This paper considers a semiparametric version of the transformation model φ(Y ) = β′ X + U under exogeneity or instrumental variables assumptions (E(U |X) = 0 or E(U |instruments ) = 0). This model is used as an example to illustrate the practice of the estimation by solving linear functional equations. This paper is specially focused on the data driven selection of the regularization parameter...

In this paper, we extend our previous image resolution enhancement results in [1] by proposing a technique for the estimation of the regularization parameter based on the assumption it should satisfy the following properties: It should be a function of the regularized noise power of the data and its choice should yield a convex functional whose minimization would give the desired high-resolutio...

This paper focuses on the development of parameter estimation techniques for models quantifying hysteresis and constitutive nonlinearities in ferroelectric materials. These models are formulated as integral equations with known kernels and unknown densities to be identified through least squares fit to data. Due to the compactness of the integral operators, the resulting discretized models inhe...

This paper presents an automatic FDP (Facial Definition Parameters) and FAP (Facial Animation Parameters) generation method from an image sequence that captures a frontal face. The proposed method is based on facial feature tracking without markers on a face. We present an efficient method to extract 2D facial features and to generate the FDP by applying 2D features to a generic face model. We ...

We introduce a method for MR parameter mapping based on three concepts: 1) an inversion recovery, variable flip angle acquisition strategy designed for speed, signal, and contrast; 2) a compressed sensing reconstruction which exploits spatiotemporal correlations through low rank regularization; and 3) a model-based optimization to simultaneously estimate proton density, T1, and T2 values from t...

In this work, a method for estimating the space-dependent perfusion coefficient parameter in a 2D bioheat transfer model is presented. In the method, the bioheat transfer model is transformed into a time-dependent semidiscrete system of ordinary differential equations involving perfusion coefficient values as parameters, and the estimation problem is solved through a nonlinear least squares tec...

This paper addresses the problem of semantic parsing, by which natural language sentences are translated into a form which conveys their underlying meaning. Semantic parsing involves a parameter estimation process, which is a convex optimization problem. The optimization formulation of previous approaches often requires huge amount of time to converge due to the high dimensional feature space. ...

Abstract We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as singular value decomposition with sparse vectors. The traditional estimation procedure for often fails when true rank high. To overcome this issue, we develop an algorithm and variable selection via regularization manifold optimization, which enables us to obtain accura...

In noise removal by the approach of regularization, the regularization parameter is global. Constructing the variational model min g ‖f − g‖L2(R) + αR(g),g is in some wavelets space. Through the wavelets pyramidal decompose and the different time-frequency properties between noise and signal, the regularization parameter is adaptively chosen, the different parameter is chosen in different level...