Financial Modeling Using Sampling-Importance Resampling

Financial Modeling Using Sampling Importance Resampling"

While Bayesian methodology has been for decades widely applied to econo­ metric models, robust methods had just lately gained more attention of econo­ metricians. More recently, simulation-based techniques, such as the Sampling­ Importance Resampling (SIR) algorithm, have become useful and popular ap­ proaches to statistical problems. This article puts together the robust and Bayesian approache...

On the convergence of quasi-random sampling/importance resampling

Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

We provide a sequential Monte Carlo method for estimating rare-event probabilities in dynamic, intensity-based point process models of portfolio credit risk. The method is based on a change of measure and involves a resampling mechanism. We propose resampling weights that lead, under technical conditions, to a logarithmically efficient simulation estimator of the probability of large portfolio ...

Sampling, Amplification, and Resampling

Many biological experiments for measuring the concentration levels of the gene transcripts or protein molecules involve the application of the Polymerase Chain Reaction (PCR) procedure to the gene or protein samples. To better model the results of the these experiments, we propose a new sampling scheme—sampling, amplification, and resampling (SAR)—for generating discrete data, and derive the as...

Importance Resampling for BSSRDF

Rendering translucent effects using the diffusion model BSSRDF [Jensen et al. 2001] is still a difficult problem compared to traditional BSDF, because it introduces additional degrees of freedom. In a BSSRDF, the light does not scatter at a single point: it enters the surface at one point, and exits at another. Various techniques have been proposed to sample an exit point based on a entry point...