This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition based least squares iterative algorithm is presented for estimating the parameter vectors in each subsystem. Moreover, a data filtering based decomposition least squares iterative algorithm is proposed. The simulation...

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP...

Linear mixed-effects models are an important class of statistical models that are not only used directly in many fields of applications but also used as iterative steps in fitting other types of mixed-effects models, such as generalized linear mixed models. The parameters in these models are typically estimated by maximum likelihood (ML) or restricted maximum likelihood (REML). In general there...

Heteroskedastic regression data are modelled using a parameterized variance function. This procedure is robustified using a method with high breakdown point and high efficiency, which provides a direct link between observations and the weights used in model fitting. This feature is vital for the application, the analysis of international trade data from the European Union. Heteroskedasticity is...

Least-squares optimized polynomials are discussed which are needed in the twostep multi-bosonic algorithm for Monte Carlo simulations of quantum field theories with fermions. A recurrence scheme for the calculation of necessary coefficients in the recursion and for the evaluation of these polynomials is introduced.

Introduction 2 Signal arithmetic 3 Signals and noise 6 Smoothing 11 Differentiation 17 Resolution enhancement 26 Harmonic analysis 28 Convolution 31 Deconvolution 32 Fourier filter 34 Integration and peak area measurement 35 Linear least-squares curve fitting 37 Multicomponent spectroscopy 49 Non-linear iterative curve fitting 54 Accuracy and precision of peak parameter measurement 58 SPECTRUM ...

Introduction 2 Signal arithmetic 3 Signals and noise 6 Smoothing 11 Differentiation 17 Resolution enhancement 26 Harmonic analysis 28 Convolution 31 Deconvolution 32 Fourier filter 34 Integration and peak area measurement 35 Linear least-squares curve fitting 37 Multicomponent spectroscopy 49 Non-linear iterative curve fitting 54 Accuracy and precision of peak parameter measurement 58 SPECTRUM ...

Introduction 2 Signal arithmetic 3 Signals and noise 6 Smoothing 11 Differentiation 17 Resolution enhancement 26 Harmonic analysis 28 Convolution 31 Deconvolution 32 Fourier filter 34 Integration and peak area measurement 35 Linear least-squares curve fitting 37 Multicomponent spectroscopy 49 Non-linear iterative curve fitting 54 Accuracy and precision of peak parameter measurement 58 SPECTRUM ...