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In this paper we consider suitable families of power series distributed random variables, and we study their asymptotic behavior in the fashion of large (and moderate) deviations. We also present applications of our results to some fractional counting processes in the literature.
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We introduce a new matrix operation on a pair of matrices, $text{swirl}(A,X),$ and discuss its implications on the limiting spectral distribution. In a special case, the resultant ensemble converges almost surely to the Rayleigh distribution. In proving this, we provide a novel combinatorial proof that the random matrix ensemble of circulant Hankel matrices converges almost surely to the Rayleigh distribution, using the method of moments.
The two-parameter Poisson-Dirichlet distribution is the law of a sequence of decreasing nonnegative random variables with total sum one. It can be constructed from stable and Gamma subordinators with the two-parameters, $alpha$ and $theta$, corresponding to the stable component and Gamma component respectively. The moderate deviation principles are established for the two-parameter Poisson-Dirichlet distribution and the corresponding homozygosity when $theta$ approaches infinity, and the large deviation principle is established for the two-parameter Poisson-Dirichlet distribution when both $alpha$ and $theta$ approach zero.
We construct an explicit filtration of the ring of algebraic power series by finite dimensional constructible sets, measuring the complexity of these series. As an application, we give a bound on the dimension of the set of algebraic power series of bounded complexity lying on an algebraic variety defined over the field of power series.
We introduce a class of stochastic processes with reinforcement consisting of a sequence of random partitions ${mathcal{P}_t}_{t ge 1}$, where $mathcal{P}_t$ is a partition of ${1,2,dots, Rt}$. At each time~$t$,~$R$ numbers are added to the set being partitioned; of these, a random subset (chosen according to a time-dependent probability distribution) joins existing blocks, and the others each start new blocks on their own. Those joining existing blocks each choose a block with probability proportional to that blocks cardinality, independently. We prove results concerning the asymptotic cardinality of a given block and central limit theorems for associated fluctuations about this asymptotic cardinality: these are proved both for a fixed block and for the maximum among all blocks. We also prove that with probability one, a single block eventually takes and maintains the leadership in cardinality. Depending on the way one sees this partition process, one can translate our results to Balls and Bins processes, Generalized Chinese Restaurant Processes, Generalized Urn models and Preferential attachment random graphs.
We consider the process ${x-N(t):tgeq 0}$, where $x>0$ and ${N(t):tgeq 0}$ is a renewal process with light-tailed distributed holding times. We are interested in the joint distribution of $(tau(x),A(x))$ where $tau(x)$ is the first-passage time of ${x-N(t):tgeq 0}$ to reach zero or a negative value, and $A(x)$ is the corresponding first-passage area. We remark that we can define the sequence ${(tau(n),A(n)):ngeq 1}$ by referring to the concept of integrated random walk. Our aim is to prove asymptotic results as $xtoinfty$ in the fashion of large (and moderate) deviations
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