The purpose of the research presented here is twofold. The first component explores the probabilistic interpretation of Stein’s method, as introduced in Barbour (1988). This is done in the setting of random variable approximations. This probabilistic method, where the Stein equation is interpreted in terms of the generator of an underlying birth and death process having equilibrium distribution equal to that of the approximant, provides a natural explanation of why Stein’s method works. An open problem has been to use this generator approach to obtain bounds on the differences of the solution to the Stein equation. Uniform bounds on these differences produce Stein “magic” factors, which control the bounds. With the choice of unit per capita death rate for the birth and death process, we are able to produce a result giving a new Stein factor bound, which applies to a selection of distributions. The proof is via a probabilistic approach, and we also include a probabilistic proof of a Stein factor bound from Barbour, Holst and Janson (1992). These results generalise the work of Xia (1999), which applies to the Poisson distribution with unit per capita death rate. (For complete abstract open document)
| Identifer | oai:union.ndltd.org:ADTP/245391 |
| Creators | Weinberg, Graham Victor |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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