Robust Radio Interferometric Calibration Using the t-Distribution

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t distribution. Student's t noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian noise model based calibration, traditional least squares minimization would not directly extend to a case when we have a Student's t noise model. Therefore, we use a variant of the Expectation Maximization (EM) algorithm, called the Expectation-Conditional Maximization Either (ECME) algorithm when we have a Student's t noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian noise model based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.Comment: MNRAS accepte

Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information

Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated 'physically based' watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model

Explore parameter sensitivities and model calibration in a locally coupled environment

A locally coupled Single Column Model (SCM) was used for sensitivity analysis and model calibration. The sensitivity analysis was used to identify 32 land-surface parameters which appeared to be more or less sensitive in the locally coupled environment. The multi-objective sensitive analysis shows that the land surface-atmosphere interactions could have significant influences on the model parameter sensitivities. The calibration results suggest that it is crucial to include both land-surface and atmospheric parameters in the calibration of a coupled land surface model

Financial model calibration using consistency hints

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market

Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model

Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently. In the second step, we incorporate the non-stationary behaviour, in terms of spatial variations of both variance and correlations, in the calibration. We insert two integrated nested Laplace approximation-stochastic partial differential equation parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach

LIBOR additive model calibration to swaptions markets

In the current paper, we introduce a new calibration methodology for the LIBOR market model driven by LIBOR additive processes based in an inverse problem. This problem can be splitted in the calibration of the continuous and discontinuous part, linking each part of the problem with at-the-money and in/out -of -the-money swaption volatilies. The continuous part is based on a semidefinite programming (convex) problem, with constraints in terms of variability or robustness, and the calibration of the Lévy measure is proposed to calibrate inverting the Fourier Transform

A preliminary investigation of the dynamic force-calibration of a magnetic suspension and balance system

The aerodynamic forces and moments acting upon a magnetically suspended wind tunnel model are derived from calibrations of suspension electro magnet currents against known forces. As an alternative to the conventional calibration method of applying steady forces to the model, early experiences with dynamic calibration are outlined, that is a calibration obtained by oscillating a model in suspension and deriving a force/current relationship from its inertia force and the unsteady components of currents. Advantages of dynamic calibration are speed and simplicity. The two methods of calibration applied to one force component show good agreement