Establishes a moderate deviations principle for the giant component size in random d-uniform hypergraphs via an adapted exploration process, associated martingale, and exponential estimates.
Lindeberg's method for moderate deviations and random summation
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We apply Lindeberg's method, invented to prove a central limit theorem, to analyze the moderate deviations around such a central limit theorem. In particular, we will show moderate deviation principles for martingales as well as for random sums, in the latter situation both, in the case when the limit distribution is Gaussian or non-Gaussian. Moreover in the Gaussian case we show moderate deviations for random sums using bounds on cumulants, alternatively. Finally, we also prove a large deviation principle for certain random sums.
fields
math.PR 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Moderate deviations for the size of the giant component in a random hypergraph
Establishes a moderate deviations principle for the giant component size in random d-uniform hypergraphs via an adapted exploration process, associated martingale, and exponential estimates.