Sequential Tests of the Hypergeometric Distribution

Author(s):  
Jr Meeker ◽  
William Q.
1986 ◽  
Vol 23 (04) ◽  
pp. 1013-1018
Author(s):  
B. G. Quinn ◽  
H. L. MacGillivray

Sufficient conditions are presented for the limiting normality of sequences of discrete random variables possessing unimodal distributions. The conditions are applied to obtain normal approximations directly for the hypergeometric distribution and the stationary distribution of a special birth-death process.


2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hiroaki Murayama ◽  
Taishi Kayano ◽  
Hiroshi Nishiura

Abstract Background In Japan, a part of confirmed patients’ samples have been screened for the variant of concern (VOC), including the variant alpha with N501Y mutation. The present study aimed to estimate the actual number of cases with variant alpha and reconstruct the epidemiological dynamics. Methods The number of cases with variant alpha out of all PCR confirmed cases was estimated, employing a hypergeometric distribution. An exponential growth model was fitted to the growth data of variant alpha cases over fourteen weeks in Tokyo. Results The weekly incidence with variant alpha from 18–24 January 2021 was estimated at 4.2 (95% confidence interval (CI): 0.7, 44.0) cases. The expected incidence in early May ranged from 420–1120 cases per week, and the reproduction number of variant alpha was on the order of 1.5 even under the restriction of contact from January-March, 2021, Tokyo. Conclusions The variant alpha was predicted to swiftly dominate COVID-19 cases in Tokyo, and this has actually occurred by May 2021. Devising the proposed method, any country or location can interpret the virological sampling data.


Biometrika ◽  
1992 ◽  
Vol 79 (1) ◽  
pp. 13-24 ◽  
Author(s):  
JOHN D. EALES ◽  
CHRISTOPHER JENNISON

1999 ◽  
Vol 578 ◽  
Author(s):  
T. Hoc ◽  
C. Rey

AbstractStrain localization in mild steel submitted to a sequential loading paths is investigated at macroscopic, mesoscopic and microscopic scales. The experimental results demonstrate that the morphology of the localization and the nominal load-displacement curves depend on the microstructural anisotropy. A crystalline model using a finite element code is proposed. The anisotropy is described by a hardening matrix whose terms correspond to dislocation-dislocation interactions and depend on the evolution of the dislocation densities on the activated slip systems during the sequential tests. The strain localization predicted by this model fits with the experimental observation and allows us to assume that localization is correlated to the saturation on the activated slip systems.


2021 ◽  
Vol 4 (4) ◽  
pp. 415-424
Author(s):  
A. A. Issa ◽  
K. O. Adetunji ◽  
T. Alanamu ◽  
E. J. Adefila ◽  
K. A. Muhammed

Statistical models of biased sampling of two non-central hypergeometric distributions Wallenius' and Fisher's distribution has been extensively used in the literature, however, not many of the logic of hypergeometric distribution have been investigated by different techniques. This research work examined the procedure of the two non-central hypergeometric distributions and investigates the statistical properties which includes the mean and variance that were obtained. The parameters of the distribution were estimated using the direct inversion method of hyper simulation of biased urn model in the environment of R statistical software, with varying odd ratios (w) and group sizes (mi). It was discovered that the two non - central hypergeometric are approximately equal in mean, variance and coefficient of variation and differ as odds ratios (w) becomes higher and differ from the central hypergeometric distribution with ω = 1. Furthermore, in univariate situation we observed that Fisher distribution at (ω = 0.2, 0.5, 0.7, 0.9) is more consistent than Wallenius distribution, although central hypergeometric is more consistent than any of them. Also, in multinomial situation, it was observed that Fisher distribution is more consistent at (ω = 0.2, 0.5), Wallenius distribution at (ω = 0.7, 0.9) and central hypergeometric at (ω = 0.2)    


Biometrics ◽  
1996 ◽  
Vol 52 (2) ◽  
pp. 442 ◽  
Author(s):  
Richard J. Cook
Keyword(s):  

Biometrika ◽  
1968 ◽  
Vol 55 (1) ◽  
pp. 268
Author(s):  
David W. Alling
Keyword(s):  

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