An Approximation for the Error of the Normal Approximation to a Linear Combination of Independently Distributed Random Variables
Haim Shore
Abstract
Haim Shore
Abstract
A new approximation introduced elsewhere is employed to approximate the error associated with the central limit approximation. In particular, the respective error obtained on approximating a linear combination of n independently distributed random variables (Sn) is examined, and it is shown that the unstandardized error is approximately independent of n. Two examples, for a discrete and for a continuous Sn, demonstrate that if a correction term, based on the above error approximation, is added to the traditional central limit approximation a remarkable improvement of accuracy ensues. Some implications of the new approximation are probed.
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A new approximation introduced elsewhere is employed to approximate the error associated with the central limit approximation. In particular, the respective error obtained on approximating a linear combination of n independently distributed random variables (Sn) is examined, and it is shown that the unstandardized error is approximately independent of n. Two examples, for a discrete and for a continuous Sn, demonstrate that if a correction term, based on the above error approximation, is added to the traditional central limit approximation a remarkable improvement of accuracy ensues. Some implications of the new approximation are probed.
Key concepts: Approximation error, Linear approximation, Mathematics, Limit (mathematics), Approximation theory, Random variable, Applied mathematics, Term (time)