Cluster non‐Gaussian functional data

Biometrics ◽  
2020 ◽  
Author(s):  
Qingzhi Zhong ◽  
Huazhen Lin ◽  
Yi Li
Keyword(s):  
Functional Data ◽  
Non Gaussian

scholarly journals Sign, Wilcoxon and Mann-Whitney Tests for Functional Data: An Approach Based on Random Projections

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 44
Author(s):  
Rafael Meléndez ◽  
Ramón Giraldo ◽  
Víctor Leiva
Keyword(s):  
Functional Data ◽  
Real Data ◽  
Nonparametric Methods ◽  
Testing Hypothesis ◽  
Random Projections ◽  
Data Set ◽  
Standard Case ◽  
Regression Methods ◽  
Two Samples ◽  
Non Gaussian

Sign, Wilcoxon and Mann-Whitney tests are nonparametric methods in one or two-sample problems. The nonparametric methods are alternatives used for testing hypothesis when the standard methods based on the Gaussianity assumption are not suitable to be applied. Recently, the functional data analysis (FDA) has gained relevance in statistical modeling. In FDA, each observation is a curve or function which usually is a realization of a stochastic process. In the literature of FDA, several methods have been proposed for testing hypothesis with samples coming from Gaussian processes. However, when this assumption is not realistic, it is necessary to utilize other approaches. Clustering and regression methods, among others, for non-Gaussian functional data have been proposed recently. In this paper, we propose extensions of the sign, Wilcoxon and Mann-Whitney tests to the functional data context as methods for testing hypothesis when we have one or two samples of non-Gaussian functional data. We use random projections to transform the functional problem into a scalar one, and then we proceed as in the standard case. Based on a simulation study, we show that the proposed tests have a good performance. We illustrate the methodology by applying it to a real data set.

Cluster analysis with regression of non‐Gaussian functional data on covariates

2021 ◽  
Author(s):  
Jiakun Jiang ◽  
Huazhen Lin ◽  
Heng Peng ◽  
Gang‐Zhi Fan ◽  
Yi Li
Keyword(s):  
Cluster Analysis ◽  
Functional Data ◽  
Non Gaussian

STATEMENT OF THE PROBLEM ON SYNTHESIS OF OPTIMAL EVALUATION OF SIGNAL PARAMETERS IN NON-GAUSSIAN NOISE

2012 ◽  
Vol 71 (17) ◽  
pp. 1541-1555
Author(s):  
V. A. Baranov ◽  
S. V. Baranov ◽  
A. V. Nozdrachev ◽  
A. A. Rogov
Keyword(s):  
Gaussian Noise ◽  
Signal Parameters ◽  
Non Gaussian ◽  
Optimal Evaluation
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