analysis of multilevel methods for eddy current problems in papers by arnold falk and winther and by hiptmair novel multigrid methods for discrete h curl elliptic boundary value problems have been proposed such problems frequently occur in computational electromagnetism particularly in the context of eddy current simulation this paper focuses on the analysis of those nodal multilevel decompositions of the spaces of edge finite elements that form the foundation of the multigrid methods it provides a significant extension of the existing theory to the case of locally vanishing coefficients and nonconvex domains in particular asymptotically uniform convergence of the multigrid method with respect to the number of refinement levels can be established under assumptions that are satisfied in realistic settings for eddy current problems the principal idea is to use approximate helmholtz decompositions of the function space h curl into an h1 regular subspace and gradients the main results of standard multilevel theory for h1 elliptic problems can then be applied to both subspaces this yields preliminary decompositions still outside the edge element spaces judicious alterations can cure this

eddy current model for nondestructive evaluation with thin cracks in this paper we propose an approximate eddy current model for nondestructive evaluation interior cracks of large steel structures are very thin compared with the characteristic length of the system numerical methods usually necessitate very fine meshes to characterize the small thickness of cracks and thus yield a very large number of degrees of freedom the proposed model neglects the thickness of cracks and treats them as interior surfaces the existence and uniqueness are established for the approximate solution upon introducing proper gauge conditions the convergence of the approximate solution to the solution of the original eddy current problem is proved as the thickness of cracks tends to zero and an error estimate is presented for homogeneous conducting materials a coupled finite element method is proposed to solve the approximate problem the well posedness and the error estimate are proved for the discrete solution numerical experiments are carried out for engineering benchmark problems

dissipativity based filtering for fuzzy switched systems with stochastic perturbation in this technical note the problem of the dissipativity based filtering problem is considered for a class of t s fuzzy switched systems with stochastic perturbation firstly a sufficient condition of strict dissipativity performance is given to guarantee the mean square exponential stability for the concerned t s fuzzy switched system then our attention is focused on the design of a filter to the t s fuzzy switched system with brownian motion by combining the average dwell time technique with the piecewise lyapunov function technique the desired fuzzy filters are designed that guarantee the filter error dynamic system to be mean square exponential stable with a strictly dissipative performance and the corresponding solvability condition for the fuzzy filter is also presented based on the linearization procedure approach finally an example is provided to illustrate the effectiveness of the proposed dissipativity based filter technique

adaptive fuzzy backstepping tracking control for strict feedback systems with input delay this paper investigates the problem of adaptive fuzzy tracking control for nonlinear strict feedback systems with input delay and output constraint input delay is handled based on the information of pade approximation and output constraint problem is solved by barrier lypaunov function some adaptive parameters of the controller need to be updated online through considering the norm of membership function vector instead of all sub vectors a novel adaptive fuzzy tracking control scheme is developed to guarantee all variables of the closed loop systems are semiglobally uniformly ultimately bounded and the tracking error can be adjusted around the origin with a small neighborhood the stability of the closed loop systems is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach

alternative variance and efficiency calculations for the case cohort design summary we define a bootstrap sampling plan for the case cohort design and present bootstrap variance estimates and confidence intervals for the log relative hazard the coverages of these confidence intervals are found to be near nominal levels in simulations a simple null variance calculation based on a superpopulation model is shown to provide good estimates of the power and efficiency of the case cohort design

robust h control of uncertain fuzzy systems under time varying sampling robust h sampled data control for uncertain fuzzy systems is discussed in many practical situations a system is modeled as a continuous time fuzzy system while the control input is the zero order hold which can be represented as a piecewise continuous delay here we take a delay system approach to the h sampled data control problem the closed loop system with a sampled data state feedback controller becomes a system with time varying delay first h performance conditions for the closed loop system are given in terms of linear matrix inequalities lmis such conditions are derived by using the leibniz newton formula and free weighting matrix method for fuzzy time delay systems under the assumption that sampling time is not greater than some prescribed number then a design method for a robust h sampled data state feedback controller for uncertain fuzzy systems is proposed numerical examples are given to illustrate our robust h sampled data state feedback control design

model approximation for discrete time state delay systems in the t s fuzzy framework this paper is concerned with the problem of h model approximation for discrete time takagi sugeno t s fuzzy time delay systems for a given stable t s fuzzy system our attention is focused on the construction of a reduced order model which not only approximates the original system well in an h performance but is also translated into a linear lower dimensional system by applying the delay partitioning approach a delay dependent sufficient condition is proposed for the asymptotic stability with an h error performance for the error system then the h model approximation problem is solved by using the projection approach which casts the model approximation into a sequential minimization problem subject to linear matrix inequality lmi constraints by employing the cone complementary linearization algorithm moreover by further extending the results h model approximation with special structures is obtained i e delay free model and zero order model finally two numerical examples are provided to illustrate the effectiveness of the proposed methods

an improved input delay approach to stabilization of fuzzy systems under variable sampling in this paper we investigate the problem of stabilization for sampled data fuzzy systems under variable sampling a novel lyapunov krasovskii functional lkf is defined to capture the characteristic of sampled data systems and an improved input delay approach is proposed by the use of an appropriate enlargement scheme new stability and stabilization criteria are obtained in terms of linear matrix inequalities lmis compared with the existing results the newly obtained ones contain less conservatism some illustrative examples are given to show the effectiveness of the proposed method and the significant improvement on the existing results

robust sampled data fuzzy control for nonlinear systems and its applications free weight matrix method in this paper sampled data control is proposed to stabilize the nonlinear system which is expressed as a takagi sugeno t s fuzzy submodels based on suitable lyapunov krasovskii functional lkf along with new weighted integral inequalities the sufficient conditions are derived in terms of linear matrix inequalities which ensure the exponential stability of the proposed closed loop t s fuzzy system the peculiarity of this paper is as novel integral inequalities and lkf are proposed less conservative results are provided when compared to existing results besides that wind energy conversion systems chaotic systems and an inverted pendulum are validated with derived sufficient conditions the corresponding simulation results show the merit of the proposed results over the existing works

nonfragile retarded sampled data switched control of t s fuzzy systems and its applications this article investigates the nonfragile retarded fuzzy sampled data control design of takagi sugeno t s fuzzy system when compared to the traditional sampled data control scheme a constant signal transmission delay is involved with uncertainties in control gain parameters which exploited for the first time to tackle the stabilization analysis of the closed loop t s fuzzy systems based on the switched control idea by constructing a fuzzy membership function fmf dependent lyapunov krasovskii functional lkf and utilizing the time derivative information of the fmf the sufficient conditions are carried out in terms of linear matrix inequalities lmis such that the resultant t s fuzzy systems can achieve the globally asymptotically stable under the designed control then the desired control gain is determined by solving the obtained lmis finally the numerical simulations are demonstrated to show the advantage and applicability of the derived sufficient conditions

maximum likelihood estimation in the proportional odds model abstract we consider maximum likelihood estimation of the parameters in the proportional odds model with right censored data the estimator of the regression coefficient is shown to be asymptotically normal with efficient variance the maximum likelihood estimator of the unknown monotonic transformation of the survival time converges uniformly at a parametric rate to the true transformation estimates for the standard errors of the estimated regression coefficients are obtained by differentiation of the profile likelihood and are shown to be consistent a likelihood ratio test for the regression coefficient is also considered

a case cohort design for epidemiologic cohort studies and disease prevention trials summary suppose that a cohort of individuals is to be followed in order to relate failure rates to preceding covariate histories a design is proposed which involves covariate data only for cases experiencing failure and for members of a randomly selected subcohort odds ratio and relative risk estimation procedures are presented for such a case cohort design a small simulation study compares case cohort relative risk estimation procedures to full cohort and synthetic case control analyses relevance to epidemiologic cohort studies and disease prevention trials is discussed

adaptive synchronization of uncertain chaotic systems based on t s fuzzy model this paper presents an adaptive approach for synchronization of takagi sugeno t s fuzzy chaotic systems t s fuzzy model can represent a general class of nonlinear system and we employ it for fuzzy modeling of the chaotic drive system since the output of the drive system is only available for synchronization the response system is designed based on fuzzy adaptive observer for uncertain parameters and parameter mismatch cases we analyze the stability of the overall fuzzy synchronization system by applying lyapunov stability theory and derive stability conditions by solving linear matrix inequalities lmis problem the adaptive law is derived to estimate the uncertain parameters or parameter mismatch numerical examples are given to demonstrate the validity of the proposed fuzzy adaptive synchronization approach

fuzzy model based sampled data control of chaotic systems a fuzzy time dependent lyapunov krasovskii functional approach this paper addresses the sampled data stabilization problem for chaotic systems represented by takagi sugeno t s fuzzy models if the upper bounds for the time derivative of membership functions are available combining the fuzzy blending for some quadratic functions together with the introduction of some new useful terms a novel fuzzy time dependent lyapunov krasovskii functional lkf is proposed to fully capture the available characteristics of the actual sampling pattern and membership functions simultaneously based on the proposed lkf a new criterion dependent on the upper bounds for the time derivative of membership functions is presented to guarantee the asymptotic stability of the whole closed loop system moreover a stability criterion independent of the upper bounds is also provided based on the corresponding common time dependent lkf then the designed fuzzy sampled data controller can be synthesized by analyzing the corresponding stabilization conditions moreover a search algorithm is provided to find the optimal tuning parameters finally one practical example of the lorenz system is given to illustrate that much less conservativeness can be achieved compared with the earlier results by using the corresponding common lkf and the results can be further improved when adopting the fuzzy time dependent lkf within large upper bounds

an h ψ formulation for the three dimensional eddy current problem in laminated structures it is a very challenging problem for the direct simulation of the three dimensional eddy currents in grain oriented go silicon steel laminations since the coating fllm is only several microns thick over each lamination and the magnetic permeability is nonlinear and anisotropic in addition the system of go silicon steel laminations has multiple scales and the ratio of the largest scale to the smallest scale can be up to 10 6 in this paper we study an h formulation for the nonlinear eddy current problem in laminated conductors by omitting the insulating fllms between neighboring laminations we propose an approximate but efiective h formulation for the nonlinear eddy current problem which reduces the scale ratio by 2 3 orders of magnitude the well posedness of the original problem and the approximate problem are established by examining their weak formulations the convergence is proved for the solution of the approximate problem to the solution of the original problem as the thickness of coating fllms approaches zero

brief paper robust sampled data h control with stochastic sampling in this paper the problem of robust h control is investigated for sampled data systems with probabilistic sampling the parameter uncertainties are time varying norm bounded and appear in both the state and input matrices for the simplicity of technical development only two different sampling periods are considered whose occurrence probabilities are given constants and satisfy bernoulli distribution which can be further extended to the case with multiple stochastic sampling periods by applying an input delay approach the probabilistic sampling system is transformed into a continuous time delay system with stochastic parameters in the system matrices by linear matrix inequality lmi approach sufficient conditions are obtained which guarantee the robust mean square exponential stability of the system with an h performance moreover an h controller design procedure is then proposed an illustrative example is included to demonstrate the effectiveness of the proposed techniques

spatially piecewise fuzzy control design for sampled data exponential stabilization of semilinear parabolic pde systems this paper employs a takagi sugeno t s fuzzy partial differential equation pde model to solve the problem of sampled data exponential stabilization in the sense of spatial l infty norm vert cdot vert infty for a class of nonlinear parabolic distributed parameter systems dpss where only a few actuators and sensors are discretely distributed in space initially a t s fuzzy pde model is assumed to be derived by the sector nonlinearity method to accurately describe complex spatiotemporal dynamics of the nonlinear dpss subsequently a static sampled data fuzzy local state feedback controller is constructed based on the t s fuzzy pde model by constructing an appropriate lyapunov krasovskii functional candidate and employing vector valued wirtinger s inequalities a variation of vector valued poincare wirtinger inequality in one dimensional spatial domain as well as a vector valued agmon s inequality it is shown that the suggested sampled data fuzzy controller exponentially stabilizes the nonlinear dpss in the sense of vert cdot vert infty if sufficient conditions presented in term of standard linear matrix inequalities lmis are fulfilled moreover an lmi relaxation technique is utilized to enhance exponential stabilization ability of the suggested sampled data fuzzy controller finally the satisfactory and better performance of the suggested sampled data fuzzy controller are demonstrated by numerical simulation results of two examples

dissipativity based sampled data control for fuzzy switched markovian jump systems in this paper the dissipativity based sampled data control problem is studied for fuzzy switched markovian jump systems fsmjs by considering the problem of the asynchronous switching caused by the subsystems switching occurred during the sampling intervals constructing novel time dependent lyapunov functional and using average dwell time adt approach sufficient mean square exponential stability criteria and strictly mathcal q s r gamma dissipativity criteria are proposed under the constraints on that the maximum sampling period is no larger than the dwell time between the transition rates switching instants meanwhile a relationship between adt and sampling period is revealed for fsmjs based on the strictly mathcal q s r gamma dissipativity criteria the sampled data controller is designed for fsmjss a circuit simulation example is provided to illustrate the effectiveness of the proposed method

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

sampled data fuzzy control of chaotic systems based on a t s fuzzy model in this paper a sampled data fuzzy controller is designed to stabilize a class of chaotic systems a takagi sugeno t s fuzzy model is employed to represent the chaotic systems based on this general model the exponential stability issue of the closed loop systems with an input constraint is first investigated by a novel time dependent lyapunov functional which is positive definite at sampling times but not necessary between the sampling times then two sufficient conditions are developed for sampled data fuzzy controller synthesis of the underlying t s fuzzy model with or without input constraint all the proposed results in this paper depend on both the upper and lower bounds on a sampling interval and the available information about the actual sampling pattern is fully utilized the proposed sampled data fuzzy control scheme is successfully applied to the chaotic lorenz system which is shown to be effective and less conservative compared with existing results

sampled data fuzzy control with space varying gains for nonlinear time delay parabolic pde systems abstract this paper introduces a sampled data fuzzy control sdfc with space varying gains for nonlinear time delay parabolic partial differential equation pde systems first a takagi sugeno t s fuzzy time delay parabolic pde model is employed to represent the nonlinear time delay pde system second with the aid of the t s fuzzy time delay pde model a sdfc design with space varying gains is developed in the formulation of space dependent linear matrix inequalities lmis by constructing an appropriate lyapunov functional which can stabilize exponentially the time delay pde system these stabilization conditions can be applied to either slowing varying time delay or fast varying one finally simulation results on two examples are presented to demonstrate the effectiveness of the proposed method

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 approach to stability and stabilization of fuzzy sampled data markovian chaotic systems abstract this paper studies the problems of stability and stabilization of a class of markovian chaotic systems via fuzzy sampled data control different from some existing results the markovian jumping with general uncertain transition rates is considered for such systems a novel approach i e input delay dependent vector approach is proposed for the first time based on this approach an input delay dependent lyapunov krasovskii functional lkf with cubic function of input delay is successfully constructed and the convex combination technique is used with ease to derive stability and stabilization criteria compared with some existing stability and stabilization criteria for chaotic systems our results are not only less conservative but also with reduced calculation complexity finally the superiorities of proposed results are illustrated by two numerical examples

a new fuzzy lyapunov function approach for a takagi sugeno fuzzy control system design in this paper a new fuzzy lyapunov function approach is presented for a class of continuous time takagi sugeno fuzzy control system the proposed fuzzy lyapunov function is formulated as a line integral of a fuzzy vector which is a function of the state and it can be regarded as the work done from the origin to the current state in the fuzzy vector field unlike the approaches using a fuzzy blending of multiple quadratic lyapunov functions the time derivatives of membership functions do not appear in the proposed approach the effectiveness of the proposed approach is shown through numerical examples

homogenization of the problem of eddy currents in a transformer core an electric transformer core constituted of two thin sheets is considered one sheet is metallic and the other is insulator which is a dielectric these two sheets are rolled up in the form of a cylinder a periodic difference of potential is applied between the two ends of the metallic sheets the purpose of this paper is to use a homogenization technique to describe the eddy currents that are developing in the transformer core the limit problem corresponds to a modified ohm s law with an average conductivity and a zero radial component of the current

asymptotic theory for the correlated gamma frailty model the frailty model is a generalization of cox s proportional hazard model where a shared unobserved quantity in the intensity induces a positive correlation among the survival times murphy showed consistency and asymptotic normality of the nonparametric maximum likelihood estimator npmle for the shared gamma frailty model without covariates in this paper we extend this result to the correlated gamma frailty model and we allow for covariates we discuss the definition of the nonparametric likelihood function in terms of a classical proof of consistency for the maximum likelihood estimator which goes back to wald our proof of the consistency for the npmle is essentially the same as the classical proof for the maximum likelihood estimator a new central limit theorem for processes of bounded variation is given furthermore we prove that a consistent estimator for the asymptotic variance of the npmle is given by the inverse of a discrete observed information matrix

a multiple lyapunov function approach to stabilization of fuzzy control systems this paper addresses stability analysis and stabilization for takagi sugeno fuzzy systems via a so called fuzzy lyapunov function which is a multiple lyapunov function the fuzzy lyapunov function is defined by fuzzily blending quadratic lyapunov functions based on the fuzzy lyapunov function approach we give stability conditions for open loop fuzzy systems and stabilization conditions for closed loop fuzzy systems to take full advantage of a fuzzy lyapunov function we propose a new parallel distributed compensation pdc scheme that feedbacks the time derivatives of premise membership functions the new pdc contains the ordinary pdc as a special case a design example illustrates the utility of the fuzzy lyapunov function approach and the new pdc stabilization method

stability and stabilization of takagi sugeno fuzzy systems via sampled data and state quantized controller in this paper we investigate the problem of stability and stabilization for sampled data fuzzy systems with state quantization by using an input delay approach the sampled data fuzzy systems with state quantization are transformed into a continuous time system with a delay in the state the transformed system contains nondifferentiable time varying state delay based on some integral techniques some new stability and stabilization criteria are first proposed by a modified lyapunov functional furthermore in the case of no quantization some new stability and stabilization criteria are also obtained it is shown that the new stability and stabilization criteria can provide a larger upper bound of the sampling interval than some existing ones in the literature two simulation examples are given to show the effectiveness of the proposed design method

an improved result on exponential stabilization of sampled data fuzzy systems this brief paper studies the exponential stabilization problem for the takagi sugeno fuzzy systems with a variable sampling different from previous results the gains of fuzzy state feedback controller adopted in this paper are time varying during two consecutive sampling instants which can contribute to the enlargement of the allowable sampling interval by choosing a suitable design parameter to reduce the design conservativeness a novel fuzzy time dependent lyapunov functional ftdlf is put forward to fully exploit the accessible information about the sampling pattern and the fuzzy basis functions moreover a more relaxed constraint condition is presented to ensure the positive definiteness of the ftdlf on sampling intervals by resorting to the novel ftdlf and the relaxed constraint condition new exponential stabilization criteria dependent on and independent of upper bounds on time derivatives of fuzzy basis functions are established by which a larger sampling interval can be achieved one example is offered to demonstrate the validity and superiorities of the obtained new results

robust variance estimation for the case cohort design large cohort studies of rare outcomes require extensive data collection often for many relatively uninformative subjects sampling schemes have been proposed that oversample certain groups for example the case cohort design of prentice 1986 biometrika 73 1 11 provides an efficient method of analysis of failure time data however the variance estimate must explicitly correct for correlated score contributions a simple robust variance estimator is proposed that allows for more complicated sampling mechanisms the variance estimate uses a jackknife estimate of the variance of the individual influence function and is shown to be equivalent to a robust variance estimator proposed by lin and wei 1989 journal of the american statistical association 84 1074 1078 for the standard cox model simulation results indicate excellent agreement with corrected asymptotic estimates and appropriate test size the technique is illustrated with data evaluating the efficacy of mammography screening in reducing breast cancer mortality

case cohort and case control analysis with cox s model prentice 1986 proposed the case cohort design and studied a pseudolikelihood estimator of regression parameters in cox s model we derive a class of estimating equations for case cohort sampling each depending on a different estimator of the population distribution which lead naturally to simple estimators that improve on prentice s pseudolikelihood estimator we also discuss an equivalence between case control and case cohort sampling in terms of the estimation of regression parameters in cox s model

weighted semiparametric likelihood method for fitting a proportional odds regression model to data from the case cohort design the problem of fitting a proportional odds regression model to data from the case cohort design proposed by prentice is considered a weighted semiparametric likelihood method is proposed under the proportional odds model the maximum weighted semiparametric likelihood estimators of both the regression parameter and the transformation function are shown to be consistent and normally distributed the applicability of the weighted semiparametric likelihood method to the semiparametric transformation regression models is also discussed in particular when the proportional hazards regression model is fitted estimators proposed by chen and lo can be generated by the weighted semiparametric likelihood method under different weighting schemes a simulation study suggests that the case cohort design is also useful under the proportional odds regression model and the proposed method performs well with practical finite sample sizes

general impulsive control of chaotic systems based on a ts fuzzy model in this paper a time delayed impulsive controller is constructed and the uniformly asymptotic stability of chaotic systems based on a takagi sugeno ts fuzzy model is investigated and some new and useful criteria are derived in addition the uniformly asymptotic synchronization of chaotic systems based on the ts fuzzy model is discussed using similar techniques finally some numerical simulations are presented to illustrate the effectiveness and feasibility of the derived results

dissipativity analysis and synthesis for discrete time t s fuzzy stochastic systemswith time varying delay this paper is concerned with the problems of dissipativity analysis and synthesis for discrete time takagi sugeno fuzzy systems with stochastic perturbation and time varying delay first a novel model transformation method is introduced to pull the time varying delay uncertainty out of the original system consequently the transformed model is composed of a linear time invariant system and a norm bounded uncertain subsystem by using this model transformation method combined with the lyapunov krasovskii technique sufficient conditions of the dissipativity are established then a fuzzy controller is designed to guarantee the dissipative performance of the closed loop system finally three examples are presented one shows the effectiveness of model transformation method the second performs the comparison with alternative approaches and the third illustrates the applicability of the proposed dissipative control methods

estimation of regression coefficients when some regressors are not always observed abstract in applied problems it is common to specify a model for the conditional mean of a response given a set of regressors a subset of the regressors may be missing for some study subjects either by design or happenstance in this article we propose a new class of semiparametric estimators based on inverse probability weighted estimating equations that are consistent for parameter vector 0 of the conditional mean model when the data are missing at random in the sense of rubin and the missingness probabilities are either known or can be parametrically modeled we show that the asymptotic variance of the optimal estimator in our class attains the semiparametric variance bound for the model by first showing that our estimation problem is a special case of the general problem of parameter estimation in an arbitrary semiparametric model in which the data are missing at random and the probability of observing complete data is bounded away from 0 and then deriving a representation for the efficient score

fitting semiparametric transformation regression models to data from a modified case cohort design summary we consider the problem of fitting semiparametric transformation regression models to data from a modified case cohort study in which the censoring times of all the censored subjects in the cohort are observed we propose to maximise a conditional profile likelihood to obtain the estimator of the regression parameter under the assumption that the censoring is independent of the covariates the estimator is shown to be consistent and asymptotically normally distributed numerical studies suggest that the relative efficiency of the estimator is very high and that the estimator is often less biased than the estimator from the complete case analysis and more accurate than the pseudolikelihood estimator

mixed h infinity and passive filtering for discrete fuzzy neural networks with stochastic jumps and time delays in this brief the problems of the mixed h infinity and passivity performance analysis and design are investigated for discrete time delay neural networks with markovian jump parameters represented by takagi sugeno fuzzy model the main purpose of this brief is to design a filter to guarantee that the augmented markovian jump fuzzy neural networks are stable in mean square sense and satisfy a prescribed passivity performance index by employing the lyapunov method and the stochastic analysis technique applying the matrix decomposition techniques sufficient conditions are provided for the solvability of the problems which can be formulated in terms of linear matrix inequalities a numerical example is also presented to illustrate the effectiveness of the proposed techniques

cox regression with incomplete covariate measurements abstract this article provides a general solution to the problem of missing covariate data under the cox regression model the estimating function for the vector of regression parameters is an approximation to the partial likelihood score function with full covariate measurements and reduces to the pseudolikelihood score function of self and prentice in the special setting of case cohort designs the resulting parameter estimator is consistent and asymptotically normal with a covariance matrix for which a simple and consistent estimator is provided extensive simulation studies show that the large sample approximations are adequate for practical use the proposed approach tends to be more efficient than the complete case analysis especially for large cohorts with infrequent failures for case cohort designs the new methodology offers a variance covariance estimator that is much easier to calculate than the existing ones and allows multiple subcohort augmentations to improve efficiency real data taken f

a mean score method for missing and auxiliary covariate data in regression models summary we consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and in addition for a subset called the validation sample true covariate data of interest have been ascertained the term auxiliary data refers to data not in the regression model but thought to be informative about the true missing covariate data of interest we discuss a method which is nonparametric with respect to the association between available and missing data allows missingness to depend on available response and covariate values and is applicable to both cohort and case control study designs the method previously proposed by flanders greenland 1991 and by zhao lipsitz 1992 is generalised and asymptotic theory is derived our expression for the asymptotic variance of the estimator provides intuition regarding performance of the method optimal sampling strategies for the validation set are also suggested by the asymptotic results