Hypothesis testing for two population means: parametric or\n non-parametric test? [PDF]
Journal of Statistical Computation and Simulation, 2018 The parametric Welch t-test and the non-parametric Wilcoxon–Mann–Whitney, empirical and exponential empirical likelihood tests are commonly used for hypothesis testing of two population means.
Michail Tsagris +3 more
semanticscholar +7 more sources
Hypothesis testing for an entangled state produced by spontaneous parametric down-conversion [PDF]
Physical Review A, 2006 Generation and characterization of entanglement are crucial tasks in quantum information processing. A hypothesis testing scheme for entanglement has been formulated.
Masahito Hayashi +5 more
semanticscholar +7 more sources
Keyed Non-parametric Hypothesis Tests [PDF]
International Conference on Network and System Security, 2019 The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention.
Yao Cheng +4 more
semanticscholar +7 more sources
Non-parametric hypothesis testing to model some cancers based on goodness of fit
AIMS Mathematics, 2022 By observing the failure behavior of the recorded survival data, we aim to compare the different processing approaches or the effectiveness of the devices or systems applied in this non-parametric statistical test.
M. E. Bakr +2 more
doaj +3 more sources
On Random Distortion Testing Based Sequential Non-Parametric Hypothesis Testing [PDF]
2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2018 In this work, we propose a new method for sequential binary hypothesis testing. The approach is non-parametric in the sense that it does not assume any knowledge of signal distributions under each hypothesis.
Prashant Khanduri +3 more
semanticscholar +4 more sources
Is non-parametric hypothesis testing model robust for statistical fault localization? [PDF]
Information and Software Technology, 2009 Fault localization is one of the most difficult activities in software debugging. Many existing statistical fault-localization techniques estimate the fault positions of programs by comparing the program feature spectra between passed runs and failed runs.
Zhenyu Zhang +4 more
semanticscholar +5 more sources
Truncated Sequential Non-Parametric Hypothesis Testing Based on Random Distortion Testing [PDF]
IEEE Transactions on Signal Processing, 2019 In this paper, we propose a new algorithm for sequential non-parametric hypothesis testing based on Random Distortion Testing (RDT). The data-based approach is non-parametric in the sense that the underlying signal distributions under each hypothesis are
Prashant Khanduri +3 more
semanticscholar +5 more sources
Non-Parametric Hypothesis Testing for Unknown Aged Class of Life Distribution Using Real Medical Data [PDF]
Axioms, 2023 Over the last few decades, the statisticians and reliability analysts have looked at putting exponentiality to the test using the Laplace transform technique.
Mahmoud. E. Bakr +1 more
doaj +2 more sources
Non-parametric Hypothesis Tests for Distributional Group Symmetry [PDF]
, 2023 Symmetry plays a central role in the sciences, machine learning, and statistics. For situations in which data are known to obey a symmetry, a multitude of methods that exploit symmetry have been developed. Statistical tests for the presence or absence of general group symmetry, however, are largely non-existent.
Kenny Chiu, Benjamin Bloem-Reddy
openalex +3 more sources
Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping [PDF]
PeerJ Computer Science, 2021 This paper proposes a computational framework for automated, landmark-free hypothesis testing of 2D contour shapes (i.e., shape outlines), and implements one realization of that framework.
Todd C. Pataky +3 more
doaj +3 more sources