EKF- and UKF-Based Estimators for Radar System

In radar, the measurements (like the range and radial velocity) are determined from the time delay and Doppler shift. Since the time delay and Doppler shift are estimated from the phase of the received echo, the concerned estimation problem is nonlinear. Consequently, the conventional estimator based on the fast Fourier transform (FFT) is prone to yield high estimation errors. Recently, nonlinear estimators based on kernel least mean square (KLMS) are introduced and found to outperform the conventional estimator. However, estimators based on KLMS are susceptible to incorrect choice of various system parameters. Thus, to mitigate the limitation of existing estimators, in this paper, two efficient low-complexity nonlinear estimators, namely, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are proposed. The EKF is advantageous due to its implementation simplicity; however, it suffers from the poor representation of the nonlinear functions by the first-order linearization, whereas UKF outperforms the EKF and offers better stability due to exact consideration of the system nonlinearity. Simulation results reveal improved accuracy achieved by the proposed EKF- and UKF-based estimators.

Statistical learning for recommending (robust) nonlinear regression methods

We are interested in comparing the performance of various nonlinear estimators of parameters of the standard nonlinear regression model. While the standard nonlinear least squares estimator is vulnerable to the presence of outlying measurements in the data, there exist several robust alternatives. However, it is not clear which estimator should be used for a given dataset and this question remains extremely difficult (or perhaps infeasible) to be answered theoretically. Metalearning represents a computationally intensive methodology for optimal selection of algorithms (or methods) and is used here to predict the most suitable nonlinear estimator for a particular dataset. The classification rule is learned over a training database of 24 publicly available datasets. The results of the primary learning give an interesting argument in favor of the nonlinear least weighted squares estimator, which turns out to be the most suitable one for the majority of datasets. The subsequent metalearning reveals that tests of normality and heteroscedasticity play a crucial role in finding the most suitable nonlinear estimator.

Feed Efficiency Estimates in Cattle: The Economic and Environmental Impacts of Reranking

This paper proposes the application of hierarchical models to the assessment of feed efficiency in beef cattle. Using a large dataset comprising 5600 cattle assembled from different experimental studies, feed efficiency rankings of cattle were estimated using the proposed approach. This was compared to more commonly used linear, and nonlinear estimators. A phenotypic selection scheme that selects cattle at the means of different percentiles was developed to illustrate potential economic and environmental outcomes resulting from changes in feed efficiency rankings. The former involved the specification of a multi-year stochastic farm simulation model. In general, our results show that improved feed efficiency is associated with positive economic and environmental benefits. A unit reduction in feed intake (kg as fed/day) is associated with an average increase of $13.23 in net returns and 33.46 tonnes reduction in emission at the end of the feeding period. We also find that feed efficiency ranking of cattle is sensitive to estimation approach. The within percentile mean estimates of the hierarchical model were comparable to the conventional linear estimator. There were, however, deviations at the tails of feed efficiency distributions where selection is most likely to occur.

AVERAGING OF AN INCREASING NUMBER OF MOMENT CONDITION ESTIMATORS

We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of$\sqrt n$-consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achieves similar objectives of aggregating the available information. One advantage of aggregating the estimators rather than the moment conditions is that it yields robustness to certain types of parameter heterogeneity in the sense that it delivers consistent estimates of the mean effect in that case. We discuss the question of optimal weighting of the estimators.