Moment bounds for vector martingale quadratic forms without the convergence assumption

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Let (εn)(\varepsilon_n) be a martingale difference sequence satisfying the homogeneity condition

E[εn+12∣Fn]=σ2a.s.\mathbb{E}[\varepsilon_{n+1}^2\mid \mathcal{F}_n]=\sigma^2 \quad \text{a.s.}

and assumption (Hp)(H_p) introduced in the preceding theorem, for some integer p≥1p\geq 1. Define fkf_k, MkM_k, Sk−1S_{k-1}, SkS_k, and dnd_n as in that theorem. Moment-bound conjecture. Almost surely,

∑k=1nfk(MktSk−1−1Mk)p=O(log⁡dn)\sum_{k=1}^{n}f_k\bigl(M_k^{t}S_{k-1}^{-1}M_k\bigr)^p=\mathcal{O}(\log d_n)

and

∑k=1n[(MktSk−1−1Mk)p−(MktSk−1Mk)p]=O(log⁡dn).\sum_{k=1}^{n}\left[\bigl(M_k^{t}S_{k-1}^{-1}M_k\bigr)^p-\bigl(M_k^{t}S_k^{-1}M_k\bigr)^p\right]=\mathcal{O}(\log d_n).

The conjecture would extend the paper's convergence estimates to the full vector problem without assuming the technical condition that all eigenvalues of SnS_n grow at the same rate. The statement is presented as unresolved in the source.

References

Primary source

Bernard Bercu, Peggy Cénac and Guy Fayolle, “On the Almost Sure Central Limit Theorem for Vector Martingales: Convergence of Moments and Statistical Applications”, arXiv:0812.3528 (2008).

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