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Relaxation of monotone coupling conditions: Poisson approximation and beyond
2018
Journal of Applied Probability
It is well known that assumptions of monotonicity in size-bias couplings may be used to prove simple, yet powerful, Poisson approximation results. Here we show how these assumptions may be relaxed, establishing explicit Poisson approximation bounds (depending on the first two moments only) for random variables which satisfy an approximate version of these monotonicity conditions. These are shown to be effective for models where an underlying random variable of interest is contaminated with
doi:10.1017/jpr.2018.48
fatcat:64ufhmaaojbklazbqmfrtyxt4q