A logarithmic calibration estimation procedure is proposed for nonparametric regression models under the multiplicative distortion measurement errors setting. The unobservable response variable and covariates are both distorted in a fashion by an observed confounding variable. By using unobserved variables, we consider to study estimates of mean function its first derivative, variance function,...

We determine the constants in some inverse inequalities for finite element functions. These constants are crucial for the correct calibration of a posteriori error estimators. Résumé: Pour deséléments finis on calcule les constantes dans certaines inégalités inverses. Cettes constantes sont importantes pour l'´ etalonnage des estimateurs d'erreur a posteriori.

We consider a general calibration problem for derivative pricing models, which we reformulate into a Bayesian framework to attain posterior distributions for model parameters. It is then shown how the posterior distribution can be used to estimate prices for exotic options. We apply the procedure to a discrete local volatility model and work in great detail through numerical examples to clarify...

This paper tackles the problem of selecting among several linear estimators in nonparametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression or spline smoothing, and the choice of a kernel in multiple kernel learning. We propose a new algorithm which first estimates consistently the variance of the noise, based...

We consider estimation problems in capture-recapture models when the covariates or the auxiliary variables are measured with errors. The naive approach, which ignores measurement errors, is found to be unacceptable in the estimation of both regression parameters and population size: it yields estimators with biases increasing with the magnitude of errors, and flawed confidence intervals. To acc...

When extracting the weak lensing shear signal, one may employ either locally normalized or globally normalized shear estimators. The former is the standard approach when estimating cluster masses, while the latter is the more common method among peak finding efforts. While both approaches have identical signal-to-noise in the weak lensing limit, it is possible that higher order corrections or s...

Many researchers have used different auxiliary parameters such as coefficient of variation, kurtosis, skewness, quartiles, deciles etc., to improve the precision estimators under various sampling schemes. This paper suggested a class ratio with two known variable for estimation population means simple random sample without replacement (SRSWOR) using calibration weighting method. The calibrated ...

The presence of extreme events gives rise to outrageous results regarding population parameters and their estimates using traditional moments. Traditional moments are usually influenced by observations. In this paper, we propose some new calibration estimators under L-Moments scheme for variance which is one the most important parameters. Some suitable constraints double stratified random sampl...

In this paper, we used the newly proposed exponential calibration for additive distortion measurement errors models, where neither response variable nor covariates can be directly observed but are distorted in fashions by an confounding variable. By using calibrated variables, three estimators of parameters and empirical likelihood-based confidence intervals proposed, studied asymptotic propert...