Multiple comparison and selection of location parameters of exponential populations

1990 ◽  
Author(s):  
Cheuk-key, Allen Ng
Keyword(s):  
Multiple Comparison ◽  
Location Parameters ◽  
Exponential Populations ◽  
Selection Of

scholarly journals Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Yongshao Ren ◽  
Duan Li ◽  
Logan Voss ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Energy Operator ◽  
Burst Suppression ◽  
Multiple Comparison ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Nonlinear Energy ◽  
Selection Of

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

Selection of an Adaptive Test Statistic for Use with Multiple Comparison Analyses of Neuroimaging Data

NeuroImage ◽  
2000 ◽  
Vol 12 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Federico Turkheimer ◽  
Karen Pettigrew ◽  
Louis Sokoloff ◽  
Carolyn Beebe Smith ◽  
Kathleen Schmidt
Keyword(s):  
Multiple Comparison ◽  
Test Statistic ◽  
Adaptive Test ◽  
Neuroimaging Data ◽  
Selection Of

scholarly journals Comparative Analysis of Classical and Modified Moses Test

2021 ◽  
Vol 3 (1) ◽  
pp. 47-54
Author(s):  
Nor Adilah Mohamad Nor Azman ◽  
Nor Aishah Ahad ◽  
Friday Zinzendoff Okwonu
Keyword(s):  
Comparative Analysis ◽  
Random Process ◽  
Random Selection ◽  
Nonparametric Method ◽  
Test Statistic ◽  
Dispersion Parameters ◽  
Data Set ◽  
Ranking Procedure ◽  
Location Parameters ◽  
Selection Of

Moses test is a nonparametric method to test the equality of two dispersion parameters. The Moses test does not assume equality of location parameters, and this fact gives the test wider applicability. However, this test is inefficient since different people applying the test will obtain different values because of a random process. One sub-division may lead to significant results where another does not. To overcome the problem of uniqueness of the result, this study proposed to modify the random selection of the observation for the subsamples based on the ranking procedure to lead for a unique result for each solution. The original and modified Moses test were tested on the same data set. The finding shows that the result for both tests is similar in terms of decision and conclusion. The analysis revealed that the modified Moses test based on ranking approach has a smaller sum of squared values compared to the original Moses test. Thus, the variability of data for each subsample is decreased as well. Ranking approach can be used as an alternative to replacing the random procedure of selecting observations for subsample to overcome the problem of uniqueness in the test statistic.

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