Adopt the setting and notation of the conditional mean-square optimality lemma: a probability space (Ω,F,P), a sub-σ-algebra G of F, a natural number k≥1, a tuple X=(X1,…,Xk) of square-integrable random variables, a fixed tuple M=(M1,…,Mk) of conditional expectations Mγ of Xγ given G, the error tuple ε=X−M, a symmetric positive semidefinite real k×k matrix R with entries Rγδ, the pointwise quadratic form U⋅(RV)=∑γ,δ=1kRγδUγVδ for tuples U,V of square-integrable random variables, and an admissible tuple Y=(Y1,…,Yk): each Yγ is square-integrable and almost surely equal to a G-measurable square-integrable random variable, with G-measurability as in the existence and uniqueness theorem for conditional expectation. Let G∈G, write 1G for the function equal to 1 on G and 0 off G, and write 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[1Gε⋅(Rε)]+E[1G(Y−M)⋅(R(Y−M))].
2. (Restricted lower bound.)
E[1G(Y−X)⋅(R(Y−X))] ≥ E[1Gε⋅(Rε)].
3. (Index expansion and independence of the choice.)
E[1Gε⋅(Rε)]=γ=1∑kδ=1∑kRγδE[1Gεγεδ],
and each E[1Gεγεδ] is unchanged if the conditional expectations Mγ are replaced by any other conditional expectations of the Xγ given G.