Consider testing the null hypothesis that a cumulative distribution function (cdf) belongs to a given set of cdfs versus the alternative that it belongs to another disjoint set of cdfs. A non-local efficiency is introduced for this testing situation. Its performance is assessed by developing...

Expressions for moment properties have not been known even for the simplest of the bivariate extreme value distributions. Here, simple expansions are derived for various properties of any given bivariate extreme value distribution. Each expansion is a single infinite sum. The properties...

The novel Balakrishnan skew-normal distribution was introduced in 2008. The only known scheme for simulating from this distribution is based on acceptance/rejection sampling. Here, we introduce an alternative scheme that is more efficient. We also derive various stochastic representations for...

Papastathopoulos and Tawn [Papastathopoulos, I., Tawn, J.A., 2013. A generalized Student’s t-distribution. Statistics & Probability Letters 83, 70–77] proposed a generalization of Student’s t distribution to account for negative degrees of freedom. Here, an alternative distribution that...

Following up on the work of Nadarajah and Teimouri [Nadarajah, S., Teimouri, M., 2012. On the characteristic function for asymmetric exponential power distributions. Econometric Reviews 31, 475–481], we derive here, for the first time, explicit closed-form expressions for the characteristic...

For a skew-normal random sequence, convergence rates of the distribution of its partial maximum to the Gumbel extreme value distribution are derived. The asymptotic expansion of the distribution of the normalized maximum is given under an optimal choice of norming constants. We find that the...

Researchers have shown that the optimally weighted z-test, where the weights are the standardized expected difference in means, is more powerful than other methods when combining p-values from independent studies. However, in practice the effect for each independent study is usually unknown,...