We consider regression when the predictor is measured with error and an instrumental variable is available. The regression function can be modeled linearly, nonlinearly, or nonparametrically. Our major new result shows that the regression function and all parameters in the measurement error model are identified under relatively weak conditions, much weaker than previously known to imply identif...

io-port 06474690 Laurain, Vincent; Tóth, Roland; Piga, Dario; Zheng, Wei Xing An instrumental least squares support vector machine for nonlinear system identification. Automatica 54, Article ID 6308, 340-347 (2015). Summary: Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproducing Kernel Hilbert Space (RKHS) theories, represent a promising approach ...

Here we report a cross-sectional study investigating the influence of instrumental music practice on the ability to monitor for and respond to processing conflicts and performance errors. Behavioural and electrophysiological indicators of response monitoring in amateur musicians with various skill levels were collected using simple conflict tasks. The results show that instrumental musicians ar...

In a dynamic world, accurate beliefs about the environment are vital for survival, and individuals should therefore regularly seek out new information with which to update their beliefs. This aspect of behaviour is not well captured by standard theories of decision making, and the neural mechanisms of information seeking remain unclear. One recent theory posits that valuation of information res...

We show how the total intensity data acquired with the European VLBI telescopes are aaected by a systematic error, the physical origin of which lies in the instrumental polarization of the telescopes. The eeect on the data can be modeled and therefore removed by data processing.

This paper looks at the problem of system identification from non-uniformly sampled input-output data. It describes how refined instrumental variable estimators can be derived to directly identify the parameters of continuous-time output error and Box-Jenkins transfer function models from irregularly sampled data. Monte Carlo simulation analysis is used to illustrate the properties of the propo...

abstarct precise knowledge of all components of water balance is essential to optimize water use in irrigated agriculture. however, water balance components are difficult to measure in required time intervals because their measuring is time consuming and costly. unsaturated zone simulator models are useful tools for predicting the effects of agricultural management on crop water use and can be ...

This paper develops two instrumental variable (IV) estimators for dynamic panel data models with exogenous covariates and a multifactor error structure when both the cross-sectional time series dimensions, N T respectively, are large. The main idea is to project out common factors from of model, construct instruments based on defactored covariates. For homogeneous slope coefficients, we propose...