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Martingale central limit theorem for random multiplicative functions
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abstract
Let $\alpha$ be a Steinhaus or a Rademacher random multiplicative function. For a wide class of multiplicative functions $f$ we show that the sum $\sum_{n \le x}\alpha(n) f(n)$, normalised to have mean square $1$, has a non-Gaussian limiting distribution. More precisely, we establish a generalised central limit theorem with random variance determined by the total mass of a random measure associated with $\alpha f$. Our result applies to $d_z$, the $z$-th divisor function, as long as $z$ is strictly between $0$ and $\tfrac{1}{\sqrt{2}}$. Other examples of admissible $f$-s include any multiplicative indicator function with the property that $f(p)=1$ holds for a set of primes of density strictly between $0$ and $\tfrac{1}{2}$.
Forward citations
Cited by 2 Pith papers
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The Fourier coefficients of the critical holomorphic multiplicative chaos
In the critical case, c_n (log n)^{1/4} converges in distribution to sqrt(M_1) Z, with M_1^{-1} exponential of mean sqrt(pi).
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A few notes on the asymptotic behavior of Rademacher random multiplicative functions
Obtains unrestricted high-moment estimates and exponential tail bounds for sums of Rademacher multiplicative functions via martingales.
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