This paper presents a new approach to shape and motion estimation based on geometric primitives and relations in a model-based framework. A description of a scene in terms of structured geometric elements sharing relationships allows to derive a parametric model with Euclidian constraints, and a camera model is also proposed to reduce the problem dimensionality. It leads to a sequential MAP est...

The development of Long Memory Stochastic Volatility (LMSV) models has increased the interest in the estimation of persistent processes observed with added noise. This paper investigates the performance of semi-parametric methods for estimating the longmemory-parameter in the long-range dependence plus noise case and demonstrates improvements obtained by preliminary smoothing and aggregation of...

We review estimation in interval censoring models including nonparametric esti mation of a distribution function and estimation of regression models In the non parametric setting we describe computational procedures and asymptotic properties of the nonparametric maximum likelihood estimators In the regression setting we focus on the proportional hazards the proportional odds and the accelerated...

Chen (2009, Biometrics) studies the semi-parametric accelerated failure time model for data that are size biased. Chen considers only the uncensored case and uses hazard-based estimation methods originally developed for censored observations. However, for uncensored data, a simple linear regression on the log scale is more natural and provides better estimators.

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software ca...

background : kernel smoothing method is a non-parametric or graphical method for statistical estimation. in the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. methods : by employing non-parametric regression methods, the curve estimation, may have some complexity.  in this article, four indices of epanechnikov, b...

We present a model for direct semi-parametric estimation of the state price density (SPD) implied by quoted option prices. treat observed prices as expected values possible pay-offs at ma...

The problem of nonlinear estimation is reexamined, and a new semi-parametric representation of uncertainty called the Biscay distribution is presented. The Biscay distribution is combined with the extended Kalman filter (EKF) and a new filtering paradigm called the Biscay distribution filter (BDF) is developed. The BDF is provably optimal for linear estimation and generalizes naturally to nonli...