By Dr Patrick Laub

Preprints

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

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