misspecified proportional hazard models summary the properties of an estimator based on a proportional hazards model are investigated when the model is incorrect the estimator from the partial likelihood is shown to be consistent for a parameter that is defined implicitly the results are used to investigate the effects on estimation if the true model is accelerated failure time or if covariates are omitted from the proportional hazards model

stability analysis and synthesis of nonlinear time delay systems via linear takagi sugeno fuzzy models abstract this paper concerns with the stability analysis and synthesis of nonlinear retarded systems via linear takagi sugeno ts fuzzy model approach first the ts fuzzy models with time delay are present and the stability conditions are derived using lyapunov razumikhin functional approach then a stabilization approach for nonlinear retarded systems through fuzzy state feedback and fuzzy observer based controller is proposed it shows that the analysis results provide an efficient technique for the design of fuzzy controllers sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain of the time delay systems are derived through the numerical solution of a set of coupled linear matrix inequalities

reliable asynchronous sampled data filtering of t s fuzzy uncertain delayed neural networks with stochastic switched topologies abstract this paper investigates the issue of the reliable asynchronous sampled data filtering of takagi sugeno t s fuzzy delayed neural networks with stochastic intermittent faults randomly occurring time varying parameters uncertainties and controller gain fluctuation the asynchronous phenomenon occurs between the system modes and controller modes first in order to reduce the utilization rate of communication bandwidth a novel alterable sampled data terminal method is considered via variable sampling rates second based on the fuzzy model based control approach improved reciprocally convex inequality and new parameter time dependent discontinuous lyapunov approach several relaxed conditions are derived and compared with the existing work third the intermittent fault tolerance scheme is also taken into fully account in designing a reliable asynchronous sampled data controller which ensures such that the resultant neural networks is asymptotically stable finally two numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results

semiparametric transformation models for semicompeting survival data semicompeting risk outcome data e g time to disease progression and time to death are commonly collected in clinical trials however analysis of these data is often hampered by a scarcity of available statistical tools as such we propose a novel semiparametric transformation model that improves the existing models in the following two ways first it estimates regression coefficients and association parameters simultaneously second the measure of surrogacy for example the proportion of the treatment effect that is mediated by the surrogate and the ratio of the overall treatment effect on the true endpoint over that on the surrogate endpoint can be directly obtained we propose an estimation procedure for inference and show that the proposed estimator is consistent and asymptotically normal extensive simulations demonstrate the valid usage of our method we apply the method to a multiple myeloma trial to study the impact of several biomarkers on patients semicompeting outcomes namely time to progression and time to death

quantifying the effect of the surrogate marker by information gain statistical validation of a surrogate marker has been studied for more than a decade recently alonso et al 2004 biometrics 60 724 728 proposed a quantity called the likelihood reduction factor lrf to evaluate the validity of a surrogate marker however as pointed out in the present article the lrf may not correctly validate a surrogate marker therefore a new quantity the proportion of information gain pig using the kullback leibler information is proposed simulations show that under some model assumptions the pig precisely reflects the role of a surrogate marker

analysis synthesis techniques for subband image coding analysis synthesis systems designed for low bit rate image coding their impact on overall system quality and their computational complexity are discussed the investigation focuses on the design of analysis synthesis systems for image coding and the perceptual impact of these systems at low bit rates two objectives are emphasized in developing these systems confining the total size of the subband images to be equal to the original image size and designing the filters so that perceptual distortion is not introduced by the analysis synthesis system methods based on circular convolution and symmetric extensions are developed and discussed in detail the theory design and implementation of both recursive and nonrecursive filtering systems are discussed methods are introduced which display advantages over conventional quadrature mirror filter based approaches

on the optimality of ideal filters for pyramid and wavelet signal approximation the reconstructed lowpass component in a quadrature mirror filter qmf bank provides a coarser resolution approximation of the input signal since the outputs of the two qmf branches are orthogonal the transformation that provides the maximum energy compaction in the lowpass channel is also the one that results in the minimum approximation error this property is used as a common strategy for the optimization of qmf banks orthogonal wavelet transforms and least squares pyramids a general solution is derived for the qmf bank that provides the optimal decomposition of an arbitrary wide sense stationary process this approach is extended to the continuous case to obtain the minimum error approximation of a signal at a given sampling rate in particular it is shown that the sine wavelet transform is optimal for the representation at all scales of signals with non increasing spectral density

matched sampling systems relation to wavelets and implementation using prcc filter banks this paper deals with two types of sampling systems namely the interpolation and approximation sampling systems closed form expressions are derived for the frequency responses of the filters used in these systems that are matched to the input process in the mean squared sense closed form expressions are also derived for the mean squared error between the input and the reconstructed processes for these matched sampling systems using these expressions it is shown that the meyer scaling function and wavelet or functions derived from these arise naturally in the context of subsampled bandlimited processes to implement these systems the perfect reconstruction circular convolution prcc filter bank is proposed as a framework for the frequency sampled implementation of these systems examples of matched interpolation and approximation sampling systems are provided and their performance is compared with some standard interpolators to demonstrate their efficacy

regression analysis of multivariate incomplete failure time data by modeling marginal distributions abstract many survival studies record the times to two or more distinct failures on each subject the failures may be events of different natures or may be repetitions of the same kind of event in this article we consider the regression analysis of such multivariate failure time observations each marginal distribution of the failure times is formulated by a cox proportional hazards model no specific structure of dependence among the distinct failure times on each subject is imposed the regression parameters in the cox models are estimated by maximizing the failure specific partial likelihoods the resulting estimators are shown to be asymptotically jointly normal with a covariance matrix that can be consistently estimated simultaneous inferential procedures are then proposed extensive monte carlo studies indicate that the normal approximation is adequate for practical use the new methods allow time dependent covariates missing observations and arbitrary patterns of censorship they are illustrat

new criteria for global robust stability of delayed neural networks with norm bounded uncertainties in this paper we study the global asymptotic robust stability of delayed neural networks with norm bounded uncertainties by employing the lyapunov stability theory and homeomorphic mapping theorem we derive some new types of sufficient conditions ensuring the existence uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slope bounded activation functions an important aspect of our results is their low computational complexity as the reported results can be verified by checking some properties of symmetric matrices associated with the uncertainty sets of the network parameters the obtained results are shown to be generalizations of some of the previously published corresponding results some comparative numerical examples are also constructed to compare our results with some closely related existing literature results

criteria for the validation of surrogate endpoints in randomized experiments the validation of surrogate endpoints has been studied by prentice 1989 statistics in medicine 8 431 440 and freedman graubard and schatzkin 1992 statistics in medicine 11 167 178 we extended their proposals in the cases where the surrogate and the final endpoints are both binary or normally distributed letting t and s be random variables that denote the true and surrogate endpoint respectively and z be an indicator variable for treatment prentice s criteria are fulfilled if z has a significant effect on t and on s if s has a significant effect on t and if z has no effect on t given s freedman relaxed the latter criterion by estimating pe the proportion of the effect of z on t that is explained by s and by requiring that the lower confidence limit of pe be larger than some proportion say 0 5 or 0 75 this condition can only be verified if the treatment has a massively significant effect on the true endpoint a rare situation we argue that two other quantities must be considered in the validation of a surrogate endpoint re the effect of z on t relative to that of z on s and gamma z the association between s and t after adjustment for z a surrogate is said to be perfect at the individual level when there is a perfect association between the surrogate and the final endpoint after adjustment for treatment a surrogate is said to be perfect at the population level if re is 1 a perfect surrogate fulfills both conditions in which case s and t are identical up to a deterministic transformation fieller s theorem is used for the estimation of pe re and their respective confidence intervals logistic regression models and the global odds ratio model studied by dale 1986 biometrics 42 909 917 are used for binary endpoints linear models are employed for continuous endpoints in order to be of practical value the validation of surrogate endpoints is shown to require large numbers of observations

stability condition for sampled data based control of linear continuous switched systems abstract most practical systems are continuous in nature but with discrete sampled feedback when digital control is utilized this paper investigates the stabilization problem of switched linear continuous time systems with sampled data based control for both known and unknown arbitrary switching processes on the basis of lyapunov stability theory a sufficient global exponential stability condition related to dwell time and sampling period is established for the latter case on line one step adaptive estimation algorithm is derived and integrated with sampled feedback for control design validation and verification of the established result are conducted through cruise control of train systems

robust stability and stabilizing controller design of fuzzy systems with discrete and distributed delays in this paper we consider delay dependent stability conditions of takagi sugeno fuzzy systems with discrete and distributed delays although many kinds of stability conditions for fuzzy systems with discrete delays have already been obtained almost no stability condition for fuzzy systems with distributed delays has appeared in the literature this is also true in case of the robust stability for uncertain fuzzy systems with distributed delays here we employ a generalized lyapunov functional to obtain delay dependent stability conditions of fuzzy systems with discrete and distributed delays we introduce some free weighting matrices to such a lyapunov functional in order to reduce the conservatism in stability conditions these techniques lead to generalized and less conservative stability conditions we also consider the robust stability of fuzzy time delay systems with uncertain parameters applying the same techniques made on the stability conditions we obtain delay dependent sufficient conditions for the robust stability of uncertain fuzzy systems with discrete and distributed delays moreover we consider the state feedback stabilization based on stability and robust stability conditions we obtain conditions for the state feedback controller to stabilize the fuzzy time delay systems finally we give two examples to illustrate our results delay dependent stability conditions obtained here are shown to guarantee a wide stability region

estimating the proportion of treatment effect explained by a surrogate marker in this paper we measure the extent to which a biological marker is a surrogate endpoint for a clinical event by the proportional reduction in the regression coefficient for the treatment indicator due to the inclusion of the marker in the cox regression model we estimate this proportion by applying the partial likelihood function to two cox models postulated on the same failure time variable we show that the resultant estimator is asymptotically normal with a simple variance estimator one can construct confidence intervals for the proportion by using the direct normal approximation to the point estimator or by using fieller s theorem extensive simulation studies demonstrate that the proposed methods are appropriate for practical use we provide applications to hiv aids clinical trials

principal component filter banks for optimal multiresolution analysis an important issue in multiresolution analysis is that of optimal basis selection an optimal p band perfect reconstruction filter bank prfb is derived in this paper which minimizes the approximation error in the mean square sense between the original signal and its low resolution version the resulting prfb decomposes the input signal into uncorrelated low resolution principal components with decreasing variance optimality issues are further analyzed in the special case of stationary and cyclostationary processes by exploiting the connection between discrete time filter banks and continuous wavelets an optimal multiresolution decomposition of l sub 2 r is obtained analogous results are also derived for deterministic signals some illustrative examples and simulations are presented

quantification of the prentice criteria for surrogate endpoints in a recent discussion on the prentice criteria for surrogate endpoints 1989 and the role of meta analyses in this context alonso et al expand on a number of points among these is a suggestion to base a measure of the degree at which an endpoint meets the prentice criteria on a likelihood reduction factor although new in this context the suggestion has been studied elsewhere and the measure of alonso et al as presented requires some modification if it is to be useful in general situations as it stands it depends upon the censoring mechanism even when independent of the failure mechanism this is however easily fixed the measure suggested by alonso et al can be seen to fit in with a well established theory so that once corrected for the presence of censoring it can be employed with confidence of particular interest is the wide availability of a standard sas program producing a coefficient which can be immediately transformed to provide an estimate of the population quantity behind the alonso et al proposal

stabilization of sampled data control systems with control inputs missing this note is concerned with the stabilization problem for sampled data control systems with control inputs missing a sufficient condition is derived for the existence of exponentially stabilizing state feedback controllers via the switched system approach the obtained condition establishes several quantitative relations among some system parameters such as the sampling period and the exponential decay rate the actual data missing rate and the admissible data missing rate bound two illustrative examples are given to show the effectiveness of the proposed results

fast algorithms for discrete and continuous wavelet transforms several algorithms are reviewed for computing various types of wavelet transforms the mallat algorithm 1989 the a trous algorithm and their generalizations by shensa the goal of this work is to develop guidelines for implementing discrete and continuous wavelet transforms efficiently and to compare the various algorithms obtained and give an idea of possible gains by providing operation counts most wavelet transform algorithms compute sampled coefficients of the continuous wavelet transform using the filter bank structure of the discrete wavelet transform although this general method is already efficient it is shown that noticeable computational savings can be obtained by applying known fast convolution techniques such as the fft fast fourier transform in a suitable manner the modified algorithms are termed fast because of their ability to reduce the computational complexity per computed coefficient from l to log l within a small constant factor for large filter lengths l for short filters smaller gains are obtained fast running fir finite impulse response filtering techniques allow one to achieve typically 30 savings in computations

augmented designs to assess principal strata direct effects summary many studies involving causal questions are often concerned with understanding the causal pathways by which a treatment affects an outcome thus the concept of direct versus indirect effects comes into play we tackle the problem of disentangling direct and indirect effects by investigating new augmented experimental designs where the treatment is randomized and the mediating variable is not forced but only randomly encouraged there are two key features of our framework we adopt a principal stratification approach and we mainly focus on principal strata effects avoiding involving a priori counterfactual outcomes using non parametric identification strategies we provide a set of assumptions which allow us to identify partially the causal estimands of interest the principal strata direct effects some examples are shown to illustrate our design and causal estimands of interest large sample bounds for the principal strata average direct effects are provided and a simple hypothetical example is used to show how our augmented design can be implemented and how the bounds can be calculated finally our augmented design is compared and contrasted with a standard randomized design

non fragile memory filtering of t s fuzzy delayed neural networks based on switched fuzzy sampled data control abstract this paper deals with the non fragile memory filtering issue of t s fuzzy delayed neural networks with randomly occurring time varying parameters uncertainties and variable sampling rates compared with existing sampled data control schemes an improved switched fuzzy memory sampled data control protocol is designed for the first time which involves not only a signal transmission delay but also switched topologies by developing some new terms and taking full advantage of the variable characteristics related to the actual sampling pattern a modified loose looped fuzzy membership functions fmfs dependent lyapunov krasovskii functional lkf is constructed based on the information of the time derivative of fmfs meanwhile some relaxed matrices chosen in lkf are not consequentially positive definite moreover with the lkf methodology and employing the developed estimation technique several optimized control algorithms with both a larger sampling period and upper bound of time varying delays for achieving the stabilization of the resultant t s fuzzy delayed neural networks are derived finally a numerical example is presented to demonstrate the superiority and applicability of the theoretical results

surrogate marker evaluation from an information theory perspective the last 20 years have seen lots of work in the area of surrogate marker validation partly devoted to frame the evaluation in a multitrial framework leading to definitions in terms of the quality of trial and individual level association between a potential surrogate and a true endpoint buyse et al 2000 biostatistics 1 49 67 a drawback is that different settings have led to different measures at the individual level here we use information theory to create a unified framework leading to a definition of surrogacy with an intuitive interpretation offering interpretational advantages and applicable in a wide range of situations our method provides a better insight into the chances of finding a good surrogate endpoint in a given situation we further show that some of the previous proposals follow as special cases of our method we illustrate our methodology using data from a clinical study in psychiatry

decentralized h fuzzy filter for nonlinear large scale sampled data systems with uncertain interconnections abstract this paper proposes a decentralized h fuzzy filter technique for nonlinear large scale interconnected systems that are based on the takagi sugeno fuzzy model the large scale system has unknown interconnection terms that are assumed to satisfy the quadratic bounds an error system between the nonlinear large scale system and the decentralized filter is constructed by using the fuzzy lyapunov technique sufficient conditions are proposed for both showing asymptotic stability and guaranteeing h fuzzy filter performance these sufficient conditions are derived in terms of linear matrix inequalities finally simulation examples are given to demonstrate the effectiveness of the proposed technique

minimum mean squared error reconstruction for generalized undersampling of cyclostationary processes the generalized sampling problem is considered using a filter bank model with a stochastic process framework a signal reconstruction solution minimizing the time averaged mean squared error is found using a filter bank optimization technique for cyclostationary signals the use of a stochastic model approach to find a minimized error solution rather than a perfect reconstruction solution allows consideration of sampling problems that deterministic approaches cannot solve for instance a particular sampling density is not required allowing reconstruction optimization in cases of undersampling an extension is also presented for optimal reconstruction in the presence of additive noise and interference the result is a generalized sampling model which can be used for an extensive variety of scenarios combining aspects of undersampling multiple input multiple output mimo sampling and periodic nonuniform sampling

the robust inference for the cox proportional hazards model abstract we derive the asymptotic distribution of the maximum partial likelihood estimator for the vector of regression coefficients under a possibly misspecified cox proportional hazards model as in the parametric setting this estimator converges to a well defined constant vector in addition the random vector n 1 2 is asymptotically normal with mean 0 and with a covariance matrix that can be consistently estimated the newly proposed robust covariance matrix estimator is similar to the so called sandwich variance estimators that have been extensively studied for parametric cases for many misspecified cox models the asymptotic limit or part of it can be interpreted meaningfully in those circumstances valid statistical inferences about the corresponding covariate effects can be drawn based on the aforementioned asymptotic theory of and the related results for the score statistics extensive studies demonstrate that the proposed robust tests and interval estimation procedures

a novel control design on discrete time takagi sugeno fuzzy systems with time varying delays this paper focuses on analyzing a new model transformation of discrete time takagi sugeno t s fuzzy systems with time varying delays and applying it to dynamic output feedback dof controller design a new comparison model is proposed by employing a new approximation for time varying delay state and then a delay partitioning method is used to analyze the scaled small gain of this comparison model a sufficient condition on discrete time t s fuzzy systems with time varying delays which guarantees the corresponding closed loop system to be asymptotically stable and has an induced l2 disturbance attenuation performance is derived by employing the scaled small gain theorem then the solvability condition for the induced l2 dof control is also established by which the dof controller can be solved as linear matrix inequality optimization problems finally examples are provided to illustrate the effectiveness of the proposed approaches

sampled data filtering of takagi sugeno fuzzy neural networks with interval time varying delays abstract this paper is concerned with sample data filtering of t s fuzzy neural networks with interval time varying delays which is formed by a fuzzy plant with time delay and a sampled data fuzzy controller connected in a closed loop a takagi sugeno t s fuzzy model is adopted for the neural networks and the sampled data fuzzy controller is designed for a t s fuzzy system to develop the guaranteed cost control a new stability condition of the closed loop system is guaranteed in the continuous time lyapunov sense and its sufficient conditions are formulated in terms of linear matrix inequalities by using a descriptor representation the sampled data fuzzy control system with time delay can be reduced to ease the stability analysis which effectively leads to a smaller number of lmi stability conditions information of the membership functions of both the fuzzy plant model and fuzzy controller are considered which allows arbitrary matrices to be introduced to ease the satisfaction of the stability conditions by a newly proposed inequality bounding technique the fuzzy sampled data filtering performance analysis is carried out such that the resultant neural networks is asymptotically stable numerical example and simulation result are given to illustrate the usefulness and effectiveness of the proposed theoretical results

delay dependent h control for uncertain fuzzy systems with time varying delays this paper addresses the robust stabilization and robust h control problems of uncertain fuzzy delayed systems via a parallel distributed compensation pdc scheme the uncertainties are norm bounded and the delay may be either constant or time varying and either differentiable or non differentiable presented in this paper are some robust stabilization and robust h control criteria which are delay dependent in general and delay derivative dependent when the delay is differentiable these results enjoy much less conservatism and more computational simplicity compared with those in the existing literature and permit if the delay is differentiable the derivative to be larger than one some examples are given to illustrate the results in this paper

brief paper new delay dependent stability criteria for systems with interval delay this paper provides a new delay dependent stability criterion for systems with a delay varying in an interval with a different lyapunov functional defined a tight upper bound of its derivative is given the resulting criterion has advantages over some previous ones in that it involves fewer matrix variables but has less conservatism which is established theoretically examples are provided to demonstrate the advantage of the stability result

new delay dependent stability criteria for t s fuzzy systems with time varying delay this paper is concerned with the stability problem of uncertain t s fuzzy systems with time varying delay by employing a further improved free weighting matrix approach by taking the relationship among the time varying delay its upper bound and their difference into account some less conservative lmi based delay dependent stability criteria are obtained without ignoring any useful terms in the derivative of lyapunov krasovskii functional finally two numerical examples are given to demonstrate the effectiveness and the merits of the proposed methods

relaxed stability and stabilization conditions of networked fuzzy control systems subject to asynchronous grades of membership this paper presents relaxed stability and stabilization conditions for takagi sugeno t s fuzzy systems under network environments subject to asynchronous grades of membership because of the introduction of a communication network the favorable property in the point to point connection that is sharing the identical premises in the fuzzy plant and the fuzzy controllers cannot be arbitrarily employed to widen the applicability of the fuzzy control method under network environments a novel method is provided to reconstruct the synchronous time scale grades of membership at the controller as those in the plant as a result the aforementioned favorable property can be conditionally used in the derivation of the stability and stabilization criteria for the system under consideration controlled over a communication network numerical examples have been given to illustrate the effectiveness of the proposed approach

new approaches on stability criteria for neural networks with interval time varying delays abstract this paper concerns the problem of delay dependent stability criteria for neural networks with interval time varying delays first by constructing a newly augmented lyapunov krasovskii functional and combining with a reciprocally convex combination technique less conservative stability criterion is established in terms of linear matrix inequalities lmis which will be introduced in theorem 1 second by taking different interval of integral terms of lyapunov krasovskii functional utilized in theorem 1 further improved stability criterion is proposed in theorem 2 third a novel approach which divides the bounding of activation function into two subinterval are proposed in theorem 3 to reduce the conservatism of stability criterion finally through two well known numerical examples used in other literature it will be shown the proposed stability criteria achieves the improvements over the existing ones and the effectiveness of the proposed idea

global exponential convergence of fuzzy complex valued neural networks with time varying delays and impulsive effects abstract in this paper the global exponential convergence of t s fuzzy complex valued neural networks with time varying delays and impulsive effects is discussed by employing lyapunov functional method and matrix inequality technique we analyze a type of activation functions with lipschitz function and sufficient conditions in terms of complex valued linear matrix inequality are obtained to ensure the global exponential convergence moreover the framework of the exponential convergence ball in the state space of the considered neural networks and the exponential convergence rate index are also given out here the existence and uniqueness of the equilibrium points need not be considered and the results improve existing results on the lyapunov exponential stability as special cases finally one numerical example with simulations is given to illustrate the effectiveness of our theoretical results

novel delay dependent robust h control of uncertain systems with distributed time varying delays this paper investigates the problem of delay dependent robust h control for a class of uncertain systems with distributed time varying delays the aim is to design a delay dependent robust h control which ensures robust asymptotic stability of the system the delay derivative dependent robust h control criteria are obtained in terms of linear matrix inequalities lmis numerical examples are given to illustrate the effectiveness of the proposed method the results are also compared with the existing results to show the conservativeness

brief paper a novel stability analysis of linear systems under asynchronous samplings this article proposes a novel approach to assess the stability of continuous linear systems with sampled data inputs the method which is based on the discrete time lyapunov theorem provides easy tractable stability conditions for the continuous time model sufficient conditions for asymptotic and exponential stability are provided dealing with synchronous and asynchronous samplings and uncertain systems an additional stability analysis is provided for the cases of multiple sampling periods and packet losses several examples show the efficiency of the method