piecewise h_ infty controller design of uncertain discrete time fuzzy systems with time delays this paper considers the robust h infin control of uncertain discrete time fuzzy systems with time delays based on piecewise lyapunov krasovskii functionals it is shown that the stability with h infin disturbance attenuation performance can be established for the closed loop fuzzy control systems if there exists a piecewise lyapunov krasovskii functional and moreover the functional and the corresponding controller can be obtained by solving a set of linear matrix inequalities that are numerically feasible a numerical example is given to demonstrate the efficiency and the advantage of the proposed method

propagation of probabilities means and variances in mixed graphical association models abstract a scheme is presented for modeling and local computation of exact probabilities means and variances for mixed qualitative and quantitative variables the models assume that the conditional distribution of the quantitative variables given the qualitative is multivariate gaussian the computational architecture is set up by forming a tree of belief universes and the calculations are then performed by local message passing between universes the asymmetry between the quantitative and qualitative variables sets some additional limitations for the specification and propagation structure approximate methods when these are not appropriately fulfilled are sketched it has earlier been shown how to exploit the local structure in the specification of a discrete probability model for fast and efficient computation thereby paving the way for exploiting probability based models as parts of realistic systems for planning and decision support the purpose of this article is to extend this computational s

delay dependent decentralized h_ infty filtering for discrete time nonlinear interconnected systems with time varying delay based on the t s fuzzy model this paper is concerned with the problem of delay dependent h filter design for a class of discrete time nonlinear interconnected system with time varying delays via the takagi sugeno t s fuzzy model the t s fuzzy model consists of n time delay t s fuzzy subsystems and a decentralized h filter is designed for each subsystem based on the delay dependent piecewise lyapunov krasovskii functional ddplkf and with an improved free weighting matrix technique the delay dependent stability and a prescribed h performance index are guaranteed for the overall filtering error system a sufficient condition for the existence of such a filter is established by using linear matrix inequalities lmis that are numerically feasible two numerical examples are given to demonstrate the effectiveness and advantage of the proposed approach

the tm algorithm for maximising a conditional likelihood function summary this paper describes an algorithm for maximising a conditional likelihood function when the corresponding unconditional likelihood function is more easily maximised the algorithm is similar to the em algorithm but different as the parameters rather than the data are augmented and the conditional rather than the marginal likelihood function is maximised in exponential families the algorithm takes a particular simple form which is computationally very close to the em algorithm the algorithm alternates between a t step which calculates a tilted version of the unconditional likelihood function and an m step which maximises it the algorithm applies to mixed graphical chain models lauritzen wermuth 1989 and their generalisations edwards 1990 and was developed with these in mind but it may have applications beyond these the algorithm has been implemented in the most recent version of the mim software edwards 2000 where it was named the me algorithm the name has been changed to avoid confusion with the algorithm described by marschner 2001

h control for descriptor systems a matrix inequalities approach abstract this paper considers the h control problem for descriptor systems that possibly have impulsive modes and or j axis zeros first we propose matrix inequalities that give a generalized stability condition and an h norm condition for descriptor systems using these matrix inequalities we show that the solvability of a set of matrix inequalities is necessary and sufficient to the existence of a proper controller that satisfies a prescribed h norm condition as well as stabilizing the closed loop system and eliminating all impulsive modes these inequalities are equivalent to certain linear matrix inequalities to which we can get solutions whenever they exist using efficient polynomial time algorithms

problems with em algorithms for ml factor analysis rubin and thayer recently presented equations to implement maximum likelihood ml estimation in factor analysis via the em algorithm they present an example to demonstrate the efficacy of the algorithm and propose that their recovery of multiple local maxima of the ml function certainly should cast doubt on the general utility of second derivatives of the log likelihood as measures of precision of estimation it is shown here in contrast that these second derivatives verify that rubin and thayer did not find multiple local maxima as claimed the only known maximum remains the one found by joreskog over a decade earlier the standard errors obtained from the second derivatives and the fisher information matrix thus remain appropriate where ml assumptions are met the advantages of the em algorithm over other algorithms for ml factor analysis remain to be demonstrated

stability and stabilization of a class of fuzzy time delay descriptor systems this paper studies a class of fuzzy time delay descriptor systems in the extended takagi sugeno t s fuzzy model sufficient conditions are derived for the stability and stabilization in terms of linear matrix inequalities lmis illustrative examples are given to show the effectiveness and the advantages of the present results

normalization and stabilization for rectangular singular fractional order t s fuzzy systems abstract this paper considers the normalization and stabilization of rectangular singular fractional order takagi sugeno t s fuzzy systems with order 0 1 by using the proportional and derivative type dynamic compensator the rectangular singular fractional order t s fuzzy systems are transformed into an augmented square singular fractional order t s fuzzy systems and the admissibility of the augmented square singular fractional order t s fuzzy systems can guarantee the normalization and stabilization of the rectangular singular fractional order t s fuzzy systems two sufficient conditions are expressed in terms of a set of bilinear matrix inequalities which can be efficiently solved by an iterative lmi algorithm a numerical example is given to verify the effectiveness of the results proposed

decomposability and selection of graphical models for multivariate time series summary we derive conditions for decomposition and collapsibility of graphical interaction models for multivariate time series these properties enable us to perform stepwise model selection under certain restrictions for illustration we apply the results to a multivariate time series describing the haemodynamic system as monitored in intensive care

remarks on the thickness and outerthickness of a graph abstract the thickness of a graph is the minimum number of planar subgraphs into which the graph can be decomposed the thickness of complete bipartite graphs km n is known for almost all values of m and n in this paper we solve the thickness of complete bipartite graphs for unknown cases m the outerthickness of a graph is the minimum number of outerplanar subgraphs into which the graph can be decomposed we give lower and upper bounds for outerthickness in the terms of the minimum and maximum degree of a graph let be the minimum degree and the maximum degree of a graph g we show that the following bounds hold for outerthickness 4 o g 2 we also discuss the possibility of determining the upper bound for outerthickness using the number of edges of a given graph

multiple imputation of missing categorical and continuous values via bayesian mixture models with local dependence abstractwe present a nonparametric bayesian joint model for multivariate continuous and categorical variables with the intention of developing a flexible engine for multiple imputation of missing values the model fuses dirichlet process mixtures of multinomial distributions for categorical variables with dirichlet process mixtures of multivariate normal distributions for continuous variables we incorporate dependence between the continuous and categorical variables by 1 modeling the means of the normal distributions as component specific functions of the categorical variables and 2 forming distinct mixture components for the categorical and continuous data with probabilities that are linked via a hierarchical model this structure allows the model to capture complex dependencies between the categorical and continuous data with minimal tuning by the analyst we apply the model to impute missing values due to item nonresponse in an evaluation of the redesign of the survey of income and program partic

robust h sub spl infin disturbance attenuation for a class of uncertain discrete time fuzzy systems the paper is concerned with robust stabilization and h sub spl infin control of a class of uncertain discrete time fuzzy systems the class of uncertain systems is described by state space takagi sugeno ts fuzzy models with linear nominal parts and norm bounded parameter uncertainties in the state and output equations first a sufficient condition on robust stability of the fuzzy models is proposed then h sub spl infin disturbance attenuation performance of the fuzzy models is analyzed some sufficient conditions are derived on robust h sub spl infin disturbance attenuation in which both robust stability and a prescribed h sub spl infin performance are required to be achieved irrespective of the uncertainties a numerical example shows the use of the results on the stabilization and h sub spl infin disturbance attenuation of a class of discrete time nonlinear systems via fuzzy switch

robust stability of uncertain discrete time singular fuzzy systems this paper considers the problem of robust stability analysis of uncertain discrete time singular fuzzy systems described by a class of extended takagi sugeno fuzzy dynamic models with time varying norm bounded parameter uncertainties a sufficient condition ensuring a discrete singular fuzzy system to be regular causal and stable is proposed in terms of a set of linear matrix inequalities based on this a robust stability condition is obtained both of these conditions can be checked by using recently developed algorithms in solving lmis examples are provided to demonstrate the reduced conservatism and effectiveness of the proposed conditions

likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates summary we consider longitudinal studies in which the outcome observed over time is binary and the covariates of interest are categorical with no missing responses or covariates one specifies a multinomial model for the responses given the covariates and uses maximum likelihood to estimate the parameters unfortunately incomplete data in the responses and covariates are a common occurrence in longitudinal studies here we assume the missing data are missing at random rubin 1976 biometrika63 581 592 since all of the missing data responses and covariates are categorical a useful technique for obtaining maximum likelihood parameter estimates is the em algorithm by the method of weights proposed in ibrahim 1990 journal of the american statistical association85 765 769 in using the em algorithm with missing responses and covariates one specifies the joint distribution of the responses and covariates here we consider the parameters of the covariate distribution as a nuisance in data sets where the percentage of missing data is high the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest we propose a conditional model for the covariate distribution that has several modeling advantages for the em algorithm and provides a reduction in the number of nuisance parameters thus providing more stable estimates in finite samples

logistic regression with incompletely observed categorical covariates a comparison of three approaches summary a logistic regression analysis based on the complete cases neglects the information due to subjects with missing values in at least one but not in all covariates three different approaches allowing the use of this information are compared maximum likelihood estimation pseudo maximum likelihood estimation and probability imputation the first two yield consistent estimates of the regression coefficients and asymptotic normality allows the construction of asymptotically valid confidence intervals the only necessary assumption is that the probability for the occurrence of missing values does not depend on the true value of the hidden covariates whereas there may be a dependence on completely observed covariates and on the outcome variable their relative efficiency and the gain relative to a complete case analysis is investigated comparison with imputation methods shows that imputation of conditional probabilities can be regarded as a close approximation to the pseudo maximum likelihood estimation whereas imputation of unconditional probabilities is associated with serious bias

maximum likelihood estimation in graphical models with missing values in this paper we discuss maximum likelihood estimation when some observations are missing in mixed graphical interaction models assuming a conditional gaussian distribution as introduced by lauritzen wermuth 1989 for the saturated case ml estimation with missing values via the em algorithm has been proposed by little schluchter 1985 we expand their results to the special restrictions in graphical models and indicate a more efficient way to compute the e step the main purpose of the paper is to show that for certain missing patterns the computational effort can considerably be reduced

remarks on the thickness of a graph abstract the thickness of a graph is the minimum number of planar subgraphs into which the graph can be decomposed this note discusses some recent attempts to determine upper bounds for the thickness of a graph as a function of the number of edges or as a function of its maximum degree

output feedback robust h sup spl infin control of uncertain fuzzy dynamic systems with time varying delay this paper presents an output feedback robust h sup spl infin control problem for a class of uncertain fuzzy dynamic systems with time varying delayed state the takagi sugeno fuzzy model is employed to represent an uncertain nonlinear systems with time varying delayed state using a single quadratic lyapunov function the globally exponential stability and disturbance attenuation of the closed loop fuzzy control system are discussed sufficient conditions for the existence of robust h sup spl infin controllers are given in terms of matrix inequalities constructive algorithm for design of robust h sup spl infin controller is also developed the resulting controller is nonlinear and automatically tuned based on fuzzy operation

a note on halton s conjecture the thickness of a graph g is the minimum number of planar graphs whose union is g halton conjectured that any graph of maximum degree d has thickness at most d 2 4 we disprove the conjecture by showing graphs of thickness d 2 for any d 5

stability analysis and design for a class of continuous time fuzzy control systems this paper presents a design method for a class of fuzzy control systems the class of fuzzy systems considered can be represented by the takagi sugeno fuzzy model which is a type of dynamic fuzzy model a constructive algorithm is developed to obtain the stabilizing feedback control law for the system the main contribution of this paper is the development of an equivalent principle that is the design of a fuzzy control system is equivalent to the design of a set of linear time invariant extreme systems thus any design method in linear control system theory can be used to design a fuzzy control system an example is given to illustrate the application of the method

an o m log n time algorithm for the maximal planar subgraph problem based on a new version of the hopcroft and tarjan planarity testing algorithm this paper develops an o m log n time algorithm to find a maximal planar subgraph

on heuristics for determining the thickness of a graph we present an empirical analysis of some heuristics for the graph thickness problem i e decomposing a graph into the minimum number of planar subgraphs the problem has applications in database systems e g the layout of e r diagrams the heuristics are based on some algorithms for finding a maximal planar subgraph of a nonplanar graph empirical results show that a randomized heuristic is a practical tool for solving this problem we also improve upon a previously derived upper bound for the thickness of a graph with respect to its size

nonparametric and semiparametric models for missing covariates in parametric regression robustness of covariate modeling for the missing covariate problem in parametric regression is studied under the missing at random assumption for a simple missing covariate pattern nonparametric covariate model is proposed and is shown to yield a consistent and semiparametrically efficient estimator for the regression parameter total robustness is achieved in this situation for more general missingcovariate patterns a novel semiparametric modeling approach is proposed for the covariates in this approach the covariate distribution is first decomposed into the product of a series of conditional distributions according to the overall missing data patterns and the conditional distributions are then represented in the general odds ratio form the general odds ratios are modeled parametrically and the other components of the covariate distribution are modeled nonparametrically maximum semiparametric likelihood is used to find the parameter estimates the proposed method yields a consistent estimator f

controller synthesis of fuzzy dynamic systems based on piecewise lyapunov functions this paper presents a kind of controller synthesis method for fuzzy dynamic systems based on a piecewise smooth lyapunov function the basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a piecewise continuous lyapunov function can be used to establish the global stability with h sub spl infin performance of the resulting closed loop fuzzy control systems it is shown that the control laws can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software an example is given to illustrate the application of the proposed methods

missing covariates in generalized linear models when the missing data mechanism is non ignorable we propose a method for estimating parameters in generalized linear models with missing covariates and a non ignorable missing data mechanism we use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one dimensional conditional distributions with each one dimensional conditional distribution consisting of a logistic regression we allow the covariates to be either categorical or continuous the joint covariate distribution is also modelled via a sequence of one dimensional conditional distributions and the response variable is assumed to be completely observed we derive the e and m steps of the em algorithm with non ignorable missing covariate data for categorical covariates we derive a closed form expression for the e and m steps of the em algorithm for obtaining the maximum likelihood estimates mles for continuous covariates we use a monte carlo version of the em algorithm to obtain the mles via the gibbs sampler computational techniques for gibbs sampling are proposed and implemented the parametric form of the assumed missing data mechanism itself is not testable from the data and thus the non ignorable modelling considered here can be viewed as a sensitivity analysis concerning a more complicated model therefore although a model may have passed the tests for a certain missing data mechanism this does not mean that we have captured even approximately the correct missing data mechanism hence model checking for the missing data mechanism and sensitivity analyses play an important role in this problem and are discussed in detail several simulations are given to demonstrate the methodology in addition a real data set from a melanoma cancer clinical trial is presented to illustrate the methods proposed

association models for clustered data with binary and continuous responses we consider analysis of clustered data with mixed bivariate responses i e where each member of the cluster has a binary and a continuous outcome we propose a new bivariate random effects model that induces associations among the binary outcomes within a cluster among the continuous outcomes within a cluster between a binary outcome and a continuous outcome from different subjects within a cluster as well as the direct association between the binary and continuous outcomes within the same subject for the ease of interpretations of the regression effects the marginal model of the binary response probability integrated over the random effects preserves the logistic form and the marginal expectation of the continuous response preserves the linear form we implement maximum likelihood estimation of our model parameters using standard software such as proc nlmixed of sas our simulation study demonstrates the robustness of our method with respect to the misspecification of the regression model as well as the random effects model we illustrate our methodology by analyzing a developmental toxicity study of ethylene glycol in mice

mixtures of continuous and categorical variables in discriminant analysis a hypothesis testing approach summary in the classical case of discrimination between two multivariate normal populations an allocation rule based on a hypothesis testing approach john s z statistic has long been in competition with the rule based on simple parameter estimation and substitution in the bayes procedure anderson s w statistic recent evidence suggests that the z statistic may be preferable in certain circumstances the purpose of this paper is to extend this approach to mixed discrete and continuous variables accordingly an allocation rule based on the location model is derived by use of a hypothesis testing argument a method for the estimation of error rates with this rule is outlined some examples are given and the new rule is compared with its direct competitors some of its possible merits are discussed briefly let v be a p component random vector which has probability densities f1 v and t2 v in two distinct populations 7r1 and 7r2 respectively and suppose that it is required to use v in discriminating between the two populations more particularly suppose that it is necessary to decide from which of the two populations an observed value v of v has come this is the classical problem of discriminant analysis which has been described by many authors a standard treatment is given by anderson 1958 chapter 6 anderson shows that if the probability densities f1 v and t2 v are completely specified the optimal decision rule is given by the bayes procedure which is to allocate v to w1 or 7r2 according to fl v lf2 v s k ae cut off point k is determined by the prior probabilities of an observation coming from each population and by the costs incurred when an individual is misclassified from each population in practice the densities t1 v and f2 v are rarely known and the only information about 7r1 and 7r2 which is usually available comes from two training sets v ll v21 v l known to come from 7r1 and v l2 v22 v m2 known to come from 7r2 in such circumstances estimation is necessary various methods have been proposed each involving a different level of assumptions at one extreme no assumptions at all are made about the functional form of the densities ti v i 1 2 and these densities are estimated from the training sets by some nonparametric method a comprehensive bibliography of such methods is provided by wertz and schneider 1979 at the other extreme exact functional forms which include unknown parameters are assumed for the densities a classical approach to the problem involves estimation of the unknown parameters from the training sets replacing the unknown parameters by these estimates in fl v lt2 v leads

robust stabilization via state feedback for descriptor systems with uncertainties in the derivative matrix this paper considers the robust stabilization problem for linear continuous time descriptor systems with norm bounded perturbations in matrix e we show that if the problem has a solution then the perturbations must not increase the rank of e under the assumption that the rank of e with uncertainties is always equal to the rank of e without uncertainties the problem under consideration falls into two cases for the first case a complete solution in terms of a necessary and sufficient condition is given for quadratic stabilization for the other case a necessary condition and a sufficient condition are presented

estimating and using propensity scores with partially missing data abstract investigators in observational studies have no control over treatment assignment as a result large differences can exist between the treatment and control groups on observed covariates which can lead to badly biased estimates of treatment effects propensity score methods are an increasingly popular method for balancing the distribution of the covariates in the two groups to reduce this bias for example using matching or subclassification sometimes in combination with model based adjustment to estimate propensity scores which are the conditional probabilities of being treated given a vector of observed covariates we must model the distribution of the treatment indicator given these observed covariates much work has been done in the case where covariates are fully observed we address the problem of calculating propensity scores when covariates can have missing values in such cases which commonly arise in practice the pattern of missing covariates can be prognostically important and

nonfragile control with guaranteed cost of t s fuzzy singular systems based on parallel distributed compensation this paper investigates the nonfragile guaranteed cost control problem for a class of uncertain nonlinear singular state delayed systems that is described by takagi sugeno models the upper bound of state delay is assumed to be available a linear quadratic cost function is specified as the performance index of the closed loop system attention is focused on the fuzzy state feedback controller design via parallel distributed compensation scheme which not only guarantees the regular impulse free and asymptotically stable of the closed loop fuzzy singular delay system but also provides an optimized upper bound of the guaranteed cost function for the possibility of feedback gain variation and all admissible uncertainties all derived sufficient conditions are given in terms of linear matrix inequalities some numerical examples are provided to illustrate the effectiveness of the proposed design scheme

robust stability and stabilization for singular systems with state delay and parameter uncertainty considers the problems of robust stability and stabilization for uncertain continuous singular systems with state delay the parametric uncertainty is assumed to be norm bounded the purpose of the robust stability problem is to give conditions such that the uncertain singular system is regular impulse free and stable for all admissible uncertainties while the purpose of the robust stabilization is to design a state feedback control law such that the resulting closed loop system is robustly stable these problems are solved via the notions of generalized quadratic stability and generalized quadratic stabilization respectively necessary and sufficient conditions for generalized quadratic stability and generalized quadratic stabilization are derived a strict linear matrix inequality lmi design approach is developed an explicit expression for the desired robust state feedback control law is also given finally a numerical example is provided to demonstrate the application of the proposed method

a genetic algorithm for determining the thickness of a graph abstract the thickness of a graph is the minimum number of planar subgraphs into which the graph can be decomposed determining the thickness of a given graph is known to be an np complete problem this paper discusses the possibility of determining the thickness of a graph using a genetic algorithm our tests show that the genetic approach outperforms the earlier heuristic algorithms reported in the literature

comments on robust stabilization of a class of uncertain nonlinear systems via fuzzy control quadratic stabilizability h sup spl infin control theory and linear matrix inequalities this paper presents stability analysis for a class of uncertain nonlinear systems and a method for designing robust fuzzy controllers to stabilize the uncertain nonlinear systems first a stability condition for takagi and sugeno s fuzzy model is given in terms of lyapunov stability theory next new stability conditions for a generalized class of uncertain systems are derived from robust control techniques such as quadratic stabilization h sup spl infin control theory and linear matrix inequalities the derived stability conditions are used to analyze the stability of takagi and sugeno s fuzzy control systems with uncertainty which can be regarded as a generalized class of uncertain nonlinear systems the design method employs the so called parallel distributed compensation important issues for the stability analysis and design are remarked finally three design examples of fuzzy controllers for stabilizing nonlinear systems and uncertain nonlinear systems are presented

regression models for a bivariate discrete and continuous outcome with clustering abstract developmental toxicity studies of laboratory animals play a crucial role in the testing and regulation of chemicals and pharmaceutical compounds exposure to developmental toxicants typically causes a variety of adverse effects such as fetal malformations and reduced fetal weight at term in this article we discuss regression methods for jointly analyzing bivariate discrete and continuous outcomes that are motivated by the statistical problems that arise in analyzing data from developmental toxicity studies we focus on marginal regression models that is models in which the marginal expectation of the bivariate response vector is related to a set of covariates by some known link functions in these models the regression parameters for the marginal expectation are of primary scientific interest whereas the association between the binary and continuous response is considered to be a nuisance characteristic of the data we describe a likelihood based approach based on the general location mode

on the curve complexity of 3 colored point set embeddings abstract we establish new results on the curve complexity of k colored point set embeddings when k 3 we show that there exist 3 colored caterpillars with only three independent edges whose 3 colored point set embeddings may require n 1 3 bends on n 2 3 edges this settles an open problem by badent et al 5 about the curve complexity of point set embeddings of k colored trees and it extends a lower bound by pach and wenger 35 to the case that the graph only has o 1 independent edges concerning upper bounds we prove that any 3 colored path admits a 3 colored point set embedding with curve complexity at most 4 in addition we introduce a variant of the k colored simultaneous embeddability problem and study its relationship with the k colored point set embeddability problem

new approaches to relaxed quadratic stability condition of fuzzy control systems this paper deals with the quadratic stability conditions of fuzzy control systems that relax the existing conditions reported in the previous literatures two new conditions are proposed and shown to be useful in analyzing and designing fuzzy control systems the first one employs the s procedure to utilize information regarding the premise parts of the fuzzy systems the next one enlarges the class of fuzzy control systems whose stability is ensured by representing the interactions among the fuzzy subsystems in a single matrix and solving it by linear matrix inequality the relationships between the suggested stability conditions and the conventional well known stability conditions reported in the previous literatures are also discussed and it is shown in a rigorous manner that the second condition of this paper includes the conventional conditions finally some examples and simulation results are presented to illustrate the effectiveness of the stability conditions

feedback and optimal sensitivity model reference transformations multiplicative seminorms and approximate inverses in this paper the problem of sensitivity reduction by feedback is formulated as an optimization problem and separated from the problem of stabilization stable feedback schemes obtainable from a given plant are parameterized salient properties of sensitivity reducing schemes are derived and it is shown that plant uncertainty reduces the ability of feedback to reduce sensitivity the theory is developed for input output systems in a general setting of banach algebras and then specialized to a class of multivariable time invariant systems characterized by n times n matrices of h infty frequency response functions either with or without zeros in the right half plane the approach is based on the use of a weighted seminorm on the algebra of operators to measure sensitivity and on the concept of an approximate inverse approximate invertibility of the plant is shown to be a necessary and sufficient condition for sensitivity reduction an indicator of approximate invertibility called a measure of singularity is introduced the measure of singularity of a linear time invariant plant is shown to be determined by the location of its right half plane zeros in the absence of plant uncertainty the sensitivity to output disturbances can be reduced to an optimal value approaching the singularity measure in particular if there are no right half plane zeros sensitivity can be made arbitrarily small the feedback schemes used in the optimization of sensitivity resemble the lead lag networks of classical control design some of their properties and methods of constructing them in special cases are presented

decomposition of maximum likelihood in mixed graphical interaction models summary we show how decompositions of marked graphs induce corresponding decompositions of maximum likelihood estimates in mixed graphical interaction models we use this result to obtain explicit formulae for the estimates in decomposable models and to obtain factorizations of likelihood ratio statistics

drawing colored graphs on colored points let g be a planar graph with n vertices and with a partition of the vertex set into subsets v 0 v k 1 for some positive integer email protected email protected n let s be a set of n distinct points in the plane with a partition into subsets s 0 s k 1 with v i s i email protected email protected k 1 this paper studies the problem of computing a planar polyline drawing of g such that each vertex of v i is mapped to a distinct point of s i lower and upper bounds on the number of bends per edge are proved for any email protected email protected n in the special case k n we improve the upper and lower bounds presented in a paper by pach and wenger j pach r wenger embedding planar graphs at fixed vertex locations graphs and combinatorics 17 2001 717 728 the upper bound is based on an algorithm for computing a topological book embedding of a planar graph such that the vertices follow a given left to right order and the number of crossings between every edge and the spine is asymptotically optimal which can be regarded as a result of independent interest

delay dependent robust stabilization of uncertain state delayed systems this paper concerns a problem of robust stabilization of uncertain state delayed systems a new delay dependent stabilization condition using a memoryless controller is formulated in terms of matrix inequalities an algorithm involving convex optimization is proposed to design a controller guaranteeing a suboptimal maximal delay such that the system can be stabilized for all admissible uncertainties

on the thickness of graphs of given degree abstract the results presented here refer to the determination of the thickness of a graph that is the minimum number of planar subgraphs into which the graph can be decomposed a useful general preliminary result obtained here is theorem 8 a planar graph has a planar representation for any arbitrary placement of its nodes in the plane it is then proved that if we have positive integers d and t such that any graph of degree at most d has thickness at most t the following hold theorem 9 any graph of degree d has thickness at most t roof d 1 d theorem 10 any graph of degree d can always be embedded in a regular graph g of any degree d corollary 5 any graph of degree d has thickness at most roof d 2 theorem 12 with d and t defined as above we have d 4t 2 corollary 6 if t 2 then d 6 we further conjecture that indeed the thickness of any graph of degree not exceeding 6 is never more than 2 and that more generally t roof d 2 4 since the design and fabrication of vlsi computer chips is essentially a concrete representation of the planar decomposition of a graph all these results are of direct practical interest

admissibility analysis and control synthesis for t s fuzzy descriptor systems the problem of admissibility analysis and control synthesis for takagi sugeno fuzzy descriptor systems is investigated first based on nonquadratic fuzzy lyapunov function and fully using the information of fuzzy membership functions a new relaxed sufficient condition ensuring a fuzzy descriptor system to be admissible regular impulse free and stable is proposed in which it is not necessary to require every fuzzy subsystem to be stable second the other sufficient condition for the admissibility is obtained without the information of time derivatives of fuzzy membership functions following the analysis both parallel and nonparallel distributed compensation controllers are designed linear matrix inequalities conditions are given to construct the controllers finally some examples are provided to illustrate the main results in this paper less conservative than some earlier related results

estimation and hypothesis testing in finite mixture models summary finite mixture models are a useful class of models for application to data when sample sizes are not large and the number of underlying densities is in question likelihood ratio tests based on joint maximum likelihood estimation of the mixing parameter x and the parameter of the underlying densities 0 are problematical our approach places a prior distribution on x and estimates 0 by maximizing the likelihood of the data given 0 with x integrated out advantages of this approach computational issues using the em algorithm and directions for further work are discussed the technique is applied to two examples

maximum likelihood estimation and model selection in contingency tables with missing data abstract in many studies the values of one or more variables are missing for subsets of the original sample this article focuses on the problem of obtaining maximum likelihood estimates mle for the parameters of log linear models under this type of incomplete data the appropriate systems of equations are presented and the expectation maximization em algorithm dempster laird and rubin 1977 is suggested as one of the possible methods for solving them the algorithm has certain advantages but other alternatives may be computationally more effective tests of fit for log linear models in the presence of incomplete data are considered the data from the protective services project for older persons blenkner bloom and nielsen 1971 blenkner bloom and weber 1974 are used to illustrate the procedures discussed in the article

robust h control for uncertain discrete singular systems with pole placement in a disk abstract this paper addresses the design of robust h controllers for uncertain discrete singular systems with time invariant uncertainty in both the state and measurement matrices the singular system to be controlled is not assumed to be regular a regular dynamic output feedback controller is designed such that a prescribed h performance condition is satisfied and the closed loop poles are placed in a specified disk while the regularity causality and stability of the closed loop system can be guaranteed for all admissible uncertainties the desired controller can be obtained by solving a set of matrix inequalities a numerical example is given to demonstrate the application of the proposed method

maximum likelihood estimation for mixed continuous and categorical data with missing values summary maximum likelihood procedures for analysing mixed continuous and categorical data with missing values are presented the general location model of olkin tate 1961 and extensions introduced by krzanowski 1980 1982 form the basis for our methods maximum likelihood estimation with incomplete data is achieved by an application of the em algorithm dempster laird rubin 1977 special cases of the algorithm include orchard woodbury s 1972 algorithm for incomplete normal samples fuchs s 1982 algorithms for log linear modelling of partially classified contingency tables and day s 1969 algorithm for multivariate normal mixtures applications include a imputation of missing values b logistic regression and discriminant analysis with missing predictors and unclassified observations c linear regression with missing continuous and categorical predictors and d parametric cluster analysis with incomplete data methods are illustrated using data from the st louis risk research project some key word8 cluster analysis discriminant analysis em algorithm incomplete data linear regression logistic regression log linear model mixture model

passivity analysis and passive control of fuzzy systems with time varying delays this paper is concerned with the passive controller design problem for a class of continuous time takagi sugeno t s fuzzy systems with both state and input delays the delays are assumed to be time varying and differentiable a notion of very strict passivity is adopted the purpose is to design a state feedback fuzzy controller such that the resulting closed loop system is very strictly passive vsp delay dependent conditions for the solvability of the addressed problem are obtained by applying recently developed techniques for time delay systems and fuzzy systems these conditions are expressed by means of strict linear matrix inequalities lmis that can be easily solved a numerical example and simulation results are provided to demonstrate the effectiveness of the proposed method

delay dependent robust h sub spl infin control for t s fuzzy systems with time delay this paper focuses on the problem of delay dependent robust fuzzy control for a class of nonlinear delay systems via state feedback the takagi sugeno t s fuzzy model is adopted for representing a nonlinear system with time delayed state a delay dependent stabilization criterion is first presented then the methods of robust stabilization and robust h sub spl infin control are developed which are dependent on the size of the delay and are based on the solutions of linear matrix inequalities lmis finally a design example of robust h sub spl infin controller for uncertain nonlinear systems is given to illustrate the effectiveness of the approaches proposed in this paper

h fuzzy control for nonlinear time delay singular markovian jump systems with partly unknown transition rates abstract this paper investigates the problem of h fuzzy control for a class of nonlinear time delay singular markovian jump systems with partly unknown transition rates this class of systems under consideration is described by takagi sugeno t s fuzzy model the goal of the paper is to design fuzzy state feedback controllers such that systems with partly unknown transition rates are not only regular impulse free and stochastically stable but also satisfy a prescribed h performance for all delays no larger than a given upper bound in terms of linear matrix inequalities and systems with completely known transition rates can be viewed as a special case of the one we consider here finally numerical examples are given to illustrate the merit and usability of the approach proposed in this paper

analysis on robust stability for interval descriptor systems this paper considers the robust stability problem for linear continuous time descriptor systems with interval system matrices we 4rst derive some necessary conditions for the considered interval descriptor system to be regular impulse free and stable then under some constraints a necessary and su5cient condition is given by using the kronecker product and analysis c 2001 elsevier science b v all rights reserved

fault detection in finite frequency domain for t s fuzzy systems with partly unmeasurable premise variables abstract this paper is concerned with the problem of finite frequency fault detection fd for a class of t s fuzzy systems with partly unmeasurable premise variables to fully use the available information of the considered t s fuzzy system an fd observer whose premise variables consist of the measurable premise variables and the estimations of unmeasurable ones of the fuzzy model is designed however the membership functions of the fuzzy model and the observer to be designed are unsynchronized such that the existing fd methods based on parallel distribute compensation pdc strategy may be infeasible to overcome this difficulty a novel non pdc h h unknown input observer is designed to achieve the purpose of fault detection and provide less conservative results finally two simulation examples are given to illustrate the validity and merits of the presented fd scheme

biplanar graphs a survey abstract a graph is called biplanar if it is the union of two planar graphs in this survey we present a variety of results on biplanar graphs some special families of such graphs and some generalizations

multiple imputation for incomplete data with semicontinuous variables we consider the application of multiple imputation to data containing not only partially missing categorical and continuous variables but also partially missing semicontinuous variables variables that take on a single discrete value with positive probability but are otherwise continuously distributed as an imputation model for data sets of this type we introduce an extension of the standard general location model proposed by olkin and tate our extension the blocked general location model provides a robust and general strategy for handling partially observed semicontinuous variables in particular we incorporate a two level model for the semicontinuous variables into the general location model the first level models the probability that the semicontinuous variable takes on its point mass value and the second level models the distribution of the variable given that it is not at its point mass in addition we introduce em and data augmentation algorithms for the blocked general location model w

delay dependent stability and h control for a class of fuzzy descriptor systems with time delay this paper addresses issues of stability analysis and h control for a class of fuzzy time delay descriptor systems described by an extended takagi sugeno t s fuzzy model a delay dependent stability criterion is first presented and a h control synthesis approach is then developed both the stability criterion and the h controller synthesis are based on the solutions of linear matrix inequalities lmis finally two examples are given to illustrate the effectiveness of the approaches proposed in this paper

dob fuzzy controller design for non gaussian stochastic distribution systems using two step fuzzy identification this paper presents a novel non gaussian stochastic control framework for the problem of disturbance estimation and rejection by combining fuzzy identification technology with disturbance observer design first fuzzy logic models are used to approximate the output probability density functions pdfs of non gaussian processes such that the task of pdf shape control can be reduced to a fuzzy weight dynamics modeling and control problem next takagi sugeno fuzzy models with multiple disturbances are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input in which a novel disturbance observer based pi type fuzzy feedback controller is designed to ensure the system stability and convergence of the tracking error to zero meanwhile the disturbance estimation and attenuation performance as well as the state constrained requirement can also be guaranteed moreover the novel composite observer is constructed by augmenting the disturbance estimation into the full state estimation the satisfactory tracking performance and full state observation effect can be achieved by the designed optimization algorithm finally simulations for paper making process are given to show the efficiency of the proposed approach

stability analysis of discrete singular fuzzy systems abstract this work mainly develops a discrete singular fuzzy model and discusses its stability the standard discrete t s model is generalized into the so called discrete singular takagi sugeno dst s model which can represent a larger class of nonlinear systems and the stability criteria of this system are then investigated these criteria are described by non strict linear matrix inequalities and we thus involve the projection method for ensuring stability based on some derived projection operators a projection algorithm is proposed for solving a feasible solution an illustrating example demonstrates the validity and effectiveness of the proposed method

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

a simulated annealing algorithm for determining the thickness of a graph the thickness of a graph is the minimum number of planar subgraphs into which the graph can be decomposed determining the thickness of a given graph is known to be an np complete problem in this paper we introduce a new heuristic algorithm for determining the thickness of a graph our algorithm is based on the simulated annealing optimization scheme we compare the quality of the solutions and running times of our algorithm against previously tested heuristic algorithms we show that the simulated annealing is a fast and efficient method to obtain good approximations for the thickness of a graph we also give a new upper bound for the thickness of complete tripartite graphs whose vertex sets are of equal size

fuzzy descriptor systems and nonlinear model following control this paper defines a fuzzy descriptor system by extending the ordinary takagi sugeno t s fuzzy model several kinds of stability conditions for the fuzzy descriptor system are derived and represented in terms of linear matrix inequalities lmis we illustrate an example of defining the fuzzy descriptor system instead of the ordinary t s fuzzy model an lmi design approach is employed to find stable feedback gains and a common lyapunov function in addition this paper presents a nonlinear model following control for the fuzzy descriptor system a new parallel distributed compensation the so called twin parallel distributed compensations tpdc to realize the nonlinear model following control is proposed we present a design technique based on the tpdc the proposed method is a unified approach to nonlinear model following control it contains the regulation and servo control problems as special cases

marginalization and collapsibility in graphical interaction models the behaviour of a graphical interaction model under marginalization is discussed a graphical interaction model is called collapsible onto a set of variables if the class of marginal distributions is the same as that implied by the related subgraph the necessary and sufficient condition for collapsibility is found and it is shown that collapsibility is equivalent to a range of other important statistical properties of the model

biometrika centenary theory and general methodology note reprinted in biometrika one hundred years edited by d m titterington and d r cox oxford university press 11 50 reference stat article 2001 002doi 10 1093 biomet 88 1 13view record in web of science record created on 2006 04 21 modified on 2017 05 12

analysis of censored survival data with intermittently observed time dependent binary covariates abstract in survival analyses that include a time dependent covariate the standard partial likelihood approach is often complicated by the fact that the necessary observations of the time dependent covariate are not always available for the subjects in each risk set in this article we consider the situation of a binary time dependent covariate we specify a longitudinal model for the binary covariate with missing observations and a proportional hazards model for survival time conditional on fixed covariates and the completed time dependent covariate history the joint posterior distribution of the missing covariates and the parameters in the covariate and hazard models is simulated using gibbs sampling the joint modeling approach allows the missing covariates to be generated using all of the observed covariates as well as the disease outcome and failure time information simulation studies show that this approach often leads to lower bias of relative risk estimates associated with the time dependent c