying that certain classes of event were necessarily, possibly, or certainly not included within a universe of discourse. Results were used to test a two-stage model of performance on hypothesis evaluation tasks that originated in work on the evaluation of conditionals. The two-stage model, unlike others available, successfully predicted the range of patterns of reply observed. In dealing with very simple hypotheses subjects in this age range tended not to make use of alternative hypotheses unless these were explicitly or implicitly suggested to them by the task. This tells against complexity of hypothesis as an explanation of the reluctance to use alternative hypotheses in evaluating standard conditionals.

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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