A regular cross terms algorithm is derived for the parameter estimation of the multi-component polynomial phase signals in additive white Gaussian noise. The basic idea is first to separate its phase parameters into two sets by nonlinear proceduresand then each set has half of the parameters in its auto-terms. Furthermore, using two linear transforms to deal with the two signals respectively, t...

This paper proposes a robust curve and surface estimate based on M-type estimators and penalty based smoothing. This approach also includes an application to wavelet regression. The concept of pseudo data, a transformation of the robust additive model to one with bounded errors, is used to derive some theoretical properties and also motivate a computational algorithm. The resulting algorithm, t...

The problem of reconstructing an unknown waveform observed in additive noise by using normalized bispectral density estimates is considered. The proposed approach is based on using a continuous-valued normalized bispectral density estimate instead of the discontinuous biphase function conventionally computed in bispectrum-based signal reconstruction algorithms. The performance and reconstructio...

We develop a new class of overlapping Schwarz type algorithms for solving scalar convection-diiusion equations discretized by nite element or nite diierence methods. The precon-ditioners consist of two components, namely, the usual two-level additive Schwarz precon-ditioner and the sum of some quadratic terms constructed by using products of ordered neighboring subdomain preconditioners. The or...

In this paper we develop a new class of overlapping Schwarz type algorithms for solving scalar steady and unsteady convection di usion equations discretized by nite element or nite di erence methods The preconditioners consist of two components namely the usual additive Schwarz preconditioner and the sum of some second order terms constructed by using products of ordered neighboring subdomain p...

Learning to rank is the problem of ranking objects by using machine learning techniques. One of the applications of learning to rank is for ranking document of search results. In this research, we compare the performance of three learning to rank algorithms: RankSVM, LambdaMART, and Additive Groves. RankSVM, which is ranking variant of the classical SVM algorithm, is commonly used as a baseline...

We consider additive models built with trend filtering, i.e., additive models whose components are each regularized by the (discrete) total variation of their (k+1)st (discrete) derivative, for a chosen integer k ≥ 0. This results in kth degree piecewise polynomial components, (e.g., k = 0 gives piecewise constant components, k = 1 gives piecewise linear, k = 2 gives piecewise quadratic, etc.)....

Given a linear switched system composed of both Hurtwiz stable and unstable subsystems, and with additive unknown constant disturbance, a switching law with average dwell time between Hurwitz stable and unstable subsystems is proposed, together with an adaptive algorithm to compensate the unknown additive disturbance. Using Lyapunov theory, exponential stability of a desired degree of the plant...

A generalized additive model (GAM) of blue shark, Prionace glauca, catch rates (catch per set) was ®tted to data gathered by National Marine Fisheries Service (NMFS) observers stationed aboard Hawaii-based commercial longline vessels (N ˆ 2010 longline sets) from March 1994 to December 1997. Its coef®cients were then applied to the values of predictor variables, which were also contained in log...