cox s regression model for counting processes a large sample study the cox regression model for censored survival data specifies that covariates have a proportional effect on the hazard function of the life time distribution of an individual in this paper we discuss how this model can be extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process this permits a statistical regression analysis of the intensity of a recurrent event allowing for complicated censoring patterns and time dependent covariates furthermore this formulation gives rise to proofs with very simple structure using martingale techniques for the asymptotic properties of the estimators from such a model finally an example of a statistical analysis is included

synchronization of ts fuzzy complex dynamical networks with time varying impulsive delays and stochastic effects in this paper global exponential synchronization of takagi sugeno ts fuzzy complex dynamical networks with multiple time varying delays and stochastic perturbations is studied via delayed impulsive distributed control both impulsive and continuous parts of the fuzzy model have different multiple time varying delays which make the considered model general and practical a novel lemma on exponential stability of impulsive delayed differential equations was established in which the impulsive functions exhibit multiple time varying delays by utilizing the proposed lemma lyapunov functions the stochastic analysis techniques and the kronecker product general criteria ensuring global exponential synchronization in mean square of the addressed ts fuzzy complex networks are obtained moreover the theory of function minimum value is utilized to reduce the conservativeness of the obtained synchronization criteria which also makes them simple and easy to be verified in practical applications results of this paper improve and extend some existing ones numerical simulations including small world network coupled with time delayed lorenz system are given to show the effectiveness of the theoretical results

analyzing the robustness of impulsive synchronization coupled by linear delayed impulses in this technical note a class of autonomous impulsive differential systems with linear delayed impulses is considered sufficient conditions required for this particular class of systems with varying and constant impulse durations to be equi attractive in the large are obtained these conditions are then applied to impulsively synchronize two coupled chaotic systems by using delayed impulses and a robustness analysis of the model is also provided simulation results are given to demonstrate the analytical results

on the stationary solutions of the 2d doi onsager model abstract in this paper we study the 2d doi onsager model 1 f z 1 e u f on s 1 with the potential 2 u f u l f s 1 sin l f d where l is a positive integer is a parameter f is a probability distribution function we derive differential equations equivalent to 1 and 2 for all positive integers l then use the corresponding variational structure to obtain the existence of solutions in particular non constant solutions when l 1 1 and 2 are the original onsager models and in this case we prove that all solutions must be axially symmetric and there exist infinitely many such solutions

stability analysis and design of impulsive control systems with time delay a class of impulsive control systems with time varying delays is considered by establishing an impulsive delay differential inequality we analyze the global exponential stability of the impulsive delay systems and estimate the exponential convergence rate on the basis of the analysis a design procedure of impulsive controller is presented the designed impulsive controller not only can globally exponentially stabilize the time delay systems but also can control the exponential convergence rate of the systems two numerical examples are given to illustrate the effectiveness of the method

coupling strength computation for chaotic synchronization of complex networks with multi scroll attractors they were synchronized complex networks with regular and irregular topology it was analyzed the stability of the synchronization error system it was obtained the coupling strength by an alternative method it was reduced the coupling strength and were given its lower and upper boundary in this paper synchronization of n coupled chaotic oscillators with multi scroll attractors is presented n chaotic oscillators are coupled in regular and irregular topologies the generalizations of the genesio tesi and chua s chaotic oscillators are used as generators of multi scroll attractors an alternative scheme for computing the coupling strength is proposed synchronization is achieved through the coupling matrix and by using the resulting alternative values in general the range of values obtained with the proposed method is smaller than the one given by wang chen method the effectiveness of this coupling strength is verified through numerical simulations

hybrid adaptive and impulsive synchronization of uncertain complex networks with delays and general uncertain perturbations this paper is concerned with the problem of asymptotic synchronization for a class of uncertain complex networks with delays and general uncertain perturbations in order to cope with the bad effects generated by the uncertain perturbations a novel hybrid adaptive and impulsive controller is designed such that the complex network can be asymptotically synchronized onto an isolate chaotic system with uncertain perturbations all the perturbations can be different from each other on the basis of a new lemma squeezing rule and lyapunov krasovskii functionals several sufficient conditions guaranteeing the realization of the synchronization goal are derived it is shown that the designed hybrid controllers exhibit powerful robustness some existing results are improved and extended numerical simulations verify the effectiveness of the theoretical results and the robustness of the new controller

cluster synchronization of complex networks via event triggered strategy under stochastic sampling abstract this paper is concerned with the issue of mean square cluster synchronization of non identical nodes connected by a directed network suppose that the nodes possess nonlinear dynamics and split into several clusters then an event triggered control scheme is proposed for synchronization based on the information from stochastic sampling meanwhile an equilibrium is considered to be the synchronization state or the virtual leader for each cluster which can apply pinning control to the following nodes assume that a spanning tree exists in the subgraph consisting of the nodes belonging to the same cluster and the corresponding virtual leader and the instants for updating controllers are determined by the given event triggered strategy then some sufficient conditions for cluster synchronization are presented according to the lyapunov stability theory and linear matrix inequality technique finally a specific numerical example is shown to demonstrate the effectiveness of the theoretical results

exponential synchronization of complex valued complex networks with time varying delays and stochastic perturbations via time delayed impulsive control considering the fact that time delays are unavoidable in the control of practical systems this paper considers globally exponential synchronization of complex valued complex dynamical networks with multiple time varying delays and stochastic perturbations by designing a time delayed impulsive control scheme by taking the advantage of lyapunov method in complex field and utilizing an impulsive inequality with delays several synchronization criteria are obtained through strict mathematical proofs our results are general which extend some existing ones concerning impulsive synchronization a numerical example is given to illustrate the effectiveness of theoretical results

cluster synchronization for nonlinearly time varying delayed coupling complex networks with stochastic perturbation via periodically intermittent pinning control this paper investigate the mean square exponential cluster synchronization of a class complex networks with nonlinear hybrid time varying delays and stochastic noises via periodically intermittent pinning control to be more practical the network topology can be directed and the time delay is assumed to be time varying based on the lyapunov stability theory stochastic analysis theory and linear matrix inequalities lmi the sufficient conditions are derived to guarantee the mean square exponential cluster synchronization furthermore our results can cover and extend the related ones in existing literatures some numerical simulations are provided to show the effectiveness of our theoretical results

flcrm functional linear cox regression model ummary we consider a functional linear cox regression model for characterizing the association between time to event data and a set of functional and scalar predictors the functional linear cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high dimensional cox regression model to characterize the joint effects of both functional and scalar predictors on the time to event data we develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters we also systematically investigate the rate of convergence of the maximum approximate partial likelihood estimates and a score test statistic for testing the nullity of the slope function associated with the functional predictors we demonstrate our estimation and testing procedures by using simulations and the analysis of the alzheimer s disease neuroimaging initiative adni data our real data analyses show that high dimensional hippocampus surface data may be an important marker for predicting time to conversion to alzheimer s disease data used in the preparation of this article were obtained from the adni database adni loni usc edu

generalized functional linear models we propose a generalized functional linear regression model for a regression situation where the response variable is a scalar and the predictor is a random function a linear predictor is obtained by forming the scalar product of the predictor function with a smooth parameter function and the expected value of the response is related to this linear predictor via a link function if in addition a variance function is specified this leads to a functional estimating equation which corresponds to maximizing a functional quasi likelihood this general approach includes the special cases of the functional linear model as well as functional poisson regression and functional binomial regression the latter leads to procedures for classification and discrimination of stochastic processes and functional data we also consider the situation where the link and variance functions are unknown and are estimated nonparametrically from the data using a semiparametric quasi likelihood procedure an essential step in our proposal is dimension reduction by approximating the predictor processes with a truncated karhunen loeve expansion we develop asymptotic inference for the proposed class of generalized regression models in the proposed asymptotic approach the truncation parameter increases with sample size and a martingale central limit theorem is applied to establish the resulting increasing dimension asymptotics we establish asymptotic normality for a properly scaled distance between estimated and true functions that corresponds to a suitable l 2 metric and is defined through a generalized covariance operator as a consequence we obtain asymptotic tests and simultaneous confidence bands for the parameter function that determines the model the proposed estimation inference and classification procedures and variants with unknown link and variance functions are investigated in a simulation study we find that the practical selection of the number of components works well with the aic criterion and this finding is supported by theoretical considerations we include an application to the classification of medflies regarding their remaining longevity status based on the observed initial egg laying curve for each of 534 female medflies

on stationary solutions of the 2d doi onsager model abstract we study the 2d doi onsager models with general potential kernel with special emphasis on the classical onsager kernel through application of topological methods from nonlinear functional analysis in particular the leray schauder degree theory we obtain the uniqueness of the trivial solution for low temperatures as well as the local bifurcation structure of the solutions

functional censored quantile regression we propose a functional censored quantile regression model to describe the time varying relationship between time to event outcomes and corresponding functional covariates the time varying effect

extended dissipative state estimation for markov jump neural networks with unreliable links this paper is concerned with the problem of extended dissipativity based state estimation for discrete time markov jump neural networks nns where the variation of the piecewise time varying transition probabilities of markov chain is subject to a set of switching signals satisfying an average dwell time property the communication links between the nns and the estimator are assumed to be imperfect where the phenomena of signal quantization and data packet dropouts occur simultaneously the aim of this paper is to contribute with a markov switching estimator design method which ensures that the resulting error system is extended stochastically dissipative in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links sufficient conditions for the solvability of such a problem are established based on the derived conditions an explicit expression of the desired markov switching estimator is presented finally two illustrated examples are given to show the effectiveness of the proposed design method

stringing high dimensional data for functional analysis we propose stringing a class of methods where one views high dimensional observations as functional data stringing takes advantage of the high dimension by representing such data as discretized and noisy observations that originate from a hidden smooth stochastic process assuming that the observations result from scrambling the original ordering of the observations of the process stringing reorders the components of the high dimensional vectors followed by transforming the high dimensional vector observations into functional data established techniques from functional data analysis can be applied for further statistical analysis once an underlying stochastic process and the corresponding random trajectory for each subject have been identified stringing of high dimensional data is implemented with distance based metric multidimensional scaling mapping high dimensional data to locations on a real interval such that predictors that are close in a suitable sample metric also are located close to each

global orientation dynamics for liquid crystalline polymers abstract we study global orientation dynamics of the doi smoluchowski equation with the maier saupe potential on the sphere which arises in the modeling of rigid rod like molecules of polymers using the orientation tensor we first reconfirm the structure and number of equilibrium solutions established in h l liu h zhang p w zhang axial symmetry and classification of stationary solutions of doi onsager equation on the sphere with maier saupe potential comm math sci 3 2 2005 201 218 we then examine global orientation dynamics in terms of eigenvalues of the orientation tensor via the doi closure approximation it is shown that for small intensity 0 4 all states will evolve into the isotropic phase for large intensity 4 5 all states will evolve into the nematic prolate phase and for the intermediate intensity 4 4 5 an initial state will evolve into either the isotropic phase or the stable phase of two nematic prolate phases depending on whether such an initial configuration crosses a critical threshold moreover the uniaxial symmetry structure is shown to be preserved in time

optimal estimation for the functional cox model functional covariates are common in many medical biodemographic and neuroimaging studies the aim of this paper is to study functional cox models with right censored data in the presence of both functional and scalar covariates we study the asymptotic properties of the maximum partial likelihood estimator and establish the asymptotic normality and efficiency of the estimator of the finite dimensional estimator under the framework of reproducing kernel hilbert space the estimator of the coefficient function for a functional covariate achieves the minimax optimal rate of convergence under a weighted l 2 risk this optimal rate is determined jointly by the censoring scheme the reproducing kernel and the covariance kernel of the functional covariates implementation of the estimation approach and the selection of the smoothing parameter are discussed in detail the finite sample performance is illustrated by simulated examples and a real application

brief paper a unified synchronization criterion for impulsive dynamical networks this paper focuses on the problem of globally exponential synchronization of impulsive dynamical networks two types of impulses are considered synchronizing impulses and desynchronizing impulses in previous literature all of the results are devoted to investigating these two kinds of impulses separately thus a natural question arises is there any unified synchronization criterion which is simultaneously effective for synchronizing impulses and desynchronizing impulses in this paper a unified synchronization criterion is derived for directed impulsive dynamical networks by proposing a concept named average impulsive interval the derived criterion is theoretically and numerically proved to be less conservative than existing results numerical examples including scale free and small world structures are given to show that our results are applicable to large scale networks

finite time cluster synchronization of t s fuzzy complex networks with discontinuous subsystems and random coupling delays this paper is concerned with the cluster synchronization in finite time for a class of complex networks with nonlinear coupling strengths and probabilistic coupling delays the complex networks consist of several clusters of nonidentical discontinuous systems suffered from uncertain bounded external disturbance based on the takagi sugeno t s fuzzy interpolation approach we first obtain a set of t s fuzzy complex networks with constant coupling strengths by developing some novel lyapunov functionals and using the concept of filippov solution some new analytical techniques are established to derive sufficient conditions ensuring the cluster synchronization in a setting time in particular this paper extends the pinning control strategies for networks with continuous time dynamics to discontinuous networks numerical simulations demonstrate that the theoretical results are effective and the t s fuzzy approach is important for relaxed results

on the stationary solutions of doi onsager model in general dimension abstract we give new results of the phase transition of dilute colloidal solutions of rod like molecules in dimension d 3 for the low concentration of particles in a carrier fluid we prove that the isotropic phase is the unique solution to the doi onsager model with the general potential kernel in addition we present the regime of the bifurcation of nematic phases in the class of axially symmetric solutions our method is based on a generalization of the classical leray schauder degree we developed for this problem

exponential synchronization of coupled stochastic memristor based neural networks with time varying probabilistic delay coupling and impulsive delay this paper deals with the exponential synchronization of coupled stochastic memristor based neural networks with probabilistic time varying delay coupling and time varying impulsive delay there is one probabilistic transmittal delay in the delayed coupling that is translated by a bernoulli stochastic variable satisfying a conditional probability distribution the disturbance is described by a wiener process based on lyapunov functions halanay inequality and linear matrix inequalities sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained numerical simulations are used to show the effectiveness of the theoretical results

matrix measure strategies for exponential synchronization and anti synchronization of memristor based neural networks with time varying delays this paper is concerned with exponential synchronization and anti synchronization of memristor based neural networks under the framework of filippov systems and a linear controller the exponential synchronization and anti synchronization criteria for memristor based neural networks can be guaranteed by the matrix measure and halanay inequality the criteria are very simple to implement in practice finally two numerical examples are given to demonstrate the correctness of the theoretical results it is shown that the matrix measure can increase the exponential convergence rate and decrease the feedback gain effectively