Extensions of Two Classical Poisson Limit Laws to Non-stationary Independent Sequences
In earlier stages in the introduction to asymptotic methods in probability theory, the weak convergence of sequences (Xn)n≥1 of binomial random variables (rv’s) to a Poisson law is classical and easy to prove. A version of such a result concerning sequences (Yn)n≥1 of negative binomial rv’s also exists. In both cases, Xn and Yn −n are by-row sums Sn[X] and Sn[Y ] of arrays of Bernoulli rv’s and corrected geometric rv’s respectively. When considered in the general frame of asymptotic theorems of by-row sums of rv’s of arrays, these two simple results in the independent and identically distributed scheme can be generalized to non-stationary data and beyond to nonstationary and dependent data. Further generalizations give interesting results that would not be found by direct methods. In this paper, we focus on generalizations to the non-stationary independent data. Extensions to dependent data will be addressed later.
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