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A terminal weak limit does not imply a path-space weak limit

Statement

Let C=C([0,1];H) with the supremum norm, choose a unit vector e∈H, and define deterministic paths w2r(t)=0,w2r+1(t)=sin⁡(πt)e(0≤t≤1). Every terminal value is zero, so the laws of wn(1) converge to the point mass at 0 in H. The laws of the paths do not converge weakly on C. This illustrates why the sample-sum CLT in Hilbert-valued CLT for L2-m-approximable Bernoulli shifts is not a time-indexed process CLT.

Facts & Assumptions

Given: The metric space C with d(w,v)=sup⁡t∈[0,1]∥w(t)−v(t)∥H and the deterministic paths in the Statement.

[F1]

Weak convergence of Borel probability laws entails convergence of integrals of bounded continuous real functions (Convergence in distribution of random elements).

[F2]

The preceding Hilbert-valued theorem concerns normalized sample sums as single H-valued random elements, not paths indexed by t∈[0,1] (Hilbert-valued CLT for L2-m-approximable Bernoulli shifts).

Proof

Given: The paths wn and their point-mass laws.

1.1givenF1algebra

Since sin⁡(0)=sin⁡(π)=0, both even and odd paths satisfy wn(1)=0. Thus their terminal laws are all the point mass δ0 and in particular converge weakly to δ0 in H.

1.2F1algebra

Define Φ:C→R by Φ(w)=min⁡{1,sup⁡0≤t≤1∥w(t)∥H}. The supremum norm inequality gives ∣sup⁡t∥w(t)∥H−sup⁡t∥v(t)∥H∣≤sup⁡t∥w(t)−v(t)∥H=d(w,v), and truncation by x↦min⁡(1,x) is 1-Lipschitz on [0,∞). Hence Φ is bounded and continuous. It equals 0 on every even path and 1 on every odd path, so its expectations under the path laws alternate between 0 and 1 and do not converge. By [F1], those path laws cannot converge weakly.

2.1F2step 1.1step 1.2∎

The example has convergence only after applying the terminal evaluation map; the bounded continuous path functional in step 1.2 detects the missing path-space convergence. Therefore endpoint convergence alone supplies no functional CLT conclusion, consistently with the scope in [F2].

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