Asymptotic conditional distribution of exceedance counts: fragility index with different margins

Asymptotic Distribution of Divergence Measure with Applications

Asymptotic Distribution for Conditional MLE of VARMA

0.1 Asymptotic Distribution for Conditional MLE of VARMA Models For a well-defined stationary and invertible VARMA(p, q) model, we assume that the innovations {at} satisfies (a) E(at|Ft−1) = 0, (b) E(atat|Ft−1) = Σ > 0, and (c) at has finite fourth moments, where Ft−1 denotes the σ-field generated by {zt−1, zt−2, · · ·}. It has been proven (Dunsmuir and Hannan, 1976, and Hannan and Deistler, 19...

Fragility of Asymptotic Agreement under Bayesian Learning∗

Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are fragile when individuals are uncertain about the signal distributions: given any such model, vanishin...

Exceedance Probabilities for the Dirichlet Distribution

We derive an efficient method to calculate exceedance probabilities (EP) for the Dirichlet distribution when the number of event types is larger than two. Also, we present an intuitive application of Dirichlet EPs and compare our method to a sampling approach which is the current practice in neuroimaging model selection.

Multivariate distribution models with generalized hyperbolic margins

Multivariate generalized hyperbolic distributions represent an attractive family of distributions (with exponentially decreasing tails) for multivariate data modelling. However, in a limited data environment, robust and fast estimation procedures are rare. In this paper we propose an alternative class of multivariate distributions (with exponentially decreasing tails) belonging to affine-linear...