Reliability evaluation of key hydraulic components for actuators of FAST based on small sample test

2017 ◽  
Vol 18 (11) ◽  
pp. 1561-1566 ◽  
Author(s):  
Ming Zhu ◽  
Jingyi Zhao ◽  
Qiming Wang
Keyword(s):  
Reliability Evaluation ◽  
Small Sample ◽  
Sample Test

A Small Sample Test of the Factor Structure of Postural Movement and Bilateral Motor Integration Using Structural Equation Modeling

2012 ◽  
Vol 115 (2) ◽  
pp. 544-557 ◽  
Author(s):  
Chin-Kai Lin ◽  
Chung-Hui Lin ◽  
Pei-Fang Wu ◽  
Huey-Min Wu ◽  
Yuh-Yih Wu ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Factor Structure ◽  
Structural Equation ◽  
Small Sample ◽  
Equation Modeling ◽  
Sample Test ◽  
Postural Movement

Inference for Income Mobility Measures in the Presence of Spatial Dependence

2019 ◽  
Vol 43 (1-2) ◽  
pp. 10-39 ◽  
Author(s):  
Wei Kang ◽  
Sergio J. Rey
Keyword(s):  
Spatial Dependence ◽  
Transition Probability ◽  
Asymptotic Variance ◽  
Transition Probability Matrix ◽  
Small Sample ◽  
Income Mobility ◽  
Type I ◽  
Test Statistics ◽  
Regional Income ◽  
Sample Test

Income mobility measures provide convenient and concise ways to reveal the dynamic nature of regional income distributions. Statistical inference about these measures is important especially when it comes to a comparison of two regional income systems. Although the analytical sampling distributions of relevant estimators and test statistics have been asymptotically derived, their properties in small sample settings and in the presence of contemporaneous spatial dependence within a regional income system are underexplored. We approach these issues via a series of Monte Carlo experiments that require the proposal of a novel data generating process capable of generating spatially dependent time series given a transition probability matrix and a specified level of spatial dependence. Results suggest that when sample size is small, the mobility estimator is biased while spatial dependence inflates its asymptotic variance, raising the Type I error rate for a one-sample test. For the two-sample test of the difference in mobility between two regional economic systems, the size tends to become increasingly upward biased with stronger spatial dependence in either income system, which indicates that conclusions about differences in mobility between two different regional systems need to be drawn with caution as the presence of spatial dependence can lead to false positives. In light of this, we suggest adjustments for the critical values of relevant test statistics.

scholarly journals Frequency-Domain Evidence for Climate Change

Econometrics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 28
Author(s):  
Manveer Kaur Mangat ◽  
Erhard Reschenhofer
Keyword(s):  
Climate Change ◽  
Frequency Domain ◽  
Long Range ◽  
Low Frequency ◽  
Frequency Domain Analysis ◽  
Conclusive Evidence ◽  
Small Sample ◽  
Sample Test ◽  
The Time Domain ◽  
Memory Parameter

The goal of this paper is to search for conclusive evidence against the stationarity of the global air surface temperature, which is one of the most important indicators of climate change. For this purpose, possible long-range dependencies are investigated in the frequency-domain. Since conventional tests of hypotheses about the memory parameter, which measures the degree of long-range dependence, are typically based on asymptotic arguments and are therefore of limited practical value in case of small or medium sample sizes, we employ a new small-sample test as well as a related estimator for the memory parameter. To safeguard against false positive findings, simulation studies are carried out to examine the suitability of the employed methods and hemispheric datasets are used to check the robustness of the empirical findings against low-frequency natural variability caused by oceanic cycles. Overall, our frequency-domain analysis provides strong evidence of non-stationarity, which is consistent with previous results obtained in the time domain with models allowing for stochastic or deterministic trends.

An Investigation into the Small Sample Properties of a Two Sample Test of Lehmann's

1968 ◽  
Vol 63 (321) ◽  
pp. 345 ◽  
Author(s):  
A. A. Afifi ◽  
R. M. Elashoff ◽  
P. G. Langley
Keyword(s):  
Small Sample ◽  
Sample Test ◽  
Small Sample Properties
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