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math.PR2020
Sensitive Random Variables are Dense in Every
Yu-Lin Chou
We show that, for every and for every Borel probability measure over , every element of $L^{p}(\mathbb{R}, \mathscr{B}_{\mathbb{R}}, \…
math.PR2020
Change of Measures for Spectral Stochastic Integrals
Yu-Lin Chou
Under mild conditions, it is possible to obtain, from almost purely measure-theoretic considerations and without any specific reference to stochastic processes, a change-of-measure…
math.PR2020
Tail Probability and Divergent Series
Yu-Lin Chou
From mostly a measure-theoretic consideration, we show that for every nonnegative, finite, and function on a given finite measure space there is some nontrivial sequence of…
math.PR2020
A New Proof for a Strong Law of Large Numbers of Kolmogorov's Type via Weak Convergence
Yu-Lin Chou
In terms of the Dirac representation of sample mean and the weak convergence of empirical distributions that holds almost surely, we construct a new proof for a strong law of large…