
doi: 10.1007/bf02491493
Let X be a standard normal random variable and let \(\sigma\) be a positive random variable independent of X. The distribution of \(\eta =\sigma X\) is expanded around that of N(0,1) and its error bounds are obtained. Bounds are given in terms of \(E(\sigma^ 2\vee \sigma^{-2}-1)^ k\), where \(\sigma^ 2\vee \sigma^{-2}\) denotes the maximum of the two quantities \(\sigma^ 2\) and \(\sigma^{-2}\), and k is a positive integer, and of \(E(\sigma^ 2-1)^ k\), if k is even.
Asymptotic approximations, asymptotic expansions (steepest descent, etc.), Asymptotic distribution theory in statistics, t-distribution, Inequalities; stochastic orderings, Statistical distribution theory, scale mixtures of normal distributions, error bounds
Asymptotic approximations, asymptotic expansions (steepest descent, etc.), Asymptotic distribution theory in statistics, t-distribution, Inequalities; stochastic orderings, Statistical distribution theory, scale mixtures of normal distributions, error bounds
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
