We discuss alternative approaches for estimating from cross-sectional categorical data in the presence of misclassification. Two parameterisations of the misclassification model are reviewed. The first employs misclassification probabilities and leads tomoment-based inference. The second employs calibration probabilities and leads tomaximum likelihood inference. We show that maximum likelihood ...

In order to better capture empirical phenomena, research on option price and implied volatility modeling increasingly advocates the use of nonparametric methods over simple functional forms. This, however, comes at a price, since they require dense observations to yield sensible results. Calibration is therefore typically performed using aggregate data. Ironically, the use of time-series data i...

We consider the problem of reconstructing the dynamic state matrix of transmission power grids from timestamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not ...

Introduction. Diffusion-weighted (DW) MRI provides important information regarding the arrangement of white matter fibres. However, imperfections in the DW gradients may cause errors in the estimation of diffusion parameters. The sources of the gradient errors are various and may arise from long-term eddy currents, background gradients, imaging gradients, and spatial non-linearity and non-unifo...

The problem of estimation in multivariate linear calibration with multivariate response and explanatory variables is considered. In this calibration problem two estimators are well-known; one is the classical estimator and the other is the inverse estimator. In this paper we show that the inverse estimator is a proper Bayes estimator under the quadratic loss with respect to a prior distribution...

In this paper we develop an asymptotic theory for estimation based on signed ranks in the ARMA model when the innovation density is symmetrical. We provide two classes of estimators and we establish their asymptotic normality with the help of the asymptotic properties for serial signed rank statistics. Finally, we compare our procedure to the one of least-squares, and we illustrate the performa...

Partial linear model is very flexible when the relation between the covariates and responses, either parametric and nonparametric. However, estimation of the regression coefficients is challenging since one must also estimate the nonparametric component simultaneously. As a remedy, the differencing approach, to eliminate the nonparametric component and estimate the regression coefficients, can ...