Hoeffding's lemma

From testwiki
Jump to navigation Jump to search

Template:Short description Template:One source In probability theory, Hoeffding's lemma is an inequality that bounds the moment-generating function of any bounded random variable,[1] implying that such variables are subgaussian. It is named after the FinnishAmerican mathematical statistician Wassily Hoeffding.

The proof of Hoeffding's lemma uses Taylor's theorem and Jensen's inequality. Hoeffding's lemma is itself used in the proof of Hoeffding's inequality as well as the generalization McDiarmid's inequality.

Statement of the lemma

Let X be any real-valued random variable such that aXb almost surely, i.e. with probability one. Then, for all λ,

𝔼[eλX]exp(λ𝔼[X]+λ2(ba)28),

or equivalently,

𝔼[eλ(X𝔼[X])]exp(λ2(ba)28).

Proof

The following proof is direct but somewhat ad-hoc. Another proof uses exponential tilting;[2]Template:Rp proofs with a slightly worse constant are also available using symmetrization.[3]

Without loss of generality, by replacing X by X𝔼[X], we can assume 𝔼[X]=0, so that a0b.

Since eλx is a convex function of x, we have that for all x[a,b],

eλxbxbaeλa+xabaeλb

So,

𝔼[eλX]b𝔼[X]baeλa+𝔼[X]abaeλb=bbaeλa+abaeλb=eL(λ(ba)),

where L(h)=haba+ln(1+aehaba). By computing derivatives, we find

L(0)=L(0)=0 and L(h)=abeh(baeh)2.

From the AMGM inequality we thus see that L(h)14 for all h, and thus, from Taylor's theorem, there is some 0θ1 such that

L(h)=L(0)+hL(0)+12h2L(hθ)18h2.

Thus, 𝔼[eλX]e18λ2(ba)2.

See also

Notes

Template:Reflist


Template:Probability-stub