Non-Parametric Threshold Estimation for the Wiener–Poisson Risk Model

Detection threshold for non-parametric estimation

A new threshold is presented for better estimating a signal by sparse transform and soft thresholding. This threshold derives from a non-parametric statistical approach dedicated to the detection of a signal with unknown distribution and unknown probability of presence in independent and additive white Gaussian noise. This threshold is called the detection threshold and is particularly appropri...

Risk bounds for the non-parametric estimation of Lévy processes

Estimation methods for the Lévy density of a Lévy process are developed under mild qualitative assumptions. A classical model selection approach made up of two steps is studied. The first step consists in the selection of a good estimator, from an approximating (finite-dimensional) linear model S for the true Lévy density. The second is a data-driven selection of a linear model S, among a given...

the estimation of survival function for colon cancer data in tehran using non-parametric bayesian model

background: colon cancer is the third cause of cancer deaths. although colon cancer survival time has increased in recent years, the mortality rate is still high. the cox model is the most common regression model often used in medical research in survival analysis, but most of the time the effect of at least one of the independent factors changes over time, so the model cannot be used. in the c...

Non–parametric volatility estimation

Non Parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps

We consider a stochastic process driven by a diffusion and jumps. We devise a technique, which is based on a discrete record of observations, for identifying the times when jumps larger than a suitably defined threshold occurred. The technique allows also jump size estimation. We prove the consistency of a nonparametric estimator of the integrated infinitesimal variance of the process continuou...