TheoremBase

Proof

Throughout, B2=B(R)⊗B(R)\mathcal{B}_2=\mathcal{B}(\mathbb{R})\otimes\mathcal{B}(\mathbb{R}) is the product σ\sigma-algebra on R2\mathbb{R}^{2}, and a function on R2\mathbb{R}^{2} is called jointly Borel if it is measurable from (R2,B2)(\mathbb{R}^{2},\mathcal{B}_2) to (R,B(R))(\mathbb{R},\mathcal{B}(\mathbb{R})), as in Joint Distribution, Expectations, and Block Independence for Independent Random Variables. Let g(x)=exp⁡(−x2/2)g(x)=\exp(-x^{2}/2) and c=∫Rg dλ∈(0,∞)c=\int_{\mathbb{R}}g\,d\lambda\in(0,\infty) be as in Standard Normal Distribution, with λ\lambda Lebesgue measure, which is σ\sigma-finite, so the product measure λ⊗λ\lambda\otimes\lambda on B2\mathcal{B}_2 is defined. Set G(x,y)=g(x) g(y)G(x,y)=g(x)\,g(y).

Step 0: Measurability inventory. By Claim 4 of Joint Distribution, Expectations, and Block Independence for Independent Random Variables, the coordinate projections π1,π2\pi_1,\pi_2 are jointly Borel, and t∘φt\circ\varphi is jointly Borel whenever φ\varphi is jointly Borel and tt is Borel; since gg is Borel (recorded in Standard Normal Distribution), g∘π1g\circ\pi_1 and g∘π2g\circ\pi_2 are jointly Borel, and their product GG is jointly Borel by the closure of measurable functions under products recorded in the preliminaries of Square-Integrable Random Variables and the Mean-Square Inner Product, applied on the measurable space (R2,B2)(\mathbb{R}^{2},\mathcal{B}_2). Next, a map T:R2→R2T:\mathbb{R}^{2}\to\mathbb{R}^{2} whose two coordinate functions are jointly Borel is measurable from (R2,B2)(\mathbb{R}^{2},\mathcal{B}_2) to itself: the preimage of a measurable rectangle B×B′B\times B' is the intersection of the coordinate preimages, which lie in B2\mathcal{B}_2, and rectangles generate B2\mathcal{B}_2 by Product Sigma-Algebra, so the generator criterion applies. Finally, for a real α\alpha the function hα(x,y)=x+αyh_\alpha(x,y)=x+\alpha y is jointly Borel: the map φα(x,y)=(x,αy)\varphi_\alpha(x,y)=(x,\alpha y) has jointly Borel coordinates (π1\pi_1, and t∘π2t\circ\pi_2 with the Borel map t(u)=αut(u)=\alpha u), hence is measurable by the rectangle argument, and hαh_\alpha is the composition of the jointly Borel addition map of Claim 4 of Joint Distribution, Expectations, and Block Independence for Independent Random Variables with φα\varphi_\alpha, and compositions of measurable maps are measurable (preimages compose).

Step 1: The joint law of (Z1,Z2)(Z_1,Z_2) has density GG. By Claim 1 of Joint Distribution, Expectations, and Block Independence for Independent Random Variables, the map V=(Z1,Z2):Ω→R2V=(Z_1,Z_2):\Omega\to\mathbb{R}^{2} is measurable and its distribution is the product measure N⊗NN\otimes N, where NN is the standard normal distribution. Define ρ:B2→[0,∞]\rho:\mathcal{B}_2\to[0,\infty] by

ρ(C)=c−2∫R21C G d(λ⊗λ).\rho(C)=c^{-2}\int_{\mathbb{R}^{2}}\mathbf{1}_{C}\,G\,d(\lambda\otimes\lambda).

Then ρ\rho is a measure: ρ(∅)=0\rho(\emptyset)=0, and for pairwise disjoint (Ck)k∈N(C_k)_{k\in\mathbb{N}} the partial sums of ∑k1CkG\sum_k\mathbf{1}_{C_k}G increase pointwise to 1∪kCkG\mathbf{1}_{\cup_kC_k}G, so countable additivity follows from Monotone Convergence Theorem together with the additivity of the integral in Linearity and Monotonicity of the Lebesgue Integral. On a measurable rectangle B×B′B\times B', the Tonelli theorem and the fact that constants factor out of the integral (by Linearity and Monotonicity of the Lebesgue Integral) give

ρ(B×B′)=c−2∫R1B′(y) g(y)(∫R1B(x) g(x) dλ(x))dλ(y)=N(B) N(B′).\rho(B\times B')=c^{-2}\int_{\mathbb{R}}\mathbf{1}_{B'}(y)\,g(y)\Bigl(\int_{\mathbb{R}}\mathbf{1}_{B}(x)\,g(x)\,d\lambda(x)\Bigr)d\lambda(y)=N(B)\,N(B').

Since ρ\rho and N⊗NN\otimes N are two measures on B2\mathcal{B}_2 assigning N(B)N(B′)N(B)N(B') to every measurable rectangle, the uniqueness assertion of Existence and Uniqueness of the Product Measure yields ρ=N⊗N\rho=N\otimes N. Hence

P(V∈C)=c−2∫R21C G d(λ⊗λ)(C∈B2).(1)P(V\in C)=c^{-2}\int_{\mathbb{R}^{2}}\mathbf{1}_{C}\,G\,d(\lambda\otimes\lambda)\qquad(C\in\mathcal{B}_2). \tag{1}

Step 2: Shears preserve the integral. For a real α\alpha define the shears Sα(x,y)=(x+αy,  y)S_\alpha(x,y)=(x+\alpha y,\;y) and Sα′(x,y)=(x,  y+αx)S'_\alpha(x,y)=(x,\;y+\alpha x); by Step 0 both are measurable from (R2,B2)(\mathbb{R}^{2},\mathcal{B}_2) to itself. We claim that for every B2\mathcal{B}_2-measurable f:R2→Rf:\mathbb{R}^{2}\to\mathbb{R} with f≥0f\ge0,

∫R2f∘Sα d(λ⊗λ)=∫R2f d(λ⊗λ),\int_{\mathbb{R}^{2}}f\circ S_\alpha\,d(\lambda\otimes\lambda)=\int_{\mathbb{R}^{2}}f\,d(\lambda\otimes\lambda),

and likewise for Sα′S'_\alpha. Indeed, f∘Sαf\circ S_\alpha is B2\mathcal{B}_2-measurable as a composition, and by the Tonelli theorem, integrating first in xx,

∫R2f∘Sα d(λ⊗λ)=∫R(∫Rf(x+αy, y) dλ(x))dλ(y).\int_{\mathbb{R}^{2}}f\circ S_\alpha\,d(\lambda\otimes\lambda)=\int_{\mathbb{R}}\Bigl(\int_{\mathbb{R}}f(x+\alpha y,\,y)\,d\lambda(x)\Bigr)d\lambda(y).

For each fixed yy, the inner integrand is the translate by t=αyt=\alpha y of the section x↦f(x,y)x\mapsto f(x,y), which is measurable by the section assertion of Tonelli and Fubini Theorems; Claim 2 of Translation Invariance of Lebesgue Measure and the Lebesgue Integral shows the inner integral equals ∫Rf(x,y) dλ(x)\int_{\mathbb{R}}f(x,y)\,d\lambda(x). Applying the Tonelli theorem once more gives the claim. The claim for Sα′S'_\alpha is proved symmetrically, integrating first in yy and using the other iterated-integral identity of Tonelli and Fubini Theorems.

Step 3: Factorization of the rotation into shears. Let R(x,y)=(a x+b y,  −b x+a y)R(x,y)=(a\,x+b\,y,\;-b\,x+a\,y); its coordinates are jointly Borel by Step 0 (sums of the jointly Borel functions t∘πit\circ\pi_i are jointly Borel, again by the preliminaries of Square-Integrable Random Variables and the Mean-Square Inner Product on (R2,B2)(\mathbb{R}^{2},\mathcal{B}_2)), so RR is measurable. Suppose first b≠0b\ne0, and set

α=γ=1−ab,β=−b.\alpha=\gamma=\frac{1-a}{b},\qquad \beta=-b .

Then R=Sα∘Sβ′∘SγR=S_\alpha\circ S'_\beta\circ S_\gamma. Indeed, applying SγS_\gamma, then Sβ′S'_\beta, then SαS_\alpha to (x,y)(x,y) yields the pair with second coordinate βx+(1+βγ)y\beta x+(1+\beta\gamma)y and first coordinate (1+αβ)x+(γ+α(1+βγ))y(1+\alpha\beta)x+\bigl(\gamma+\alpha(1+\beta\gamma)\bigr)y; here 1+αβ=1+βγ=1−(1−a)=a1+\alpha\beta=1+\beta\gamma=1-(1-a)=a, and

γ+α a=(1−a)(1+a)b=1−a2b=b2b=b\gamma+\alpha\,a=\frac{(1-a)(1+a)}{b}=\frac{1-a^{2}}{b}=\frac{b^{2}}{b}=b

using a2+b2=1a^{2}+b^{2}=1, so the pair is exactly (a x+b y,  −b x+a y)(a\,x+b\,y,\;-b\,x+a\,y). Consequently, for B2\mathcal{B}_2-measurable f≥0f\ge0, writing f∘R=((f∘Sα)∘Sβ′)∘Sγf\circ R=\bigl((f\circ S_\alpha)\circ S'_\beta\bigr)\circ S_\gamma and applying Step 2 three times,

∫R2f∘R d(λ⊗λ)=∫R2f d(λ⊗λ).(2)\int_{\mathbb{R}^{2}}f\circ R\,d(\lambda\otimes\lambda)=\int_{\mathbb{R}^{2}}f\,d(\lambda\otimes\lambda). \tag{2}

Step 4: The density is rotation invariant. By the multiplicative property of the exponential function (Basic Properties of the Exponential Function), G(x,y)=exp⁡(−(x2+y2)/2)G(x,y)=\exp\bigl(-(x^{2}+y^{2})/2\bigr), and

(a x+b y)2+(−b x+a y)2=(a2+b2)(x2+y2)=x2+y2,(a\,x+b\,y)^{2}+(-b\,x+a\,y)^{2}=(a^{2}+b^{2})(x^{2}+y^{2})=x^{2}+y^{2},

since the cross terms 2ab xy2ab\,xy and −2ab xy-2ab\,xy cancel. Hence G∘R=GG\circ R=G pointwise on R2\mathbb{R}^{2}.

Step 5: Conclusion for b≠0b\ne0. The functions W1,W2W_1,W_2 are random variables by the closure preliminaries of Square-Integrable Random Variables and the Mean-Square Inner Product, and (W1,W2)=R∘V(W_1,W_2)=R\circ V pointwise on Ω\Omega. Fix Borel sets B,B′B,B' and put C=R−1(B×B′)∈B2C=R^{-1}(B\times B')\in\mathcal{B}_2. Then {W1∈B}∩{W2∈B′}=V−1(C)\{W_1\in B\}\cap\{W_2\in B'\}=V^{-1}(C), and 1C=1B×B′∘R\mathbf{1}_{C}=\mathbf{1}_{B\times B'}\circ R, so by Step 4, 1C G=(1B×B′ G)∘R\mathbf{1}_{C}\,G=(\mathbf{1}_{B\times B'}\,G)\circ R. Combining the identity (1) of Step 1, the invariance (2) of Step 3 applied to f=1B×B′ Gf=\mathbf{1}_{B\times B'}\,G, and Step 1 again,

P(W1∈B,  W2∈B′)=c−2∫R2(1B×B′ G)∘R d(λ⊗λ)=c−2∫R21B×B′ G d(λ⊗λ)=N(B) N(B′).P(W_1\in B,\;W_2\in B')=c^{-2}\int_{\mathbb{R}^{2}}(\mathbf{1}_{B\times B'}\,G)\circ R\,d(\lambda\otimes\lambda)=c^{-2}\int_{\mathbb{R}^{2}}\mathbf{1}_{B\times B'}\,G\,d(\lambda\otimes\lambda)=N(B)\,N(B').

Taking B′=RB'=\mathbb{R} gives P(W1∈B)=N(B)P(W_1\in B)=N(B) for every Borel BB, so W1W_1 is standard normal; taking B=RB=\mathbb{R} gives the same for W2W_2. The displayed identity then reads P(W1∈B,  W2∈B′)=P(W1∈B) P(W2∈B′)P(W_1\in B,\;W_2\in B')=P(W_1\in B)\,P(W_2\in B') for all Borel B,B′B,B', which is independence of W1W_1 and W2W_2.

Step 6: The case b=0b=0. Then a2=1a^{2}=1. If a=1a=1, then (W1,W2)=(Z1,Z2)(W_1,W_2)=(Z_1,Z_2) and there is nothing to prove. If a=−1a=-1, then (W1,W2)=(−Z1,−Z2)(W_1,W_2)=(-Z_1,-Z_2): each is standard normal by Claim 3 of Reflection Invariance of Lebesgue Measure and Symmetry of the Standard Normal Distribution, and for Borel B,B′B,B' the events {−Z1∈B}={Z1∈−B}\{-Z_1\in B\}=\{Z_1\in-B\} and {−Z2∈B′}={Z2∈−B′}\{-Z_2\in B'\}=\{Z_2\in-B'\} involve the Borel sets −B,−B′-B,-B' by Claim 1 of Reflection Invariance of Lebesgue Measure and Symmetry of the Standard Normal Distribution, so the product identity follows from the independence of Z1Z_1 and Z2Z_2. ■\blacksquare

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