A Two-Sample Test Sensitive to Crossing Hazards in Uncensored and Singly Censored Data

Biometrics ◽  
1985 ◽  
Vol 41 (3) ◽  
pp. 643 ◽  
Author(s):  
Donald M. Stablein ◽  
I. A. Koutrouvelis
Keyword(s):  
Censored Data ◽  
Sample Test ◽  
Crossing Hazards

A k-sample test with interval censored data

Biometrika ◽  
2006 ◽  
Vol 93 (2) ◽  
pp. 315-328 ◽  
Author(s):  
Kam-Chuen Yuen ◽  
Jian Shi ◽  
Lixing Zhu
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Sample Test

A Two-Sample Test for Stochastic Ordering with Interval-Censored Data

Biometrics ◽  
1994 ◽  
Vol 50 (1) ◽  
pp. 77 ◽  
Author(s):  
Gina R. Petroni ◽  
Robert A. Wolfe
Keyword(s):  
Censored Data ◽  
Stochastic Ordering ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Sample Test

scholarly journals On the statistical significance of communities from weighted graphs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zengyou He ◽  
Wenfang Chen ◽  
Xiaoqi Wei ◽  
Yan Liu
Keyword(s):  
Censored Data ◽  
Statistical Significance ◽  
Significance Testing ◽  
Edge Weight ◽  
Weighted Graphs ◽  
Logrank Test ◽  
Weighted Networks ◽  
Sample Test ◽  
Definition Of ◽  
New Formulation

AbstractCommunity detection is a fundamental procedure in the analysis of network data. Despite decades of research, there is still no consensus on the definition of a community. To analytically test the realness of a candidate community in weighted networks, we present a general formulation from a significance testing perspective. In this new formulation, the edge-weight is modeled as a censored observation due to the noisy characteristics of real networks. In particular, the edge-weights of missing links are incorporated as well, which are specified to be zeros based on the assumption that they are truncated or unobserved. Thereafter, the community significance assessment issue is formulated as a two-sample test problem on censored data. More precisely, the Logrank test is employed to conduct the significance testing on two sets of augmented edge-weights: internal weight set and external weight set. The presented approach is evaluated on both weighted networks and un-weighted networks. The experimental results show that our method can outperform prior widely used evaluation metrics on the task of individual community validation.

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