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Value at Ruin and Tail Value at Ruin of the Compound Poisson Process with Diffusion and Efficient Computational Methods

Gatto, Riccardo ; Baumgartner, Benjamin

In: Methodology and Computing in Applied Probability, 2014, vol. 16, no. 3, p. 561-582

Some computational aspects of the generalized von Mises distribution

Gatto, Riccardo

In: Statistics and Computing, 2008, vol. 18, no. 3, p. 321-331

Saddlepoint Approximations to Tail Probabilities and Quantiles of Inhomogeneous Discounted Compound Poisson Processes with Periodic Intensity Functions

Gatto, Riccardo

In: Methodology and Computing in Applied Probability, 2012, vol. 14, no. 4, p. 1053-1074

A Saddlepoint Approximation to the Distribution of Inhomogeneous Discounted Compound Poisson Processes

Gatto, Riccardo

In: Methodology and Computing in Applied Probability, 2010, vol. 12, no. 3, p. 533-551

An efficient simulation algorithm for the generalized von Mises distribution of order two

Pfyffer, Samuel ; Gatto, Riccardo

In: Computational Statistics, 2013, vol. 28, no. 1, p. 255-268

A Bootstrap Test for the Probability of Ruin in the Compound Poisson Risk Process

Baumgartner, Benjamin ; Gatto, Riccardo

In: ASTIN Bulletin, 2010, vol. 40, no. 1, p. 241-255

High-order asymptotic expansions for robust tests

De Rossi, François-Xavier ; Gatto, Riccardo

In: Biometrika, 2001, vol. 88, no. 4, p. 1153-1168

An Accurate Asymptotic Approximation for Experience Rated Premiums

Gatto, Riccardo

In: ASTIN Bulletin, 2004, vol. 34, no. 1, p. 113-124

General saddlepoint approximations of marginal densities and tail probabilities

Gatto, Riccardo ; Ronchetti, Elvezio ; della Svizzera italiana, Svizzera

In: Journal of the American statistical association, 1996, vol. 91, no. 434, p. 666-673

Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear statistics are derived. They are based on the expansion of the statistic up to the second order. Their accuracy is shown in a variety of examples, including logit and probit models and rank estimators for regression.