Abstract: A smoothing finite impulse response (FIR) filter is addressed for discrete time-invariant state-space polynomial models commonly used to represent signals over finite data. A general gain is derived for the relevant p-lag unbiased smoothing FIR filter. Applications are given for the time interval errors of a local crystal clock and the United States Naval Observatory Master Clock. An ...

Introduction Local canonical correlation analysis (CCA) is a multivariate method that takes into account the spatial correlation of neighboring voxels rather than treating each voxel time series individually. Mathematically, CCA is a generalization of the general linear model (GLM), and is defined by maximizing the correlation of a linear combination of voxel time series in a local region and a...

We propose a method for analyzing data which consist of curves on multiple individuals, i.e., longitudinal or functional data. We use a Bayesian model where curves are expressed as linear combinations of B-splines with random coefficients. The curves are estimated as posterior means obtained via Markov chain Monte Carlo (MCMC) methods, which automatically select the local level of smoothing. Th...

The varying-coeecient model allows to investigate and visualize the form of the interaction of variables in the predictor. Moreover, common approaches like semiparametric modelling and generalized linear models are special cases. The focus is on local estimators, in particular local likelihood and locally weighted least squares estimators, which both are consistent and asymptotically normally d...

In this paper, a sequential system of linear equations (SSLE) algorithm for solving mathematical problem with linear complementarity constraints is introduced, which uses Fischer-Burmeister (F-B) function and smoothing technique to rewrite CONG ZHANG et al. 150 the complementarity constraints ”. “ 0 0 ≥ ⊥ ≤ w y Under some suitable conditions without upper level complementarity, the proposed met...

A variance reduction technique in nonparametric smoothing is proposed: at each point of estimation, form a linear combination of a preliminary estimator evaluated at nearby points with the coefficients specified so that the asymptotic bias remains unchanged. The nearby points are chosen to maximize the variance reduction. We study in detail the case of univariate local linear regression. While ...