I have to show : Xn→X in distribution, Yn→0 in probability ⟹ XnYn→0 in probability.
Let α>0,ϵ>0. Then ∃δ>0 such that −ϵ/δ, ϵ/δ are continuity points of distribution of X and P(|X|>ϵ/δ)≤α
Since Xn→X in distribution, P(Xn≤x)→P(X≤x) for all continuity points (and in particular −ϵ/δ and ϵ/δ). There exists N1,N2∈N such that
n≥N1⟹|P(Xn≤−ϵ/δ)−P(X≤−ϵ/δ)|<α
n≥N2⟹|P(Xn≤ϵ/δ)−P(X≤ϵ/δ)|<α
Let N=max. Then for n \geq N,
P(|X_n|>\epsilon/\delta)=1-P(|X_n| \leq \epsilon/\delta)=1-P(-\epsilon/\delta \leq X_n \leq \epsilon/\delta) = 1-P(X_n \leq \epsilon/\delta)+P(X_n < -\epsilon/\delta) = 1-P(X_n \leq \epsilon/\delta)+P(X_n \leq -\epsilon/\delta \,\,[\text{by continuity}] \leq 1-P(X \leq \epsilon/\delta)+P(X \leq -\epsilon/\delta)+2\alpha = P(|X|>\epsilon/\delta)+2\alpha \leq 3\alpha
Since Y_n \to 0 in probability, \exists N_3 \in \mathbb{N} such that
n \geq N_3 \implies P(|Y_n|>\delta) \leq \alpha
Choose N^{*}=\max\{N,N_3\}. Note that
|X_nY_n|>\epsilon \implies |X_n|>\epsilon/\delta \,\,\text{or}\,\, |Y_n|>\delta
Hence,
P(|X_nY_n|>\epsilon) \leq P(|X_n|>\epsilon/\delta \,\,\text{or}\,\, |Y_n|>\delta) \leq P(|X_n|>\epsilon/\delta)+P(|Y_n|>\delta)
Thus,
n \geq N^{*} \implies P(|X_nY_n|>\epsilon) \leq 4\alpha
Since, \alpha>0 is arbitrary, X_nY_n \to 0 in probability.
Is the proof okay? I have a feeling that I have sort of over-killed it. Is it possible to write a shorter proof of the result? Thank you.
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