Boosting Estimation of RBF Neural Networks for Dependent Data

This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specification testing illustrates the usefulness of the results.Neural Networks, Boosting

Iterative Estimation of Solutions to Noisy Nonlinear Operator Equations in Nonparametric Instrumental Regression

This paper discusses the solution of nonlinear integral equations with noisy integral kernels as they appear in nonparametric instrumental regression. We propose a regularized Newton-type iteration and establish convergence and convergence rate results. A particular emphasis is on instrumental regression models where the usual conditional mean assumption is replaced by a stronger independence assumption. We demonstrate for the case of a binary instrument that our approach allows the correct estimation of regression functions which are not identifiable with the standard model. This is illustrated in computed examples with simulated data

Real-time diffuse optical tomography using reduced-order light propagation models based on a priori anatomical and functional information

This paper proposes a new fast 3D image reconstruction algorithm for Diffuse Optical Tomography using reduced order polynomial mappings from the space of optical tissue parameters into the space of flux measurements at the detector locations. The polynomial mappings are constructed through an iterative estimation process involving structure detection, parameter estimation and cross-validation using data generated by simulating a diffusion approximation of the radiative transfer equation incorporating a priori anatomical and functional information provided by MR scans and prior psychological evidence. Numerical simulation studies demonstrate that reconstructed images are remarkably similar in quality as those obtained using the standard approach, but obtained at a fraction of the time

S-AMP: Approximate Message Passing for General Matrix Ensembles

In this work we propose a novel iterative estimation algorithm for linear observation systems called S-AMP whose fixed points are the stationary points of the exact Gibbs free energy under a set of (first- and second-) moment consistency constraints in the large system limit. S-AMP extends the approximate message-passing (AMP) algorithm to general matrix ensembles. The generalization is based on the S-transform (in free probability) of the spectrum of the measurement matrix. Furthermore, we show that the optimality of S-AMP follows directly from its design rather than from solving a separate optimization problem as done for AMP.Comment: 5 pages, 1 figur

Iterative Estimation of the Extreme Value Index

Let {Xn, n ≥ 1} be a sequence of independent random variables with common continuous distribution function F having finite and unknown upper endpoint. A new iterative estimation procedure for the extreme value index γ is proposed and one implemented iterative estimator is investigated in detail, which is asymptotically as good as the uniform minimum varianced unbiased estimator in an ideal model. Moreover, the superiority of the iterative estimator over its non iterated counterpart in the non asymptotic case is shown in a simulation stud

Interrogation of Employees Concerning Union Matters as an Unfair Labor Practice

The problemof estimating parameters of amultivariate normal p-dimensional random vector is considered for a banded covariance structure reflecting mdependence. A simple non-iterative estimation procedure is suggested which gives an explicit, unbiased and consistent estimator of the mean and an explicit and consistent estimator of the covariance matrix for arbitrary p and m.Preliminary version published as Research Report 2008:3 at the Centre of Biostochastics Swedish University of Agricultural Sciences.The original publication is available at www.springerlink.com:Martin Ohlson, Zhanna Andrushchenko and Dietrich von Rosen, Explicit Estimators under m-Dependence for a Multivariate Normal Distribution, 2011, Annals of the Institute of Statistical Mathematics, (63), 1, 29-42.http://dx.doi.org/10.1007/s10463-008-0213-1Copyright: Springer Science Business Mediahttp://www.springerlink.com

Contribution Among Joint Tortfeasors and the Marital Immunity

Estimation of parameters in the classical Growth Curve model when the covariance matrix has some specific linear structure is considered. In our examples maximum likelihood estimators can not be obtained explicitly and must rely on optimization algorithms. Therefore explicit estimators are obtained as alternatives to the maximum likelihood estimators. From a discussion about residuals, a simple non-iterative estimation procedure is suggested which gives explicit and consistent estimators of both the mean and the linear structured covariance matrix.Original Publication:Martin Ohlson and Dietrich von Rosen, Explicit Estimators of Parameters in the Growth Curve Model with Linearly Structured Covariance Matrices, 2010, Journal of Multivariate Analysis, (101), 5, 1284-1295.http://dx.doi.org/10.1016/j.jmva.2009.12.023Copyright: Elsevier Science B.V., Amsterdamhttp://www.elsevier.com

Iterative Estimation of Variance Components in the 2-Way Crossed Classification, Mixed Model, with Interaction, Using Unbalanced Data

24 pages, 1 article*Iterative Estimation of Variance Components in the 2-Way Crossed Classification, Mixed Model, with Interaction, Using Unbalanced Data* (Corbeil, R. R.; Searle, S. R.) 24 page