weak solutions of a class of quasilinear hyperbolic integro differential equations describing viscoelastic materials global weak solutions of scalar second order quasilinear hyperbolic integro differential equations with singular kernels are constructed perturbations of rest states are shown to propagate with finite speed smoothing effects of the solution operator are exhibited and conditions for the asymptotic stability of rest states are given the equations arise in viscoelasticity

nonparametric multivariate density estimation a comparative study the paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques where no assumption is made about the data being drawn from any of known parametric families of distribution the first type is the popular kernel method and several of its variants which uses locally tuned radial basis e g gaussian functions to interpolate the multidimensional density the second type is based on an exploratory projection pursuit technique which interprets the multidimensional density through the construction of several 1d densities along highly interesting projections of multidimensional data performance evaluations using training data from mixture gaussian and mixture cauchy densities are presented the results show that the curse of dimensionality and the sensitivity of control parameters have a much more adverse impact on the kernel density estimators than on the projection pursuit density estimators

sample size requirements for stratified cluster randomization designs sample size requirements are provided for designs of studies in which clusters are randomized within each of several strata where cluster size itself may be a stratifying factor the approach generalizes a formula derived by woolson et al which provides sample size requirements for the cochran mantel haenszel statistic issues of data analysis are also discussed

blind separation of instantaneous mixture of sources via an independent component analysis in this paper we introduce a procedure for separating a multivariate distribution into nearly independent components based on minimizing a criterion defined in terms of the kullback leibner distance by replacing the unknown density with a kernel estimate we derive useful forms of this criterion when only a sample from that distribution is available we also compute the gradient and hessian of our criteria for use in an iterative minimization setting this gradient to zero yields a set of separating functions similar to the ones considered in the source separation problem except that here these functions are adapted to the observed data finally some simulations are given illustrating the good performance of the method

correlated binomial variates properties of estimator of intraclass correlation and its effect on sample size calculation in group randomized studies the sample size calculations are complicated by within group worksite community etc correlation we compare by simulation the moment method and the more standard anova method of estimating the intraclass correlation we find the former is less biased for a small to moderate number of clusters but the difference disappears when the appropriate degree of freedom is used for the anova estimator we propose a simulation approach for sample size determination and illustrate it with an example

signal separation by symmetric adaptive decorrelation stability convergence and uniqueness the performance of signal enhancement systems based on adaptive filtering is highly dependent on the quality of the noise reference in the lms algorithm signal leakage into the noise reference leads to signal distortion and poor noise cancellation the origin of the problem lies in the fact that lms decorrelates the signal estimate with the noise reference which in the case of signal leakage makes little sense an algorithm is proposed that decorrelates the signal estimate with a signal free noise estimate obtained by adding a symmetric filter to the classical structure the symmetric adaptive decorrelation sad algorithm no longer makes a distinction between signal and noise and is therefore a signal separator rather than a noise canceler stability and convergence are of the utmost importance in adaptive algorithms and hence are carefully studied apart from limitations on the adaptation constants stability around the desired solution can only be guaranteed for a subclass of signal mixtures furthermore the decorrelation criterion does not yield a unique solution and expressions for the phantom solutions are derived simulations with short fir filters confirm the predicted behavior

sample size formulae for intervention studies with the cluster as unit of randomization this paper presents sample size formulae for both continuous and dichotomous endpoints obtained from intervention studies that use the cluster as the unit of randomization the formulae provide the required number of clusters or the required number of individuals per cluster when the other number is given the proposed formulae derive from student s t test with use of cluster summary measures and a variance that consists of within and between cluster components power contours are provided to help in the design of intervention studies that use cluster randomization sample size formulae for designs with and without stratification of clusters appear separately

information maximization and independent component analysis is there a difference this article provides a detailed and rigorous analysis of the two commonly used methods for redundancy reduction linear independent component analysis ica posed as a direct minimization of a suitably chosen redundancy measure and information maximization infomax of a continuous stochastic signal transmitted through an appropriate nonlinear network the article shows analytically that ica based on the kullbackleibler information as a redundancy measure and infomax lead to the same solution if the parameterization of the output nonlinear functions in the latter method is sufficiently rich furthermore this work discusses the alternative redundancy measures not based on the kullback leibler information distance the practical issues of applying ica and infomax are also discussed and illustrated on the problem of extracting statistically independent factors from a linear pixel by pixel mixture of images

estimation of a common odds ratio in paired cluster randomization designs we develop two estimators of a common odds ratio for designs in which the investigator randomly assigns each of two clusters to interventions within strata the estimators rely on an empirical adjustment for clustering to provide improved estimators of relative to the standard woolf and mantel haenszel estimators respectively the results of a simulation study show that the suggested adjustment improves the accuracy of both of these well known estimators under conditions likely to arise in practice we find the clustered woolf estimator as particularly effective in terms of mean squared error reduction we also discuss interval estimation

odds ratio inference with dependent data summary when data can be presented as a series of k 2 x 2 tables with cell counts xi ni xi yi mi yi it is often assumed that xi and yi are binomially distributed this paper deals with inference for the common odds ratio i l when the binomial assumption is invalid when k increases the consistency and asymptotic normality of the mantel haenszel 1959 estimator is derived the conditional maximum likelihood estimator is shown to be inconsistent and the asymptotic bias is computed when either the first order markov chain or beta binomial model is assumed the mantel haenszel test for testing f 1 is also shown to be inappropriate through some simulation studies two consistent test statistics are proposed and shown to be comparable to each other in terms of size and efficiency some possible further work is described

blind source separation using clustering based multivariate density estimation algorithm a learning algorithm is developed for blind separation of the independent source signals from their linear mixtures the algorithm is based on minimizing a contrast function defined in terms of the kullback leibler distance we use a clustering based multivariate density estimation approach to reduce the number of the parameters to be updated simulations illustrate the validity of the algorithm

estimation of the design effect in community intervention studies this paper considers the estimation of the variance of a mortality rate in community intervention studies with little or no replication of intervention regimens our approach in estimation of this cluster sampling variance is to determine the variance for simple random sampling and multiply it by a design effect which we calculate with use of information obtained from other sources when the county is the unit of randomization and the outcome is mortality we calculate the design effect as the ratio of the age adjusted mortality rates for single stage cluster sampling relative to simple random sampling we use information from all counties in a state in the calculations we apply this approach empirically for breast cancer mortality we found that these design effects were dependent on length of time for accumulation of deaths 1 1 for one year up to 3 5 for eight years and were quite consistent for the three states and nine years considered in the investigation we present a model that accounts for the time dependence of the design effect and we show it provides a good representation of the observed relationship

waveform preserving blind estimation of multiple independent sources the problem of blind estimation of source signals is to estimate the source signals without knowing the characteristics of the transmission channel it is shown that the minimum variance unbiased estimates can be obtained if and only if the transmission channel can be identified blindly it is shown that the channel can be blindly identified if and only if there is not more than one gaussian source this condition suggests that waveform preserving blind estimation can be achieved over a wide range of signal processing applications including those cases in which the source signals have identical nongaussian distributions the constructive proof of the necessary and sufficient condition serves as a foundation for the development of waveform preserving blind signal estimation algorithms examples are presented to demonstrate the applications of the theoretical results

sample size calculations for clustered binary data in this paper we propose a sample size calculation method for testing on a binomial proportion when binary observations are dependent within clusters in estimating the binomial proportion in clustered binary data two weighting systems have been popular equal weights to clusters and equal weights to units within clusters when the number of units varies cluster by cluster performance of these two weighting systems depends on the extent of correlation among units within each cluster in addition to them we will also use an optimal weighting method that minimizes the variance of the estimator a sample size formula is derived for each of the estimators with different weighting schemes we apply these methods to the sample size calculation for the sensitivity of a periodontal diagnostic test simulation studies are conducted to evaluate a finite sample performance of the three estimators we also assess the influence of misspecified input parameter values on the calculated sample size the optimal estimator requires equal or smaller sample sizes and is more robust to the misspecification of an input parameter than those assigning equal weights to units or clusters

weak solutions for a class of parabolic volterra integrodifferential equations abstract we define weak solutions for a class of volterra integrodifferential equations of the form u t au t 0t a t s g s u s ds f t u t t 0 u 0 0 the operator a is the negative infinitesimal generator of an analytic semigroup in a banach space x the operator g t u is related to a by a special form g t u a 1 2 q t u where q t u is an appropriate lower order operator we show the existence and uniqueness of weak solutions and their continuability to infinity under suitable conditions using our results we study the asymptotic behavior as time goes to infinity of strong solutions of a second order initial boundary value problem

asymptotic behavior for a viscoelastic problem with not necessarily decreasing kernel in this paper we consider a problem which arises in viscoelasticity we prove an exponential decay of solutions under weaker assumptions than the ones frequently used in the literature in particular the kernels we are considering are not necessarily exponentially decaying to zero as was assumed before the present results improve also a previous work of the authors

blind separation of mixture of independent sources through a quasi maximum likelihood approach we propose two methods for separating mixture of independent sources without any precise knowledge of their probability distribution they are obtained by considering a maximum likelihood ml solution corresponding to some given distributions of the sources and relaxing this assumption afterward the first method is specially adapted to temporally independent non gaussian sources and is based on the use of nonlinear separating functions the second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters a theoretical analysis of the performance of the methods has been made a simple procedure for optimally choosing the separating functions is proposed further in the second method a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided finally some numerical and simulation results are given illustrating the performance of the method and the good agreement between the experiments and the theory

exponential stability for the wave model with localized memory in a past history framework abstract in this paper we discuss the asymptotic stability as well as the well posedness of the damped wave equation posed on a bounded domain of r n n 2 x u t t u 0 g s div a x u t s d s b x u t 0 subject to a locally distributed viscoelastic effect driven by a nonnegative function a x and supplemented with a frictional damping b x 0 acting on a region a of where a 0 in a assuming that x is constant considering that the well known geometric control condition t 0 holds and supposing that the relaxation function g is bounded by a function that decays exponentially to zero we prove that the solutions to the corresponding partial viscoelastic model decay exponentially to zero even in the absence of the frictional dissipative effect in addition in some suitable cases where the material density x is not constant it is also possible to remove the frictional damping term b x u t that is the localized viscoelastic damping is strong enough to assure that the system is exponentially stable the semi linear case is also considered

equivariant adaptive source separation source separation consists of recovering a set of independent signals when only mixtures with unknown coefficients are observed this paper introduces a class of adaptive algorithms for source separation that implements an adaptive version of equivariant estimation and is henceforth called equivariant adaptive separation via independence easi the easi algorithms are based on the idea of serial updating this specific form of matrix updates systematically yields algorithms with a simple structure for both real and complex mixtures most importantly the performance of an easi algorithm does not depend on the mixing matrix in particular convergence rates stability conditions and interference rejection levels depend only on the normalized distributions of the source signals closed form expressions of these quantities are given via an asymptotic performance analysis the theme of equivariance is stressed throughout the paper the source separation problem has an underlying multiplicative structure the parameter space forms a matrix multiplicative group we explore the favorable consequences of this fact on implementation performance and optimization of easi algorithms

new criteria for blind deconvolution of nonminimum phase systems channels a necessary and sufficient condition for blind deconvolution without observing the input of nonminimum phase linear time invariant systems channels is derived based on this condition several optimization criteria are proposed and their solution is shown to correspond to the desired response these criteria involve the computation only of second and fourth order moments implying a simple tap update procedure the proposed methods are universal in the sense that they do not impose any restrictions on the probability distribution of the unobserved input sequence it is shown that in several important cases e g when the additive noise is gaussian the proposed criteria are essentially unaffected

analysis of data arising from a stratified design with the cluster as unit of randomization this paper discusses statistical techniques for the analysis of dichotomous data arising from a design in which the investigator randomly assigns each of two clusters of possibly varying size to interventions within strata the problem addressed is that of assessing the statistical significance of the intervention effect over all strata we propose a weighted paired t test based on the empirical logistic transform for designs that randomize large aggregate clusters in each of several strata

asymptotic behavior of a class of abstract semilinear integrodifferential equations and applications a class of semilinear hyperbolic volterra integrodifferential equations in hilbert spaces is considered the main results in this paper are the global existence of solutions to the initial problem of the equation for large data and an asymptotic estimation of the solution an application to a system relating to three dimensional viscoelastic dynamics is presented

estimation of a common odds ratio in case control studies of familial aggregation a new estimator of a common odds ratio is proposed for case control studies of familial aggregation the proposed estimator is a modification to the usual mantel haenszel estimator that relies on an empirical adjustment for the within family clustering which is typical of such designs a simulation study shows that the estimator tends to have smaller mean squared error than the unmodified mantel haenszel estimator under conditions likely to arise in practice the construction of confidence intervals is also discussed

an information maximization approach to blind separation and blind deconvolution we derive a new self organizing learning algorithm that maximizes the information transferred in a network of nonlinear units the algorithm does not assume any knowledge of the input distributions and is defined here for the zero noise limit under these conditions information maximization has extra properties not found in the linear case linsker 1989 the nonlinearities in the transfer function are able to pick up higher order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation this enables the network to separate statistically independent components in the inputs a higher order generalization of principal components analysis we apply the network to the source separation or cocktail party problem successfully separating unknown mixtures of up to 10 speakers we also show that a variant on the network architecture is able to perform blind deconvolution cancellation of unknown echoes and reverberation in a speech signal finally we derive dependencies of information transfer on time delays we suggest that information maximization provides a unifying framework for problems in blind signal processing