Most of the methods among the inverse regression literature rely on a slicing of the range of the response variable. Theoretical results are usually shown assuming that (i) the slices are fixed while in practice estimators are constructed with (ii) random slices that contain the same number of observations. In this paper we obtain the asymptotic normality in the case where the slices contains t...

Inverse problems are found in many areas of science and engineering. It is usually difficult to approximate the inverse relationship due to the generally multi-valued and discontinuous nature. Several approaches to the solution of inverse problem have been proposed, but they are generally not applicable to situations and applications other than those they are developed for. In this thesis work,...

Over the past two decades, several inverse methods have been proposed to estimate scalar source and sink strengths from measured mean concentration profiles within the canopy volume (hereafter termed the ‘inverse’ problem). These inverse methods commonly assumed neutral atmospheric stability conditions for the entire canopy volume. For non-neutral conditions, atmospheric stability corrections i...

Sequence logos are stacked bar graphs that generalize the notion of consensus sequence. They employ entropy statistics very effectively to display variation in a structural alignment of sequences of a common function, while emphasizing its over-represented features. Yet sequence logos cannot display features that distinguish functional subclasses within a structurally related superfamily nor do...

The Drazin inverse has important applications in matrix theory and fields such as statistics, probability, linear systems theory, differential and difference equations, Markov chains, and control theory ([1, 2, 11]). In [9], Koliha extended the Drazin invertibility in the setting of Banach algebras with applications to bounded linear operators on a Banach space. In this paper, Koliha was able t...

Time series is attracting more attention across statistics, machine learning and pattern recognition as it appears widely in both industry and academia, but few advances has been achieved in effective time series visualization due to its temporal dimensionality and complex dynamics. Inspired by recent effort on using network metrics to characterize time series for classification, we present an ...

This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end...

This paper provides a comparison study among a set of robust diffusion algorithms for processing optical flows. The proposed algorithms combine the smoothing ability of the heat kernel, modelled by the local Hessian, and the outlier rejection mechanisms of robust statistics algorithms. Smooth optical flow variation can be modelled very well using heat kernels. The diffusion kernel is considered...

Complex nonequilibrium systems are often effectively described by a "statistics of a statistics", in short, a "superstatistics". We describe how to proceed from a given experimental time series to a superstatistical description. We argue that many experimental data fall into three different universality classes: Chi2 superstatistics (Tsallis statistics), Chi2 inverse superstatistics, and log-no...

My long-term research goals are: (1) The characterization, understanding, and prediction of the statistics (mean, variance and coherence) of low-frequency acoustic signals and ambient noise in the littoral zone. The signal statistics are primarily influenced by the ocean variability and bottom properties. The noise statistics are influenced by atmospheric forcing and shipping in addition to the...