Recently, a lot of attention has been paid to regularization based methods for sparse signal
reconstruction (eg, basis pursuit denoising and compressed sensing) and feature selection …

The problem of estimating underlying trends in time series data arises in a variety of
disciplines. In this paper we propose a variation on Hodrick–Prescott (HP) filtering, a widely …

For discrete-time linear systems, we propose a suboptimal approach to constrained
estimation so that the associated computation burden is reduced. This is achieved by …

This paper addresses the problem of testing whether the Mahalanobis distance between a
random signal Θ and a known deterministic model θ 0 exceeds some given non-negative …

The lasso and its variants have attracted much attention recently because of its ability of
simultaneous estimation and variable selection. When some prior knowledge exists in …

The ordinary least squares estimate for linear regression is sensitive to errors with large
variance. It is not robust to heavy-tailed errors or outliers, which are commonly encountered …

1 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus,
Vandalur–Kelambakkam Road, Chennai–600127, Tamil Nadu, India; joshuva1991@ gmail …

The shape-constrained problems in statistics have attracted much attention in recent
decades. One of them is the task of finding the best fitting monotone regression. The …

Structural health monitoring (SHM) systems might be exposed to broadly varying
environmental conditions that can influence the damage observations obtained by the …

The problem of constructing k-monotone regression is to find a vector z ∈ R^ n with the
lowest square error of approximation to a given vector y ∈ R^ n (not necessary k-monotone) …