8 citations · 9 across the 6 of their papers we have counts for
7 papers
Tail asymptotics for the bivariate skew normal in the general case
Thomas Fung, Eugene Seneta
The present paper is a sequel to and generalization of Fung and Seneta (2016) whose main result gives the asymptotic behaviour as of $λ_L(u) = P(X_1 \leq F_1^{-1}(u)…
Tail asymptotics for the bivariate equi-skew Variance-Gamma distribution
Thomas Fung, Eugene Seneta
We derive the asymptotic rate of decay to zero of the tail dependence of the bivariate skew Variance Gamma (VG) distribution under the equal-skewness condition, as an explicit regu…
Consistent second-order discrete kernel smoothing using dispersed Conway-Maxwell-Poisson kernels
Alan Huang, Lucas Sippel, Thomas Fung
The histogram estimator of a discrete probability mass function often exhibits undesirable properties related to zero probability estimation both within the observed range of count…
Quantile function expansion using regularly varying functions
Thomas Fung, Eugene Seneta
We present a simple result that allows us to evaluate the asymptotic order of the remainder of a partial asymptotic expansion of the quantile function as or …
Semiparametric generalized linear models for time-series data
Thomas Fung, Alan Huang
Time-series data in population health and epidemiology often involve non-Gaussian responses. In this note, we propose a semiparametric generalized linear models framework for time-…
Tail dependence convergence rate for the bivariate skew normal under the equal-skewness condition
Thomas Fung, Eugene Seneta
We derive the rate of decay of the tail dependence of the bivariate skew normal distribution under the equal-skewness condition θ1 = θ2,= θ, say. The rate of convergence depends on…
Convergence rate to a lower tail dependence coefficient of a skew-t distribution
Thomas Fung, Eugene Seneta
We examine the rate of decay to the limit of the tail dependence coefficient of a bivariate skew t distribution which always displays asymptotic tail dependence. It contains as a s…