To estimate geometrically regular images in the white noise model and obtain an adaptive near asymptotic minimaxity result, we consider a model selection based bandlet estimator. This bandlet estimator combines the best basis selection behaviour of the model selection and the approximation properties of the bandlet dictionary. We derive its near asymptotic minimaxity for geometrically regular i...

Functional data analysis is typically performed in two steps: first, functionally representing discrete observations, and then applying functional methods, such as the principal component analysis, to so-represented data. While initial choice of a representation may have significant impact on second phase this issue has not gained much attention past. Typically, rather ad hoc some standard basi...

The paper proposes to combine an orthogonal least squares (OLS) model selection with local regularisation for efficient sparse kernel data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.

Multiple-Input multiple-output (MIMO) technology is capable of enhancing capacity and coverage of wireless links. Implementation of MIMO devices is intricate because of the increased number of radio frequency chains (one for each antenna element). Antenna selection is one attractive approach to mitigate this requirement. It only utilizes an optimal subset of all available antennas, adapting it ...

This paper presents a method for effectively detecting patterns and clusters in high dimensional time-dependent functional data. It is based on waveletbased similarity measures since wavelets are ideal for identifying highly discriminant local time and scale features. We consider the contribution of each scale to the global energy, in the orthogonal wavelet transform of each input function to g...

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is i...