By viewing quadratic and other second-order finite population functions as totals or means over a derived synthetic finite population, we show that the recently proposed model calibration and pseudoempirical likelihood methods for effective use of auxiliary information from survey data can be readily extended to obtain efficient estimators of quadratic and other second-order finite population f...

For instance nutritional data are often subject to severe measurement error, and an adequate adjustment of the estimators is indispensable to avoid deceptive conclusions. This paper discusses and extends the method of regression calibration to correct for measurement error in Cox regression. Special attention is paid to the modelling of quadratic predictors, the role of heteroscedastic measurem...

The paper presents the results of a case study fitting the generalized Pareto distribution to insurance industry claims data. Besides classical parametric procedures, robust statistical concepts are considered. The latter provide instruments to assess the characteristics of estimators also in the neighborhood of parametric models. A demand for robust methods may arise in cases of fitting distri...

Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0, 1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is employed. Three estimation methods are presente...

This paper addresses the use of temperature measurements to estimate product compositions in distillation columns. A simple linear multivariate calibration procedure based on steady-state data is used, which requires minimal modeling effort. It is found that these principal-component-regression (PCR) and partial-least-squares (PLS) estimators perform well, even for multicomponent mixtures, pres...

This paper considers the problem of estimation and inference in semiparametric varying coefficients models when the response variable is subject to random censoring. The paper proposes an estimator based on combining inverse probability censoring weighting and profile least squares estimation. The resulting estimator is shown to be asymptotically normal. The paper proposes three test statistics...

Temporally encoded structured light systems are one of the many types of active 3D range sensors available. 3D data is obtained by observing light patterns projected into the scene. In some situations, e.g., calibration, it is necessary to determine the point on the projector emitter plane that corresponds to a given image-plane location, even though the emitter plane is discretised into a fini...

In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with a deterministic but unknown spectral amplitude. The Bernoulli variables are modeled in turn by first order Markov processes with transition probabilities learned from a training database. We consi...

We define an empirical likelihood approach which gives consistent design-based confidence intervals which can be calculated without the need of variance estimates, design effects, resampling, joint inclusion probabilities and linearization, even when the point estimator is not linear. It can be used to construct confidence intervals for a large class of sampling designs and estimators which are...