We propose a new estimation procedure for covariate adjusted nonlinear regression models for situations where both the predictors and response in a nonlinear regression model are not directly observed, however distorted versions of the predictors and response are observed. The distorted versions are assumed to be contaminated with a multiplicative factor that is determined by the value of an un...

AIM Data on the weights of amalgam restorations are lacking. The aim of this study was to determine these weights and to develop criteria to facilitate their estimation. METHODS Four separate regression models with 4 covariates in various combinations were used to estimate the weight of amalgam restorations. Model I, based on 514 restorations from both natural and anatomical replica teeth, co...

Probabilistic-based methods have been used for designing noise tolerant circuits recently. In these methods, however, there is not any reliability mechanism that is essential for nanometer digital VLSI circuits. In this paper, we propose a novel method for designing reliable probabilistic-based logic gates. The advantage of the proposed method in comparison with previous probabilistic-based met...

A common assumption in traditional supervised learning is the similar probability distributionof data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalog...

A common design in social psychology involves the use of two independent variables, an experimental manipulation and a measured individual difference, and the interest is in the interaction between them. In such designs, there are often obvious covariate(s), correlated with the measured independent variable, which the researcher wishes to control. Typically this is done by including the covaria...

Latent variable models represent hidden structure in observational data. To account for the distribution of the observational data changing over time, space or some other covariate, we need generalizations of latent variable models that explicitly capture this dependency on the covariate. A variety of such generalizations has been proposed for latent variable models based on the Dirichlet proce...

The receiver operating characteristic (ROC) curve is the most widely used measure for evaluating the discriminatory performance of a continuous marker. Often, covariate information is also available and several regression methods have been proposed to incorporate covariate information in the ROC framework. Until now, these methods are only developed for the case where the covariate is univariat...