ORCID as entered in ROS

Select Publications
Jang J; Laub PJ; Siu TK; Zhao H, 2025, Arbitrage-free catastrophe reinsurance valuation for compound dynamic contagion claims, http://arxiv.org/abs/2502.13325v1
Avanzi B; Dong E; Laub PJ; Wong B, 2024, Distributional Refinement Network: Distributional Forecasting via Deep Learning, http://arxiv.org/abs/2406.00998v1
Laub PJ; Lee Y; Pollett PK; Taimre T, 2024, Hawkes Models And Their Applications, http://arxiv.org/abs/2405.10527v1
Lee Y; Laub PJ; Taimre T; Zhao H; Zhuang J, 2021, Exact simulation of extrinsic stress-release processes, http://arxiv.org/abs/2106.14415v1
Goffard P-O; Laub PJ, 2020, Approximate Bayesian Computations to fit and compare insurance loss models, http://arxiv.org/abs/2007.03833v2
Laub PJ; Karoui NE; Loisel S; Salhi Y, 2020, Quickest detection in practice in presence of seasonality: An illustration with call center data, http://arxiv.org/abs/2006.04576v1
Taimre T; Laub PJ, 2018, Rare tail approximation using asymptotics and $L^1$ polar coordinates, http://arxiv.org/abs/1809.06594v1
Asmussen S; Hashorva E; Laub PJ; Taimre T, 2017, Tail asymptotics of light-tailed Weibull-like sums, http://arxiv.org/abs/1712.04070v1
Goffard P-O; Laub PJ, 2017, Orthogonal polynomial expansions to evaluate stop-loss premiums, http://arxiv.org/abs/1712.03468v2
Laub PJ; Salomone R; Botev ZI, 2017, Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables, http://arxiv.org/abs/1711.11218v2
Andersen LN; Laub PJ; Rojas-Nandayapa L, 2016, Efficient simulation for dependent rare events with applications to extremes, http://arxiv.org/abs/1609.09725v2
Asmussen S; Goffard P-O; Laub PJ, 2016, Orthonormal polynomial expansions and lognormal sum densities, http://arxiv.org/abs/1601.01763v1
Laub PJ; Asmussen S; Jensen JL; Rojas-Nandayapa L, 2015, Approximating the Laplace transform of the sum of dependent lognormals, http://arxiv.org/abs/1507.03750v2
Laub PJ; Taimre T; Pollett PK, 2015, Hawkes Processes, http://arxiv.org/abs/1507.02822v1