Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes' Description

Bayesian, and non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Non-Parametric Survival Functions: The R Package CFC

The R package CFC performs cause-specific, competing-risk survival analysis by computing cumulative incidence functions from unadjusted, cause-specific survival functions. A high-level API in CFC enables end-to-end survival and competing-risk analysis, using a single-line function call, based on the parametric survival regression models in survival package. A low-level API allows users to achie...

Non - parametric VaR techniques

1 VaR (Value at Risk) estimates are currently based on two main techniques, the variance-covariance approach or simulation. Statistical and computational problems affect the reliability of these techniques. We illustrate a new technique, filtered historical simulation, that is designed to remedy some of the shortcomings of the simulation approach. We compare the estimates it produces with tradi...

Non–parametric volatility estimation

Contributions to Parametric and Non-parametric Analysis of Mixed Spectra

The present thesis consists of four parts, all connected to sensor array signal processing in a wide sense. The rst part covers the optimal solution to a commonly occurring matrix optimization problem and the resulting implications for the best linear unbiased estimator (BLUE) and for spatial and temporal lter design. Non-parametric spectral estimation is covered in the second part. Here, we in...

Polyhedral Techniques for Parametric Memory Requirement Estimation