scholarly journals The Erdös-Faber-Lovász conjecture – the uniform regular case

2010 ◽  
Vol 1 (2) ◽  
pp. 113-120 ◽  
Author(s):  
Vance Faber
Keyword(s):  
Regular Case ◽  
Lovász Conjecture

The multitype continuous-time Markov branching process in a periodic environment

1980 ◽  
Vol 12 (1) ◽  
pp. 81-93 ◽  
Author(s):  
B. Klein ◽  
P. D. M. MacDonald
Keyword(s):  
Continuous Time ◽  
Branching Process ◽  
Cell Kinetics ◽  
Initial Conditions ◽  
Random Variable ◽  
Diurnal Rhythms ◽  
Biological Applications ◽  
Regular Case ◽  
Markov Branching Process ◽  
Periodic Environment

The multitype continuous-time Markov branching process has many biological applications where the environmental factors vary in a periodic manner. Circadian or diurnal rhythms in cell kinetics are an important example. It is shown that in the supercritical positively regular case the proportions of individuals of various types converge in probability to a non-random periodic vector, independent of the initial conditions, while the absolute numbers of individuals of various types converge in probability to that vector multiplied by a random variable whose distribution depends on the initial conditions. It is noted that the proofs are straightforward extensions of the well-known results for a constant environment.

scholarly journals Solution of the Bernstein Problem in the Non-regular Case

2000 ◽  
Vol 223 (1) ◽  
pp. 109-132 ◽  
Author(s):  
J.Carlos Gutiérrez Fernández
Keyword(s):  
Regular Case ◽  
Bernstein Problem

scholarly journals On the finite W-algebra for the Lie superalgebra Q(N) in the non-regular case

2017 ◽  
Vol 58 (11) ◽  
pp. 111701 ◽  
Author(s):  
Elena Poletaeva ◽  
Vera Serganova
Keyword(s):  
Lie Superalgebra ◽  
Regular Case

On Jackknifing in Estimating The Finite End–Points of a Distribution*

1977 ◽  
Vol 26 (1-4) ◽  
pp. 17-24 ◽  
Author(s):  
Pranab Kumar Sen
Keyword(s):  
Extreme Values ◽  
Regular Case ◽  
End Points ◽  
Biased Estimators

Sample extreme values are biased estimators of the end-points of a distribution, and hence, jackknifing is useful. However, the properties of jackknifing in such a case differ considerably from those in the regular case. These are studied here. Along with a modification of jackknifing, some applications are also considered.

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