Parametric Estimation and the CIR Model

Parametric Estimation in a Recurrent Competing Risks Model

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the compet- ing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. ...

Multiple parametric motion model estimation and segmentation

This paper presents a motion estimation and segmentation algorithm based on multiple parametric model estimation that determines the a priori unknown number of motion models present in the data. The algorithm applies a quasisimultaneously parametric model fitting method based on a general Least Square fitting. Some experiments are showed in order to demonstrate the results obtained using the pr...

Estimation of the Historical Mean-reverting Parameter in the Cir Model

Estimation of semi-parametric additive coefficient model

In the multivariate regression setting, we propose a flexible varying coefficient model in which the regression coefficients of some predictors are additive functions of other predictors. Marginal integration estimators of the coefficients are developed and their asymptotic properties investigated. Under -mixing, it is found that the estimators of the parameters in the regression coefficients h...

Robust Adaptive-Scale Parametric Model Estimation

for Computer Vision Hanzi Wang and David Suter, Senior Member, IEEE Department of Electrical and Computer Systems Engineering Monash University, Clayton Vic. 3800, Australia. {hanzi.wang ; d.suter}@eng.monash.edu.au Abstract Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator fo...