Abstract Steganography is the art and science of writing hidden messages in such a way that no one apart from the intended recipient knows of the existence of the message. In today’s world, it is widely used in order to secure the information. Since digital forensics aims to detect, recover and examine the digital evidence and steganography is a method for hiding digital evidence, detecting the...

fn(x) _ (nh d ) ' ~= 1 K((x X1)lh) where h = h n is a sequence of positive numbers, and K is an absolutely integrable function with f K(x) dx =1 . Let J, = f l f ,(x) f (x) ( dx. We show that when limnh = 0 and limnnh d = oo, then for every e > 0 there exist constants r, no > 0 such that P(Jn > e) <_ exp(-rn), n ? no. Also, when J, -p 0 in probability as n --p oo and K is a density, then limnh ...

The Nadaraya-Watson estimator of regression is known to be highly sensitive to the presence of outliers in the sample. A possible way of robusti-fication consists in using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. We show that ...

This study uses acoustic analysis to determine whether unstressed pretonic high vowels in Lezgi are deleted or devoiced. We argue that the vowel gesture is not deleted, but it is overlapped and consequently devoiced by the preceding [s] gesture. We use spectral analysis to test the increased gestural overlap hypothesis. Three results support this hypothesis and consequently the devoicing interp...

Testing of Analog-to-Digital Converters is classically composed of two successive and independent phases: the histogram-based test technique evaluating static specifications and the spectral analysis technique evaluating the dynamic performances. Consequently, the fundamental objective here is to investigate the feasibility of an alternative test flow involving exclusively spectral analysis to ...

Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996) for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative estimation and multiple bandwidths of diierent order. We derive a similar estimator in the context of local...

This paper provides a practical description of density estimation based on kernel methods. An important aim is to encourage practicing statisticians to apply these methods to data. As such, reference is made to implementations of these methods in R, S-PLUS and SAS.

Density Estimation: Deals with the problem of estimating probability density functions (PDFs) based on some data sampled from the PDF. May use assumed forms of the distribution, parameterized in some way (parametric statistics); or May avoid making assumptions about the form of the PDF (nonparametric statistics). We are concerned more here with the non-parametric case (see Roger Barlow’s lectur...

We provide stylized facts on the evolving shape dynamics of the US personal income distribution from 1962 to 2000. Based on adaptive kernel density estimation, we propose an adaptive bootstrap test for multimodality. Our results indicate that multimodality has been a predominant feature of the US income distribution. Both the number and location of modes change over time, revealing rich distrib...

We consider kernel density and regression estimation for a wide class of nonlinear time series models. Asymptotic normality and uniform rates of convergence of kernel estimators are established under mild regularity conditions. Our theory is developed under the new framework of predictive dependence measures which are directly based on the data-generating mechanisms of the underlying processes....