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Register a new userLarge deviation principles for the Ewens-Pitman sampling model
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Let $M_{l,n}$ be the number of blocks with frequency $l$ in the exchangeable random partition induced by a sample of size $n$ from the Ewens-Pitman sampling model. We show that, as $n$ tends to infinity, $n^{-1}M_{l,n}$ satisfies a large deviation principle and we characterize the corresponding rate function. A conditional counterpart of this large deviation principle is also presented. Specifically, given an initial sample of size $n$ from the Ewens-Pitman sampling model, we consider an additional sample of size $m$. For any fixed $n$ and as $m$ tends to infinity, we establish a large deviation principle for the conditional number of blocks with frequency $l$ in the enlarged sample, given the initial sample. Interestingly, the conditional and unconditional large deviation principles coincide, namely there is no long lasting impact of the given initial sample. Potential applications of our results are discussed in the context of Bayesian nonparametric inference for discovery probabilities.
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Consider a population of individuals belonging to an infinity number of types, and assume that type proportions follow the two-parameter Poisson-Dirichlet distribution. A sample of size n is selected from the population. The total number of different types and the number of types appearing in the sample with a fixed frequency are important statistics. In this paper we establish the moderate deviation principles for these quantities. The corresponding rate functions are explicitly identified, which help revealing a critical scale and understanding the exact role of the parameters. Conditional, or posterior, counterparts of moderate deviation principles are also established.
Let $(a_k)_{kinmathbb N}$ be a sequence of integers satisfying the Hadamard gap condition $a_{k+1}/a_k>q>1$ for all $kinmathbb N$, and let $$ S_n(omega) = sum_{k=1}^ncos(2pi a_k omega),qquad ninmathbb N,;omegain [0,1]. $$ The lacunary trigonometric sum $S_n$ is known to exhibit several properties typical for sums of independent random variables. In this paper we initiate the investigation of large deviation principles (LDPs) for $S_n$. Under the large gap condition $a_{k+1}/a_ktoinfty$, we prove that $(S_n/n)_{ninmathbb N}$ satisfies an LDP with speed $n$ and the same rate function $tilde{I}$ as for sums of independent random variables with the arcsine distribution, but show that the LDP may fail to hold when we only assume the Hadamard gap condition. However, we prove that in the special case $a_k=q^k$ for some $qin {2,3,ldots}$, $(S_n/n)_{ninmathbb N}$ satisfies an LDP with speed $n$ and a rate function $I_q$ different from $tilde{I}$. We also show that $I_q$ converges pointwise to $tilde I$ as $qtoinfty$ and construct a random perturbation $(a_k)_{kinmathbb N}$ of the sequence $(2^k)_{kinmathbb N}$ for which $a_{k+1}/a_kto 2$ as $ktoinfty$, but for which $(S_n/n)_{ninmathbb N}$ satisfies an LDP with the rate function $tilde{I}$ as in the independent case and not, as one might na{i}vely expect, with rate function $I_2$. We relate this fact to the number of solutions of certain Diophantine equations. Our results show that LDPs for lacunary trigonometric sums are sensitive to the arithmetic properties of $(a_k)_{kinmathbb N}$. This is particularly noteworthy since no such arithmetic effects are visible in the central limit theorem by Salem and Zygmund or in the law of the iterated logarithm by Erdos and Gal. Our proofs use a combination of tools from probability theory, harmonic analysis, and dynamical systems.
The aim of this paper is to prove a Large Deviation Principle (LDP) for cumulative processes also known as coumpound renewal processes. These processes cumulate independent random variables occuring in time interval given by a renewal process. Our result extends the one obtained in Lefevere et al. (2011) in the sense that we impose no specific dependency between the cumulated random variables and the renewal process. The proof is inspired from Lefevere et al. (2011) but deals with additional difficulties due to the general framework that is considered here. In the companion paper Cattiaux-Costa-Colombani (2021) we apply this principle to Hawkes processes with inhibition. Under some assumptions Hawkes processes are indeed cumulative processes, but they do not enter the framework of Lefevere et al. (2011).
In this article, we develop a framework to study the large deviation principle for matrix models and their quantiz
We study derangements of ${1,2,ldots,n}$ under the Ewens distribution with parameter $theta$. We give the moments and marginal distributions of the cycle counts, the number of cycles, and asymptotic distributions for large $n$. We develop a ${0,1}$-valued non-homogeneous Markov chain with the property that the counts of lengths of spacings between the 1s have the derangement distribution. This chain, an analog of the so-called Feller Coupling, provides a simple way to simulate derangements in time independent of $theta$ for a given $n$ and linear in the size of the derangement.
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