Testing for the equivalence of several sets of time series and its multiple comparison procedure

2021 ◽  
pp. 1-16
Author(s):  
Yukio Yanagisawa
Keyword(s):  
Time Series ◽  
Multiple Comparison ◽  
Multiple Comparison Procedure

scholarly journals Effect of phoneme variations on blind reverberation time estimation

Acta Acustica ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Andrea Andrijašević
Keyword(s):  
Time Estimation ◽  
Multiple Comparison ◽  
Reverberation Time ◽  
Impulse Responses ◽  
Estimation Errors ◽  
Significance Level ◽  
Frequency Bands ◽  
Interaction Terms ◽  
Multiple Comparison Procedure

This study focuses on an unexplored aspect of the performance of algorithms for blind reverberation time (T) estimation – on the effect that speech signal’s phonetic content has on the value of the estimate of T that is obtained from the reverberant version of that signal. To this end, the performance of three algorithms is assessed on a set of logatome recordings artificially reverberated with room impulse responses from four rooms, with their T20 value in the [0.18, 0.55] s interval. Analyses of variance showed that the null hypotheses of equal means of estimation errors can be rejected at the significance level of 0.05 for the interaction terms between the factors “vowel”, “consonant”, and “room”, while the results of Tukey’s multiple comparison procedure revealed that there are both some similarities in the behaviour of the algorithms and some differences, where the latter are stemming from the differences in the details of algorithms’ implementation such as the number of frequency bands and whether T is estimated continuously or only on the selected, the so-called speech decay, segments of the signal.

scholarly journals Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data

Stats ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 56-67
Author(s):  
Dewi Rahardja
Keyword(s):  
Real Data ◽  
Multiple Comparisons ◽  
Multiple Comparison ◽  
Omnibus Test ◽  
Binomial Data ◽  
Sequential Tests ◽  
Multiple Sample ◽  
Multiple Comparison Procedure ◽  
Multiple Comparison Procedures ◽  
Equality Test

In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, Šidák, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example.

scholarly journals Resampling-based multiple comparison procedure with application to point-wise testing with functional data

2014 ◽  
Vol 22 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Olga A. Vsevolozhskaya ◽  
Mark C. Greenwood ◽  
Scott L. Powell ◽  
Dmitri V. Zaykin
Keyword(s):  
Functional Data ◽  
Multiple Comparison ◽  
Multiple Comparison Procedure

Evaluation of a weighted multiple comparison procedure

2005 ◽  
Vol 4 (1) ◽  
pp. 7-13 ◽  
Author(s):  
Matthew Somerville ◽  
Timothy Wilson ◽  
Gary Koch ◽  
Peter Westfall
Keyword(s):  
Multiple Comparison ◽  
Multiple Comparison Procedure
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