activity
20182022
most citedA robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis

11 citations · 24 across the 4 of their papers we have counts for

collaborators

6 papers

stat.ME202211 cited

A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis

Ivette Raices Cruz, Matthias C. M. Troffaes, Johan Lindström +1

Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled qua…

math.OC20212 cited

Improving and benchmarking of algorithms for -maximin, -maximax and interval dominance

Nawapon Nakharutai, Matthias C. M. Troffaes, Camila C. S. Caiado

-maximin, -maximax and inteval dominance are familiar decision criteria for making decisions under severe uncertainty, when probability distributions can only be partially id…

math.OC20199 cited

Improving and benchmarking of algorithms for decision making with lower previsions

Nawapon Nakharutai, Matthias C. M. Troffaes, Camila C. S. Caiado

Maximality, interval dominance, and E-admissibility are three well-known criteria for decision making under severe uncertainty using lower previsions. We present a new fast algorit…

q-fin.MF20192 cited

Evaluating betting odds and free coupons using desirability

Nawapon Nakharutai, Camila C. S. Caiado, Matthias C. M. Troffaes

In the UK betting market, bookmakers often offer a free coupon to new customers. These free coupons allow the customer to place extra bets, at lower risk, in combination with the u…

math.OC2018

Improved linear programming methods for checking avoiding sure loss

Nawapon Nakharutai, Matthias C. M. Troffaes, Camila C. S. Caiado

We review the simplex method and two interior-point methods (the affine scaling and the primal-dual) for solving linear programming problems for checking avoiding sure loss, and pr…

stat.CO2018

Imprecise Monte Carlo simulation and iterative importance sampling for the estimation of lower previsions

Matthias C. M. Troffaes

We develop a theoretical framework for studying numerical estimation of lower previsions, generally applicable to two-level Monte Carlo methods, importance sampling methods, and a…