This article is concerned with the estimation of a varying-coefficient regression model when the explanatory variables are measured with additive errors and the response variable is sometimes missing. Estimated coefficient function in complete observational data and interpolation data respectively, and all the proposed estimators for the coefficient function are proved to be asymptotic normalit...

We introduce a general formulation for dimension reduction and coefficient estimation in the multivariate linear model. We argue that many of the existing methods that are commonly used in practice can be formulated in this framework and have various restrictions. We continue to propose a new method that is more flexible and more generally applicable. The method proposed can be formulated as a ...

Time-varying coefficient models are useful in longitudinal data analysis. Various efforts have been invested for the estimation of the coefficient functions, based on the least squares principle. Related work includes smoothing spline and kernel methods among others, but these methods suffer from the shortcoming of non-robustness. In this paper, we introduce a local M-estimation method for esti...

New representations are developed for 2D IP (implicit polynomial) curves of arbitrary degree. These representations permit shape recognition and pose estimation with essentially single, rather than iterative, computation, and extract and use all the information in the polynomial coefficients. This is accomplished by decomposing polynomial coefficient space into a union of orthogonal subspaces f...

This paper presents a novel two-frame motion estimation algorithm. The first step is to approximate each neighborhood of both frames by quadratic polynomials, which can be done efficiently using the polynomial expansion transform. From observing how an exact polynomial transforms under translation a method to estimate displacement fields from the polynomial expansion coefficients is derived and...

A comparative analysis of the efficiency of correlation (cross-correlation, Tanimoto coefficient and Kendall's rank correlation coefficient) and information (mutual information of Tsallis and Shannon, F-information measure and entropy of the joint probability distribution) measures of image similarity for the synthesis of recursive estimation algorithms is presented for the problem of estimatin...

DS-CDMA can achieve good bit error rate (BER) performance in a severe frequency-selective fading channel by using frequency-domain equalization (FDE) based on MMSE criterion instead of rake combining. FDE requires accurate channel estimation (CE). Recently, a decision feedback (DF)MMSE-CE using a fixed DF filter coefficient has been proposed. The DF filter coefficient is an important design par...

Motivated by recent work on studying massive imaging data in various neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) with a set of covariates. Two stylized features of neuorimaging data are the presence of multiple piecewise smooth regions with unkno...

A novel channel estimator which performs channel estimation bit-wise instead of symbol-wise is proposed in this paper. Combined with superposition mapping (SM), the proposed algorithm is able to provide multiple channel estimates for a single channel coefficient. Numerical results indicate that bit-wise soft channel estimation (BWSCE) is able to outperform symbol-wise soft channel estimation (S...

In order to assign appropriate baseline estimation algorithms to different fetal heart rate tracing, a method to evaluate the fetal heart rate (FHR) baseline combining with the fetal movement information was proposed. Fetal actography and tocography were used to extract the fetal movement information. The results showed that the combined method, where the fetal movement detection result was the...