TheoremBase

Conditional Mean-Square Optimality Restricted to an Event of the Conditioning Sigma-Algebra

Statement

Adopt the setting and notation of the conditional mean-square optimality lemma: a probability space (Ω,F,P)(\Omega,\mathcal{F},P), a sub-σ\sigma-algebra G\mathcal{G} of F\mathcal{F}, a natural number k≥1k\ge1, a tuple X=(X1,…,Xk)X=(X^1,\dots,X^k) of square-integrable random variables, a fixed tuple M=(M1,…,Mk)M=(M^1,\dots,M^k) of conditional expectations MγM^\gamma of XγX^\gamma given G\mathcal{G}, the error tuple ε=X−M\varepsilon=X-M, a symmetric positive semidefinite real k×kk\times k matrix RR with entries RγδR_{\gamma\delta}, the pointwise quadratic form U⋅(RV)=∑γ,δ=1kRγδUγVδU\cdot(RV)=\sum_{\gamma,\delta=1}^{k}R_{\gamma\delta}U^\gamma V^\delta for tuples U,VU,V of square-integrable random variables, and an admissible tuple Y=(Y1,…,Yk)Y=(Y^1,\dots,Y^k): each YγY^\gamma is square-integrable and almost surely equal to a G\mathcal{G}-measurable square-integrable random variable, with G\mathcal{G}-measurability as in the existence and uniqueness theorem for conditional expectation. Let G∈GG\in\mathcal{G}, write 1G\mathbf{1}_{G} for the function equal to 11 on GG and 00 off GG, and write E\mathbb{E} for the expectation. Then all the products appearing below are integrable and:

1. (Restricted orthogonal decomposition.)

E[1G (Y−X)⋅(R (Y−X))]=E[1G ε⋅(R ε)]+E[1G (Y−M)⋅(R (Y−M))].\mathbb{E}\big[\mathbf{1}_{G}\,(Y-X)\cdot\big(R\,(Y-X)\big)\big]=\mathbb{E}\big[\mathbf{1}_{G}\,\varepsilon\cdot(R\,\varepsilon)\big]+\mathbb{E}\big[\mathbf{1}_{G}\,(Y-M)\cdot\big(R\,(Y-M)\big)\big].

2. (Restricted lower bound.)

E[1G (Y−X)⋅(R (Y−X))] ≥ E[1G ε⋅(R ε)].\mathbb{E}\big[\mathbf{1}_{G}\,(Y-X)\cdot\big(R\,(Y-X)\big)\big]\ \ge\ \mathbb{E}\big[\mathbf{1}_{G}\,\varepsilon\cdot(R\,\varepsilon)\big].

3. (Index expansion and independence of the choice.)

E[1G ε⋅(R ε)]=∑γ=1k∑δ=1kRγδ E[1G εγεδ],\mathbb{E}\big[\mathbf{1}_{G}\,\varepsilon\cdot(R\,\varepsilon)\big]=\sum_{\gamma=1}^{k}\sum_{\delta=1}^{k}R_{\gamma\delta}\,\mathbb{E}\big[\mathbf{1}_{G}\,\varepsilon^\gamma\varepsilon^\delta\big],

and each E[1Gεγεδ]\mathbb{E}[\mathbf{1}_{G}\varepsilon^\gamma\varepsilon^\delta] is unchanged if the conditional expectations MγM^\gamma are replaced by any other conditional expectations of the XγX^\gamma given G\mathcal{G}.

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