This article considers the nonparametric maximum likelihood estimator (MLE) for the joint distribution function of an interval censored survival time and a continuous mark variable. We derive a new explicit formula for the MLE that has both computational and theoretical advantages. Using this formula and the mark specific cumulative hazard function of Huang & Louis (1998), we derive the almost ...

In this study, a multiscale neural network learning paradigm based on empirical mode decomposition (EMD) is proposed for crude oil price prediction. In this learning paradigm, the original price series are first decomposed into various independent intrinsic mode components (IMCs) with a range of frequency scales. Then the internal correlation structures of different IMCs are explored by neural ...

The acquisition and the analysis of electrophysiological signals are often followed by the detection of parameters of clinical importance. In the case of fetal electrocardiogram signal, the presence of QRS complex is the best indicator of the fetal heart rate, as it is directly correlated with the fetal cardiac activity. However, to detect this complex, it is necessary to separate the signal fr...

The object of this paper is to establish a theorem for a unique common fixed point of four self mappings, weakly compatibile in pairs and satisfying a generalized contractive condition in a cone metric space. Our result generalizes and synthesizes the results of Abbas– Jungck [1], Arshad et al. [2], Huang–Zhang [3] and Vetro [8].

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...