Incomplete Cholesky factorization

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In mathematics, Doob's martingale inequality is a result in the study of stochastic processes. It gives a bound on the probability that a stochastic process exceeds any given value over a given interval of time. As the name suggests, the result is usually given in the case that the process is a non-negative martingale, but the result is also valid for non-negative submartingales.

The inequality is due to the American mathematician Joseph L. Doob.

Statement of the inequality

Let X be a submartingale taking non-negative real values, either in discrete or continuous time. That is, for all times s and t with s < t,

𝐄[Xt|β„±s]β‰₯Xs.

(For a continuous-time submartingale, assume further that the process is cΓ dlΓ g.) Then, for any constant C > 0 and p ≥ 1,

𝐏[sup0≀t≀TXtβ‰₯C]≀𝐄[XTp]Cp.

In the above, as is conventional, P denotes the probability measure on the sample space Ξ© of the stochastic process

X:[0,T]Γ—Ξ©β†’[0,+∞)

and E denotes the expected value with respect to the probability measure P, i.e. the integral

𝐄[XT]=∫ΩXT(Ο‰)d𝐏(Ο‰)

in the sense of Lebesgue integration. β„±s denotes the Οƒ-algebra generated by all the random variables Xi with i ≤ s; the collection of such Οƒ-algebras forms a filtration of the probability space.

Further inequalities

There are further (sub)martingale inequalities also due to Doob. With the same assumptions on X as above, let

St=sup0≀s≀tXs,

and for p ≥ 1 let

β€–Xtβ€–p=β€–Xtβ€–Lp(Ξ©,β„±,𝐏)=(𝐄[|Xt|p])1p.

In this notation, Doob's inequality as stated above reads

𝐏[STβ‰₯C]≀‖XTβ€–ppCp.

The following inequalities also hold: for p = 1,

β€–STβ€–p≀eeβˆ’1(1+β€–XTlogXTβ€–p)

and, for p > 1,

β€–XTβ€–p≀‖STβ€–p≀ppβˆ’1β€–XTβ€–p.

Doob's inequality for discrete-time martingales implies Kolmogorov's inequality: if X1, X2, ... is a sequence of real-valued independent random variables, each with mean zero, it is clear that

𝐄[X1++Xn+Xn+1|X1,,Xn]=X1++Xn+𝐄[Xn+1|X1,,Xn]=X1+β‹―+Xn,

so Mn = X1 + ... + Xn is a martingale. Note that Jensen's inequality implies that |Mn| is a nonnegative submartingale if Mn is a martingale. Hence, taking p = 2 in Doob's martingale inequality,

𝐏[max1≀i≀n|Mi|β‰₯Ξ»]≀𝐄[Mn2]Ξ»2,

which is precisely the statement of Kolmogorov's inequality.

Application: Brownian motion

Let B denote canonical one-dimensional Brownian motion. Then

𝐏[sup0≀t≀TBtβ‰₯C]≀exp(βˆ’C22T).

The proof is just as follows: since the exponential function is monotonically increasing, for any non-negative Ξ»,

{sup0≀t≀TBtβ‰₯C}={sup0≀t≀Texp(Ξ»Bt)β‰₯exp(Ξ»C)}.

By Doob's inequality, and since the exponential of Brownian motion is a positive submartingale,

𝐏[sup0≀t≀TBtβ‰₯C]=𝐏[sup0≀t≀Texp(Ξ»Bt)β‰₯exp(Ξ»C)]≀𝐄[exp(Ξ»BT)]exp(Ξ»C)=exp(12Ξ»2Tβˆ’Ξ»C)𝐄[exp(Ξ»Bt)]=exp(12Ξ»2t)

Since the left-hand side does not depend on Ξ», choose Ξ» to minimize the right-hand side: Ξ» = C/T gives the desired inequality.

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