finite time l 2 l tracking control for markov jump repeated scalar nonlinear systems with partly usable model information this paper studies the problem of finite time l 2 l tracking control for markov jump repeated scalar nonlinear systems with partly usable model information the partly usable model information is considered following a certain bernoulli distributed white noise sequence the objective of the study is to design a state feedback controller such that the augmented closed loop system is mean square stochastically finite time bounded and a tracking performance level is achieved over a finite time interval by using the mode dependent diagonally dominant lyapunov function approach some sufficient conditions are established for the existence of an admissible state feedback controller based on these a state feedback controller can be constructed by using a simple matrix decoupling approach two numerical examples and a modified general economic model are presented to demonstrate the effectiveness of our proposed approach

survival models for heterogeneous populations derived from stable distributions summary a new three parameter family of distributions on the positive numbers is proposed it includes the stable distributions on the positive numbers the gamma the degenerate and the inverse gaussian distributions the family is characterized by the laplace transform from which moments convolutions infinite divisibility unimodality and other properties are derived the density is complicated but a simple saddlepoint approximation is provided weibull and gompertz distributions are naturally mixed over some of the distributions the family is natural exponential in one of the parameters the distributions are relevant for application as frailty distributions in life table methods for heterogeneous populations desirable properties of such distributions are discussed as an example survival after myocardial infarction is considered

asynchronous l 2 l filtering for discrete time stochastic markov jump systems with randomly occurred sensor nonlinearities this paper is concerned with the problem of asynchronous l 2 l filtering for discrete time stochastic markov jump systems with sensor nonlinearity the sensor nonlinearity is assumed to occur randomly according to a stochastic variable satisfying the bernoulli distribution a sufficient condition is first given such that the resultant filtering error system which is a kind of nonhomogeneous markov jump system is stochastically stable with a guaranteed l 2 l performance index then the existence criterion of the desired asynchronous filter with piecewise homogeneous markov chain is proposed in terms of a set of linear matrix inequalities a numerical example is given to show the effectiveness and potential of the developed theoretical results

temperature states on loop groups boson fermion correspondence and a determinant identity the boson fermion correspondence for the representation of the car algebra over l2 s1 c defined by the kms state with chemical potential is considered and the non bijectivity shown using an alternative formulation the correlations are recalculated leading to a determinant identity reminiscent of szego s theorem

resilient dissipative based controller for stochastic systems with randomly occurring gain fluctuations abstract this paper addresses the dissipativity based resilient sampled data control problem for a class of stochastic systems with actuator failures in particular the controller is assumed to posses probabilistic actuator faults with different failure rates moreover the controller gain fluctuation appears in a random way which obeys certain bernoulli distributed white noise sequences a new set of sufficient conditions is obtained in terms of linear matrix inequalities for the derivation of resilient dissipative sampled data controller by constructing an appropriate lyapunov krasovskii functional together with free weighting matrix technique simulation studies are performed to verify the performance and effectiveness of the proposed control design technique

new results on stability analysis for systems with discrete distributed delay the integral inequality technique is widely used to derive delay dependent conditions and various integral inequalities have been developed to reduce the conservatism of the conditions derived in this study a new integral inequality was devised that is tighter than existing ones it was used to investigate the stability of linear systems with a discrete distributed delay and a new stability condition was established the results can be applied to systems with a delay belonging to an interval which may be unstable when the delay is small or nonexistent three numerical examples demonstrate the effectiveness and the smaller conservatism of the method

finite time resilient reliable sampled data control for fuzzy systems with randomly occurring uncertainties abstract this paper deals with the problem of finite time passivity based h resilient reliable state feedback sampled data control for takagi sugeno fuzzy systems with randomly occurring uncertainties and gain variations where the randomly occurring characteristics are designed by stochastic variables satisfying the bernoulli distribution the main objective of this paper is to design a resilient reliable sampled data controller such that the resulting closed loop system is finite time bounded and satisfies finite time mixed h and passive performance index based on the constructed lyapunov krasovskii functional and wirtinger integral inequality some delay dependent sufficient conditions are derived for achieving the required result of the considered systems moreover the derived finite time stability conditions are established in the form of linear matrix inequalities lmis which can be solved by matlab lmi toolbox finally two numerical examples are presented to demonstrate the applicability of the proposed control design technique

bivariate survival models induced by frailties abstract when two observed survival times depend via a proportional hazards model on the same unobserved variable called a frailty this common dependence induces an association between the observed times this article considers the class of bivariate survival distributions that can arise in this way extends it to allow negative association and shows that the observable bivariate distribution determines the unobserved frailty distribution up to a scale parameter a cross ratio function easily estimated even from censored data is the key to both the characterization results and inferential procedures and diagnostic plots that are introduced and illustrated by real examples the models considered are the natural counterpart for survival data of the latent variable models that have long been used in factor analysis for continuous data or in latent structure analysis for binary data the main distinguishing feature of survival data is the possibility of censoring which makes the standard methods difficu

the probability of causation under a stochastic model for individual risk in this paper we offer a mathematical definition for the probability of causation that formalizes the legal and ordinary language meaning of the term we show that under this definition even the average probability of causation among exposed cases is not identifiable from epidemiologic data this is because the probability of causation depends both on the unknown mechanisms by which exposure affects disease risk and competing risks and on the unknown degree of heterogeneity in the background disease risk of the exposed population we derive the maximum and minimum values for the probability of causation consistent with the observable population quantities we also derive the relationship of the assigned share excess incidence rate as a proportion of total incidence rate to the probability of causation

finite time h_ infty filtering for t s fuzzy discrete time systems with time varying delay and norm bounded uncertainties in this paper we investigate the filtering problem of discrete time takagi sugeno t s fuzzy uncertain systems subject to time varying delays a reduced order filter is designed with the augmentation technique a filtering error system with delayed states is obtained in order to deal with time delays in system states the filtering error system is first transformed into two interconnected subsystems by using a two term approximation for the time varying delay sufficient delay dependent conditions of finite time boundedness and h infty performance of the filtering error system are derived with the lyapunov function based on these conditions the filter design methods are proposed and the filter gain matrices can be obtained by calculating a set of linear matrix inequalities a numerical example is used to illustrate the effectiveness of the proposed approaches

automorphisms of the canonical anticommutation relations and index theory let h be a complex hilbert space p an orthogonal projection on h and p the complementary projection if g is any symmetrically normed ideal in the ring of bounded operators on h then we consider the group of unitary operators on h such that p up and p u p lie in g when g is the hilbert schmidt class these unitaries define automorphisms of the c algebra b of the canonical anticommutation relations over h which are implementable in the representation of b determined by p we investigate the structure of the group u proving in particular that it has infinitely many connected components uk labelled by the fredholm index of p u p the connected component of the identity u0 is generated by unitaries of the form exp ia with a self adjoint and p a p in g finally we consider an application of these results to two dimensional field theory showing in particular that the charge and chiral charge quantum numbers arise as the fredholm indices of p up for certain unitary u on l2 r c2

stability analysis of sampled data systems using sum of squares this technical brief proposes a new approach to stability analysis of linear systems with sampled data inputs or channels the method based on a variation of the discrete time lyapunov approach provides stability conditions using functional variables subject to convex constraints these stability conditions can be solved using the sum of squares methodology with little or no conservatism in both the case of synchronous and asynchronous sampling numerical examples are included to show convergence

an eigenvalue decomposition for first hitting times in random walks distributions for first hitting times in random walks skip free markov chains on the non negative integers are studied their probability generating functions are expressed in terms of the eigenvalues of probability transition submatrices the study of first hitting times leads to several classes of infinitely divisible distributions closely related to the bondesson class of generalized convolutions of mixtures of exponential distributions which arises from the study of first hitting times in birth death processes some properties of these classes are derived from their characterization by pick functions

loop groups anyons and the calogero sutherland model the positive energy representations of the loop group of u 1 are used to construct a boson anyon correspondence we compute all the correlation functions of our anyon fields and study an anyonic w algebra of unbounded operators with a common dense domain this algebra contains an operator with peculiar exchange relations with the anyon fields this operator can be interpreted as a second quantized calogero sutherland cs hamiltonian and may be used to solve the cs model in particular we inductively construct all eigenfunctions of the cs model from anyon correlation functions for all particle numbers and positive couplings

optimal h 2 state feedback sampled data control design of markov jump linear systems this paper is entirely devoted to analyze and solve the h 2 optimal state feedback sampled data control design problem of markov jump linear systems several theoretical aspects are addressed in particular mean square stability and performance optimization the numerical procedure follows by embedding the system under consideration into a more general class of dynamical systems named hybrid markov jump linear system hence the state feedback sampled data control gains are calculated from the solution of a new and specific two point boundary value problem a global uniformly convergent algorithm based exclusively on linear matrix inequality is proposed the theory is illustrated by means of practical examples

matching conditional and marginal shapes in binary random intercept models using a bridge distribution function summary random effects logistic regression models are often used to model clustered binary response data regression parameters in these models have a conditional subject specific interpretation in that they quantify regression effects for each cluster very often the logistic functional shape conditional on the random effects does not carry over to the marginal scale thus parameters in these models usually do not have an explicit marginal population averaged interpretation we study a bridge distribution function for the random effect in the random intercept logistic regression model under this distributional assumption the marginal functional shape is still of logistic form and thus regression parameters have an explicit marginal interpretation the main advantage of this approach is that likelihood inference can be obtained for either marginal or conditional regression inference within a single model framework the generality of the results and some properties of the bridge distribution functions are discussed an example is used for illustration

stability for neural networks with time varying delays via some new approaches this paper considers the problem of delay dependent stability criteria for neural networks with time varying delays first by constructing a newly augmented lyapunov krasovskii functional a less conservative stability criterion is established in terms of linear matrix inequalities second by proposing novel activation function conditions which have not been proposed so far further improved stability criteria are proposed finally three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea

modeling and estimation for networked systems with multiple random transmission delays and packet losses abstract in networked systems data packets are transmitted through networks from a sensor to a data processing center due to the unreliability of communication channels a packet may be delayed even lost during the transmission at each moment the data processing center may receive one or multiple data packets or nothing at all a novel model is developed to describe the possible multiple random transmission delays and data packet losses by employing a group of bernoulli distributed random variables it is transformed to a measurement model with multiple random delayed states and noises based on the model an optimal linear filter in the linear minimum variance sense is proposed by using the orthogonal projection approach which is a universal tool to find the optimal linear estimate it does not have a steady state performance since it depends on the values of random variables that depict the phenomena of delays and losses at each moment so it needs to be computed online to reduce the online computational cost a suboptimal linear filter dependent on the probabilities of random variables is also proposed however it is worth noting that it is linearly optimal among all the linear filters dependent on the probabilities it can be computed offline since it has the steady state performance a sufficient condition of existence for the steady state performance is given a simulation example shows the effectiveness

two interpretations of the discrimination parameter in this paper we propose two interpretations for the discrimination parameter in the two parameter logistic model 2plm the interpretations are based on the relation between the 2plm and two stochastic models in the first interpretation the 2plm is linked to a diffusion model so that the probability of absorption equals the 2plm the discrimination parameter is the distance between the two absorbing boundaries and therefore the amount of information that has to be collected before a response to an item can be given for the second interpretation the 2plm is connected to a specific type of race model in the race model the discrimination parameter is inversely related to the dependency of the information used in the decision process extended versions of both models with person to person variability in the difficulty parameter are considered when fitted to a data set it is shown that a generalization of the race model that allows for dependency between choices and response times rts is the best fitting model

a remarkable property of generalized gamma convolutions it is proved that f x is the density of a generalized gamma convolution ggc then so is c 1 cx f x provided that is a nonnegative integer some classes of ggc s are introduced

survival analysis 1982 1991 the second decade of the proportional hazards regression model a short review of the cox regression model and its development from publication to 1982 is given i discuss how the model is used and presented in medical journals and how improvement may be achieved finally some extensions of the model are outlined

a parametric model for long term follow up data from phase iii breast cancer clinical trials we propose a parametric version of a univariate gamma frailty model the proposed model is shown to be flexible enough to model long term follow up survival data from breast cancer clinical trials when the treatment effect diminishes as time progresses a case for which neither the proportional hazards nor proportional odds assumptions are satisfied the observed information matrix is computed to evaluate the variances of parameter estimates a simple parametric test statistic to test proportional odds assumption is also constructed the model is applied to a data set from a phase iii clinical trial on breast cancer

nonfragile distributed filtering for t s fuzzy systems in sensor networks this paper is concerned with the nonfragile distributed h infty filtering problem for a class of discrete time takagi sugeno t s systems in sensor networks additive filter gain uncertainties that reflect imprecision in filter implementation are considered based on the robust control approach sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed h infty performance level and the eigenvalues of the filtering error system in a given circular region the filter parameters are determined by solving a set of linear matrix inequalities a simulation study on the nonlinear tunnel diode circuit system is presented to show the effectiveness of the proposed design method

wirtinger based integral inequality application to time delay systems in the last decade the jensen inequality has been intensively used in the context of time delay or sampled data systems since it is an appropriate tool to derive tractable stability conditions expressed in terms of linear matrix inequalities lmis however it is also well known that this inequality introduces an undesirable conservatism in the stability conditions and looking at the literature reducing this gap is a relevant issue and always an open problem in this paper we propose an alternative inequality based on the fourier theory more precisely on the wirtinger inequalities it is shown that this resulting inequality encompasses the jensen one and also leads to tractable lmi conditions in order to illustrate the potential gain of employing this new inequality with respect to the jensen one two applications on time delay and sampled data stability analysis are provided

two constructions of affine lie algebra representations and boson fermion correspondence in quantum field theory we establish an isomorphism between the vertex and spinor representations of affine lie algebras for types dl 1 and dl 1 2 we also study decomposition of spinor representations using the infinite family of casimir operators and prove that they are either irreducible or have two irreducible components we show that the vertex and spinor constructions of the representations can be reformulated in the language of two dimensional quantum field theory in this physical context the two constructions yield the generalized sine gordon and thirring models respectively already in renormalized form the isomorphism of representations implies an equivalence of these two models which is known in quantum field theory as the boson fermion correspondence

sampled data synchronization control of dynamical networks with stochastic sampling this technical note is concerned with the sampled data synchronization control problem for a class of dynamical networks the sampling period considered here is assumed to be time varying that switches between two different values in a random way with given probability the addressed synchronization control problem is first formulated as an exponentially mean square stabilization problem for a new class of dynamical networks that involve both the multiple probabilistic interval delays mpids and the sector bounded nonlinearities sbns then a novel lyapunov functional is constructed to obtain sufficient conditions under which the dynamical network is exponentially mean square stable both gronwall s inequality and jenson integral inequality are utilized to substantially simplify the derivation of the main results subsequently a set of sampled data synchronization controllers is designed in terms of the solution to certain matrix inequalities that can be solved effectively by using available software finally a numerical simulation example is employed to show the effectiveness of the proposed sampled data synchronization control scheme

improved fuzzy control design for nonlinear markovian jump systems with incomplete transition descriptions this paper addresses the state feedback control of nonlinear continuous time markovian jump systems the nonlinearity is represented by takagi sugeno fuzzy models and the transition probability matrix is assumed to be partly known some elements in the matrix are known some are unknown but with known lower and upper bounds and some are completely unknown by making full use of the continuous property of the transition probability matrix new sufficient conditions for the stochastic stability of the system are obtained in terms of linear matrix inequalities we show that the conditions given are less conservative than or at least the same as those for existing results moreover using the conditions obtained we establish a method for design of a h state feedback controller numerical examples illustrate the effectiveness of the proposed method

robust finite time sampled data control of linear systems subject to random occurring delays and its application to four tank system this work studies the problem of robust finite time sampled data control of linear systems subject to random occurring delays and its application to four tank system the input time delays are described by bernoulli distributed white sequences by employing a newly augmented lyapunov krasovskii functional with jensen s inequality free weighting matrix method and wirtinger based double integral inequality approach some less conservative delay dependent conditions for finite time sampled data control law are obtained finally to reveal the proposed technique an application to a four tank system is addressed to illustrate the effectiveness of developed techniques

on robustness of marginal regression coefficient estimates and hazard functions in multivariate survival analysis of family data when the frailty distribution is mis specified the shared frailty model is an extension of the cox model to correlated failure times and essentially a random effects model for failure time outcomes in this model the latent frailty shared by individual members in a cluster acts multiplicatively as a factor on the hazard function and is typically modelled parametrically one commonly used distribution is gamma where both shape and scale parameters are set to be the same to allow for unique identification of baseline hazard function it is popular because it is a conjugate prior and the posterior distribution possesses the same form as gamma in addition the parameter can be interpreted as a time independent cross ratio function a natural extension of odds ratio to failure time outcomes in this paper we study the effect of frailty distribution mis specification on the marginal regression estimates and hazard functions under assumed gamma distribution with an application to family studies the simulation results show that the biases are generally 10 and lower even when the true frailty distribution deviates substantially from the assumed gamma distribution this suggests that the gamma frailty model can be a practical choice in real data analyses if the regression parameters and marginal hazard function are of primary interest and individual cluster members are exchangeable with respect to their dependencies

the sector constants of continuous state branching processes with immigration abstract continuous state branching processes with immigration are studied we are particularly concerned with the associated non symmetric dirichlet form after observing that gamma distributions are only reversible distributions for this class of models we prove that every generalized gamma convolution is a stationary distribution of the process with suitably chosen branching mechanism and with continuous immigration for such non reversible processes the strong sector condition is discussed in terms of a characteristic called the thorin measure in addition some connections with notion from non commutative probability theory will be pointed out through calculations involving the stieltjes transform

the identifiability of the competing risks model summary this paper considers the consequences for identifiability of introducing regressors into the competing risks model of multistate duration analysis we establish conditions under which access to regressors overturns the nonidentification theorem of cox and tsiatis for both proportional and accelerated failure time models

second order reciprocally convex approach to stability of systems with interval time varying delays recently some triple integral terms in the lyapunov krasovskii functional have been introduced in the literature to reduce conservatism in the stability analysis of systems with interval time varying delays when we apply the jensen inequality to partitioned double integral terms in the derivation of lmi conditions a new kind of linear combination of positive functions weighted by the inverses of squared convex parameters emerges this paper proposes an efficient method to manipulate such a combination by extending the lower bound lemma some numerical examples are given to demonstrate the improvement of the proposed method

fermion currents in 1 1 dimensions let f denote the fock space for a free massless fermion field in two space time dimensions if q and q5 denote the charge and chiral charge operators then f n1 n2fn1 n2 where fn1 n2 is the joint eigenspace of q and q5 with corresponding eigenvalues n1 and n2 the smeared time zero free fermion currents formally given by j f x x f x dx 0 1 f s r are in fact self adjoint densely defined operators on f and their exponentials generate a c algebra which leaves each subspace fn1 n2 invariant if a periodic box cutoff for the fermions is introduced uhlenbrock has shown that this c algebra acts irreducibly in each sector fn1 n2 we prove this same result without any cutoffs and determine the properties of the representations in each sector the importance of this result for the problem of constructing fermion fields from observables in two dimensional models such as those of thirring and schwinger is discussed

finite time h fuzzy control of nonlinear markovian jump delayed systems with partly uncertain transition descriptions abstract this paper addresses a finite time h fuzzy control problem for a class of nonlinear markovian jump delayed systems with partly uncertain transition descriptions which is represented as a takagi sugeno t s fuzzy model a new homogeneous polynomial of partly uncertain transition rates is chosen free matrix based and double integral forms of the wirtinger based integral inequalities are employed to make the proposed approach less conservative then sufficient conditions are derived such that the fuzzy nonlinear makovian jump delayed system exhibits stochastic finite time boundedness finally two numerical examples are given to illustrate the effectiveness of the proposed design methodology

finite time h static output control of markov jump systems with an auxiliary approach this paper considers the finite time h static output feedback control of markov jump systems with the help of introducing two new variables related to the original system state and output sufficient conditions for the closed loop system to be stochastic finite time boundedness with the prescribed h performance level are proposed in terms of linear matrix inequalities lmis meanwhile the static output control gains are solved explicitly by the proposed conditions it is shown that the proposed method is less or at least the same conservative than the existing result lastly a numerical example is given to demonstrate the effectiveness of the proposed method

reliable state estimation of switched neutral system with nonlinear actuator faults via sampled data control this paper aims to solve the reliable state estimation problem of an uncertain switched neutral system subject to nonlinear actuator faults and time varying delay by using sampled data approach to be precise the well known luenberger estimator is constructed to determine the immeasurable states of the addressed system and a general actuator fault model is employed to describe the nonlinearities encountering in the control design after that by defining an error variable the corresponding estimation error system is formulated by the virtue of lyapunov krasovskii stability theory and average dwell time method a new delay dependent sufficient criterion is established to guarantee the existence of the desired estimator and sampled data gains and subsequently the explicit expression of those gains is presented by means of the solution to a set of linear matrix inequalities moreover two numerical examples are given to show the efficiency of the proposed estimator design schemes where it is shown that the estimator gains can ensure the exponential stability of the considered switched neutral systems

cumulative incidence association models for bivariate competing risks data association models like frailty and copula models are frequently used to analyze clustered survival data and evaluate within cluster associations the assumption of noninformative censoring is commonly applied to these models though it may not be true in many situations in this paper we consider bivariate competing risk data and focus on association models specified for the bivariate cumulative incidence function cif a nonparametrically identifiable quantity copula models are proposed which relate the bivariate cif to its corresponding univariate cifs similarly to independently right censored data and accommodate frailty models for the bivariate cif two estimating equations are developed to estimate the association parameter permitting the univariate cifs to be estimated either parametrically or nonparametrically goodness of fit tests are presented for formally evaluating the parametric models both estimators perform well with moderate sample sizes in simulation studies the practical use of the methodology is illustrated in an analysis of dementia associations

an optimization based approach to sampled data control of networked control systems with multiple delays a networked control system ncs is a control system in which plants sensors controllers and actuators are connected through communication networks in this paper the optimal sampled data control problem of linear systems with multiple time delays which is one of the fundamental problems in ncss is considered where multiple time delays and the sampling period are uncertain first the problem is transformed into the optimal control problem of discrete time uncertain linear systems where the intersample behavior is considered in the cost function next under a certain assumption the obtained problem is further transformed into a convex quadratic programming problem finally a numerical simulation is presented

feedback fuzzy control for quantized networked systems with random delays this paper investigates the problem of feedback fuzzy control design for networked systems involving both random measurement and actuation delays and subject to quantization and random packet dropout it is assumed that system state or output signal is quantized before being communicated the paper focuses initially on an observer based output feedback fuzzy controller design and then derives a state feedback controller as special case sufficient conditions for the existence of an admissible fuzzy controller are established to ensure the exponential stability of the resulting closed loop fuzzy system simulation experiments on a lab scale four tank system to demonstrate the applicability of the designed fuzzy control algorithm

multivariate survival analysis with positive stable frailties in this paper we describe bayesian modeling of dependent multivariate survival data using positive stable frailty distributions a flexible baseline hazard formulation using a piecewise exponential model with a correlated prior process is used the estimation of the stable law parameter together with the parameters of the conditional proportional hazards model is facilitated by a modified gibbs sampling procedure the methodology is illustrated on kidney infection data mcgilchrist and aisbett 1991

stability of time delay systems via wirtinger based double integral inequality based on the wirtinger based integral inequality a double integral form of the wirtinger based integral inequality hereafter called as wirtinger based double integral inequality is introduced in this paper to show the effectiveness of the proposed inequality two stability criteria for systems with discrete and distributed delays are derived within the framework of linear matrix inequalities lmis the advantage of employing the proposed inequalities is illustrated via two numerical examples

robust h control for a class of uncertain mechanical systems in this article the problem of h control is investigated for a class of mechanical systems with input delay and parameter uncertainties which appear in all the mass damping and stiffness matrices two approaches norm bounded and linear fractional transformation lft uncertainty formulations are considered by using a new lyapunov krasovskii functional approach combined with the advanced techniques for achieving delay dependence improved robust h state feedback controller design methods are developed the existence condition for admissible controllers is formulated in the form of linear matrix inequalities lmis and the controller design is cast into a convex optimisation problem subject to lmi constraints if the optimisation problem is solvable a desired controller can be readily constructed the result for the norm bounded uncertainty case improves the existing ones in terms of design conservatism and that for the lft uncertainty case represents the first attempt in this direction an ill

analysis of overdispersed count data by mixtures of poisson variables and poisson processes count data often show overdispersion compared to the poisson distribution overdispersion is typically modeled by a random effect for the mean based on the gamma distribution leading to the negative binomial distribution for the count this paper considers a larger family of mixture distributions including the inverse gaussian mixture distribution it is demonstrated that it gives a significantly better fit for a data set on the frequency of epileptic seizures the same approach can be used to generate counting processes from poisson processes where the rate or the time is random a random rate corresponds to variation between patients whereas a random time corresponds to variation within patients

finite time h control for a class of discrete time switched singular time delay systems subject to actuator saturation this paper concerns with the finite time h control problem for a class of discrete time switched singular time delay with actuator saturation not only linear matrix inequality conditions for the systems but also average dwell time of switching signal is given to guarantee regular causal and finite time boundedness for the discrete time switched singular time delay system moreover sufficient conditions are presented to ensure the h disturbance attenuation level and the design method of h controller is developed by solving linear matrix inequalities lmi optimization problem without any decompositions of system matrices and equivalent transformation furthermore the function in the proof procedure belongs to multiple lyapunov like functions whose advantage lies in their flexibility finally numerical examples are employed to verify the effectiveness of the proposed methods and to illustrate the significant improvement on the conservativeness of some reported results in the literature

a linear regression model for the analysis of life times a linear model is suggested for the influence of covariates on the intensity function this approach is less vulnerable than the cox model to problems of inconsistency when covariates are deleted or the precision of covariate measurements is changed a method of non parametric estimation of regression functions is presented this results in plots that may give information on the change over time in the influence of covariates a test method and two goodness of fit plots are also given the approach is illustrated by simulation as well as by data from a clinical trial of treatment of carcinoma of the oropharynx

life table methods for heterogeneous populations distributions describing the heterogeneity taking account of heterogeneity between the individuals in population based mortality studies is important a systematic way of describing heterogeneity is by an unobserved quantity called frailty entering the hazard multiplicatively until now most studies have used a gamma distributed frailty which is mathematically convenient for example the distribution among survivors is also gamma this paper shows that several other distributions have equally simple properties the main example being the inverse gaussian distribution consequences of the different distributions are examined the inverse gaussian makes the population homogeneous with time whereas for the gamma the relative heterogeneity is constant

finite time h reliable control for t s fuzzy systems with variable sampling abstract this work mainly concentrates on the finite time h reliable control for t s fuzzy systems with aperiodic sampling by considering the information of the state an improved lyapunov krasovskii functional lkf is structured moreover based on free weighted method and aperiodic sampling techniques further criteria are proposed to guarantee that the t s fuzzy systems is finite time stabilization with h performance meanwhile the h reliable controller is designed with larger sampling interval finally the advantages of the obtained results are affirmed by a truck trailer system

implementability of gauge transformations and quantization of fermions in external fields quantization of fermions in an external soliton field leading to a representation of the canonical anticommutation relation car which is inequivalent to the representation connected to the massive dirac operator is studied classes of gauge and axial gauge transformations that can be unitarily implemented are determined in the latter case quantization conditions for gauge functions are obtained integers entering can be interpreted as winding numbers

a note on the boson fermion correspondence and infinite dimensional groups we show how to construct irreducible projective representations of the infinite dimensional lie group map s1 mathbb t by embedding it into the group of bogoliubov automorphisms of the car using techniques of g segal for extending certain representations of map s1 su 2 we show that our representations extend to give representations of a certain infinite dimensional superalgebra we relate our work to the well known boson fermion correspondence which exists in 1 1 dimensions

temperature states on loop groups theta functions and the luttinger model abstract we consider projective representations of the loop group of u n suggested by statistical mechanics these representations are defined by certain positive definite functions called temperature states on the universal central extension of the loop group we find that the boson fermion correspondence known to exist in a mathematically precise form at zero temperature persists at non zero temperature by expressing the temperature state for u 1 in two different ways we obtain identities between elliptic functions we apply these ideas to a simple model in statistical physics the luttinger model establishing the existence of a projective representation of the loop group of u 1 u 1 in the presence of interaction and at all temperatures a rigorous derivation of the correlation functions for this model is given using the boson fermion correspondence

flexible modeling of longitudinal highly skewed outcomes the analysis of data from epidemiologic and environmental studies presents challenges such as skewness of distribution rounding and multiple measurements over time to model trends over time based on repeated measurements we propose a general latent model suitable for highly skewed data the model assumes that the observed outcome is determined by an unobservable outcome that follows a weibull distribution to accommodate correlations among repeated responses over time we introduce a general random effect from the power variance function pvf family of distributions including the gamma distribution often employed in the literature the resulting marginal likelihood has a closed form without resorting to numerical or approximation methods we study estimation and hypothesis testing under these models with different choices of random effect distributions simulation studies are conducted to evaluate their performance finally we apply the proposed method to exposure data collected from the michigan polybrominated biphenyl mipbb study

analysis of failure time data with multilevel clustering with application to the child vitamin a intervention trial in nepal we consider the problem of estimating covariate effects in the marginal cox proportional hazard model and multilevel associations for child mortality data collected from a vitamin a supplementation trial in nepal where the data are clustered within households and villages for this purpose a class of multivariate survival models that can be represented by a functional of marginal survival functions and accounts for hierarchical structure of clustering is exploited based on this class of models an estimation strategy involving a within cluster resampling procedure is proposed and a model assessment approach is presented the asymptotic theory for the proposed estimators and lack of fit test is established the simulation study shows that the estimates are approximately unbiased and the proposed test statistic is conservative under extremely heavy censoring but approaches the size otherwise the analysis of the nepal study data shows that the association of mortality is much greater within households than within villages

quantifying the association between progression free survival and overall survival in oncology trials using kendall s τ this paper considers methods for estimating the association between progression free and overall survival in oncology trials copula based nonparametric and illness death model based methods are reviewed in addition the approach based on an underlying illness death model is generalized to allow general parametric models the performance of these methods in terms of bias and efficiency is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer the simulations suggest that the illness death model based method provides good estimates of kendall s across several scenarios in some situations copula based methods perform well but their performance is sensitive to the choice of copula the clayton copula is most appropriate in scenarios which might realistically reflect an oncology trial but the use of copula models in practice is questionable

generalized gamma convolutions and complete monotonicity lete be the class ofpdf sf on 0 such that for eachu 0 f uv f u v is completely monotone as a function ofw v v 1 this class includes many familiarpdf s and is closed with respect to multiplication and division of independentrv s further e t wheret is the class of generalized gamma convolutions ggc introduced by o thorin moreover e coincides with the class ofpdf s of the form c cdot x beta 1 cdot mathop pi limits i 1 n 1 c i x y i all parameters positive or limits thereof the laplace transform of aggc is characterized by complete monotonicity of uv u v as a function ofw this characterization has many consequences and applications it follows that also the classt has simple multiplicative properties

characterizations of natural exponential families with power variance functions by zero regression properties series of new characterizations by zero regression properties are derived for the distributions in the class of natural exponential families with power variance functions such a class of distributions has been introduced in bar lev and enis 1986 in the context of an investigation of reproductible exponential families this class is broad and includes the following families normal poisson type gamma all families generated by stable distributions with characteristic exponent an element of the unit interval among these are the inverse gaussian modified bessel type and whittaker type distributions and families of compound poisson distributions generated by gamma variates the characterizations by zero regression properties are obtained in a unified approach and are based on certain relations which hold among the cumulants of the distributions in this class some remarks are made indicating how the techniques used here can be extended to obtain characterizations of general exponential families

sampled data synchronization of chaotic lur e systems via input delay dependent free matrix zero equality approach abstract this paper focuses on the sampled data synchronization problem of chaotic lur e systems clss by using sampled output of the systems with variable sampling rates one novel approach input delay dependent free matrix zero equality iddfmze approach is proposed for the first time the iddfmze approach can not only fully capture the available information on the actual sampling pattern but deploy more system information at the dynamic partitioning point a new lyapunov krasovskii functional lkf with some new terms is constructed which can use more information of the activation function at the dynamic partitioning point based on the presented iddfmze approach and the constructed lkf developed synchronization criterion is obtained in the form of linear matrix inequalities lmis the desired sampled data controller is designed under larger sampling period finally the superiority of proposed results is shown by two numerical examples