robust model based and model assisted predictors of the finite population total the prediction approach to finite population inference has received considerable attention in recent years under this approach the finite population is assumed to be a realization from a superpopulation described by a known probability model usually a linear model the prediction approach is often criticized for its lack of robustness against model misspecification in this article we revisit this important issue and introduce a new robust prediction approach in which the superpopulation model is chosen adaptively from the well known box cox class of probability distributions the richness of the box cox class ensures robustness in our model based prediction approach we explain how our robust model based predictor can be adjusted to handle zero observations for the study variable and to achieve the design unbiasedness and benchmarking properties we demonstrate the robustness of our proposed predictors using a monte carlo simulation study and a real life example

the existence and global exponential stability of periodic solution for a neutral coupled system on networks with delays in this paper we establish sufficient conditions for the existence and global exponential stability of periodic solution to a type of neutral coupled system on networks with delays the key to prove the existence of periodic solutions is using the combined method of graph theory coincidence degree theory and lyapunov functional method and the sufficient conditions are easy to be checked finally a numerical simulation is carried out to show the correctness of our main results

generalized synchronization via the differential primitive element abstract generalized synchronization gs in nonlinear systems appears when the states of one system through a functional mapping are equal to states of another this mapping can be obtained if there exists a differential primitive element which generates a differential transcendence basis we introduce a new definition of gs in nonlinear systems using the concept of differential primitive element in this contribution we investigate the gs problem when we have strictly different nonlinear systems and we consider that for both the slave and master systems only some states are available from measurements the first component of the mapping is called differential primitive element and in general is defined by means of a linear combination of the known states and the inputs of the system furthermore we design a new dynamical feedback controller able to achieve complete synchronization in the coordinate transformation systems and gs in the original coordinates these particular forms of gs are illustrated with numerical results of well known chaotic benchmark systems

a proof of marton s coding theorem for the discrete memoryless broadcast channel corresp a simple proof using random partitions and typicality is given for marton s coding theorem for broadcast channels

the estimation of the mean squared error of small area estimators abstract small area estimation has received considerable attention in recent years because of a growing demand for reliable small area statistics the direct survey estimators based only on the data from a given small area or small domain are likely to yield unacceptably large standard errors because of small sample size in the domain therefore alternative estimators that borrow strength from other related small areas have been proposed in the literature to improve the efficiency these estimators use models either implicitly or explicitly that connect the small areas through supplementary e g census and administrative data for example simple synthetic estimators are based on implicit modeling in this article three small area models of battese harter and fuller 1988 dempster rubin and tsutakawa 1981 and fay and herriot 1979 are investigated these models are all special cases of a general mixed linear model involving fixed and random effects and a small area mean can be expr

estimation of finite population domain means a model assisted empirical best prediction approach in this article we introduce a general methodology for producing a model assisted empirical best predictor ebp of a finite population domain mean using data from a complex survey our method improves on the commonly used design consistent survey estimator by using a suitable mixed model such a model combines information from related sources such as census and administrative data unlike a purely model based ebp the proposed model assisted ebp converges in probability to the customary design consistent estimator as the domain and sample sizes increase the convergence in probability is shown to hold with respect to the sampling design irrespective of the assumed mixed model a property commonly known as design consistency this property ensures robustness of the proposed predictor against possible model failures in addition the convergence in probability is shown to be valid with respect to the assumed mixed model model consistency a new mean squared prediction error mspe estimator is proposed

stability analysis for coupled systems with time delay on networks abstract in this paper the stability problem for some coupled systems with time delay on networks csdns is investigated we provide a systematic method for constructing a global lyapunov functional for csdns by using graph theory the stability uniform stability and global uniform stability of the systems are investigated and by using the lyapunov functional constructed some sufficient conditions of stability are obtained

adaptive pinning control for the projective synchronization of drive response dynamical networks abstract this paper studies the projective synchronization of a drive response dynamical network of partially linear systems the coupling configuration matrix of the network is not necessarily symmetric two adaptive pinning control strategies are derived based on the barbalat s lemma in the first control strategy the pinning control gain is adaptively updated to realize the projective synchronization in the second control strategy both the pinning control gain and the coupling strength between nodes are adaptively updated numerical simulation results show that the coupling strength needed in the second control strategy is smaller than that in the first control strategy to achieve the projective synchronization of the network

sharp bounds for mutual covering a fundamental tool in network information theory is the covering lemma which lower bounds the probability that there exists a pair of random variables among a given number of independently generated candidates falling within a given set we use a weighted sum trick and talagrand s concentration inequality to prove new mutual covering bounds we identify two interesting applications 1 when the probability of the set under the given joint distribution is bounded away from 0 and 1 the covering probability converges to 1 doubly exponentially fast in the blocklength which implies that the covering lemma does not induce penalties on the error exponents in the applications to coding theorems and 2 using hall s marriage lemma we show that the maximum difference between the probability of the set under the joint distribution and the covering probability equals half the minimum total variation distance between the joint distribution and any distribution that can be simulated by selecting a pair from the candidates thus we use the mutual covering bound to derive the exact error exponent in the joint distribution simulation problem in both applications the determination of the exact exponential or double exponential behavior relies crucially on the sharp concentration inequality used in the proof of the mutual covering lemma

nested linear lattice codes for structured multiterminal binning network information theory promises high gains over simple point to point communication techniques at the cost of higher complexity however lack of structured coding schemes limited the practical application of these concepts so far one of the basic elements of a network code is the binning scheme wyner 1974 1978 and other researchers proposed various forms of coset codes for efficient binning yet these schemes were applicable only for lossless source or noiseless channel network coding to extend the algebraic binning approach to lossy source or noisy channel network coding previous work proposed the idea of nested codes or more specifically nested parity check codes for the binary case and nested lattices in the continuous case these ideas connect network information theory with the rich areas of linear codes and lattice codes and have strong potential for practical applications we review these developments and explore their tight relation to concepts such as combined shaping and precoding coding for memories with defects and digital watermarking we also propose a few novel applications adhering to a unified approach

from scale free to erdos renyi networks we analyze a model that interpolates between scale free and erdos r enyi networks the model introduced generates a one parameter family of networks and allows one to analyze the role of structural heterogeneity analytical calculations are compared with extensive numerical simulations in order to describe the transition between these two important classes of networks finally an application of the proposed model to the study of the percolation transition is presented

multi receiver wiretap channel with public and confidential messages we study the multi receiver wiretap channel mr wc with public and confidential messages in this channel there is a transmitter that wishes to communicate with two legitimate users in the presence of an external eavesdropper the transmitter sends a pair of public and confidential messages to each legitimate user while there are no secrecy constraints on the public messages confidential messages need to be transmitted in perfect secrecy we study the discrete memoryless mr wc as well as its gaussian multi input multi output mimo counterpart first we propose an inner bound for the general not necessarily degraded discrete memoryless mr wc by using marton s inner bound and rate splitting in conjunction with superposition coding and binning second we specialize this inner bound for the degraded discrete memoryless case this specialized form of the inner bound can be obtained by using superposition coding and binning only next we obtain an outer bound for the capacity region of the degraded channel which matches the inner bound for some special cases third we consider the degraded gaussian mimo channel and show that to evaluate both the inner and outer bounds considering only jointly gaussian auxiliary random variables and channel input is sufficient similar to the discrete memoryless case for the gaussian mimo case as well these bounds match for some special cases

generalized synchronization in complex dynamical networks via adaptive couplings abstract this paper investigates generalized synchronization of three typical classes of complex dynamical networks scale free networks small world networks and interpolating networks the proposed synchronization strategy is to adjust adaptively a node s coupling strength based on the node s local generalized synchronization information by taking the auxiliary system approach and using the lyapunov function method we prove that for any given initial coupling strengths the generalized synchronization can take place in complex networks consisting of nonidentical dynamical systems it is demonstrated that the coupling strengths are affected by topologies of the networks furthermore it is found that there are hierarchical features in the processes of generalized synchronization in scale free networks because of their highly heterogeneous distributions of connection degree finally we discuss in detail how a network s degree of heterogeneity affects its generalization synchronization behavior

empirical bayes estimation of finite population means from complex surveys abstract estimation of finite population means is considered when samples are collected using a stratified sampling design finite populations for different strata are assumed to be realizations from different superpopulations the true means of the observations lie on a regression surface with random intercepts for different strata the true sampling variances are also different and random for different strata the strata are connected through two common prior distributions one for the intercepts and another for the sampling variances for all the strata the model is appropriate in two important survey situations first it can be applied to repeated surveys where the physical characteristics of the sampling units change slowly over time second the model is appropriate in small area estimation problems where a very few samples are available for any particular area empirical bayes estimators of the finite population means are shown to be asymptotically optimal in the sense of robbins the proposed emp

robust empirical bayes estimation of means from stratified samples abstract this article considers simultaneous estimation of means from several strata where each stratum contains a finite number of elements for example quite often estimates of annual incomes unemployment rates and so forth must be made simultaneously for many areas our results find application in small area estimation where very few samples are available from an individual area and an estimate of a certain area mean or simultaneous estimates of several area means can be improved by incorporating information from similar neighboring areas ghosh and meeden 1986 considered empirical bayes estimation of the finite population mean assuming a normal superpopulation model their estimators generalize automatically when one estimates a vector of stratum means this article relaxes the normality assumption and assumes instead that the posterior expectation of any stratum mean is a linear function of the sample observations this is the so called posterior linearity property as described in ericson 1

constrained bayes estimation with applications abstract bayesian techniques are widely used in these days for simultaneous estimation of several parameters in compound decision problems often however the main objective is to produce an ensemble of parameter estimates whose histogram is in some sense close to the histogram of population parameters this is for example the situation in subgroup analysis where the problem is not only to estimate the different components of a parameter vector but also to identify the parameters that are above and the others that are below a certain specified cutoff point we have proposed in this paper bayes estimates in a very general context that meet this need these estimates are obtained by matching the first two moments of the histogram of the estimates and the posterior expectations of the first two moments of the histogram of the parameters and minimizing subject to these conditions the posterior expectation of the euclidean distance between the estimates and the parameters several applications of the m

on applicability of auxiliary system approach to detect generalized synchronization in complex network generalized synchronization is ubiquitous in nature it is well known that the auxiliary system approach has been widely used to verify the presence of generalized synchronization this approach was firstly proposed in a drive response system then extended to the bidirectionally coupled systems and complex networks however the well known generalized auxiliary system method lacks a rigorous theoretical basis for its various applications two recent counterexamples indicate us the inapplicability of this method inspired by the counterexamples we find that it is interesting to ask the following two fundamental questions i why is the generalized auxiliary system approach unworkable in the networks with bidirectional couplings ii are there any essential conditions for the applications of this approach this technical note aims at establishing a rigorous theoretical basis for the applicability of auxiliary system approach that is the generalized auxiliary system approach is effective only if there does not exist any path from nodes to their driving neighbors who drive these nodes in a network several representative examples are also given to validate our theoretical results

function projective synchronization in complex networks with switching topology and stochastic effects although function projective synchronization in complex dynamical networks has come into the limelight in recent years litter research has been published on the problem in the dynamical networks with switching topology and stochastic effects this study aims to fill the gap in this paper the problem of function projective synchronization is investigated for complex networks with switching topology and stochastic effects a hybrid feedback control method is designed to achieve function projective synchronization for the complex network using the property of martingale and gronwally inequality we obtain some conditions to guarantee that the complex network can realize mean square synchronization and mean square exponential synchronization respectively furthermore we also present a probability approach to the method of lyapunov functionals to analyze function projective synchronization in the dynamical network under a particular assumption our approaches not only can replace the lasalle type theorem but also allow improvements of existing results in the literature in particular the study also presents an equivalent way of regarding ito integral which may be a useful tool to deal with the problem of synchronization in variety of complex dynamical networks with stochastic effects finally some numerical examples are provided to demonstrate the effectiveness of the proposed approach

generalized projective synchronization of fractional order chaotic systems abstract in this paper based on the idea of a nonlinear observer a new method is proposed and applied to generalized projective synchronization for a class of fractional order chaotic systems via a transmitted signal this synchronization approach is theoretically and numerically studied by using the stability theory of linear fractional order systems suitable conditions for achieving synchronization are given numerical simulations coincide with the theoretical analysis

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

on multiple descriptions and team guessing witsenhausen s hyperbola bound for the multiple description problem without excess rate in case of a binary source is not tight for exact joint reproductions however this bound is tight for almost exact joint reproductions theorem 1 conjectured by witsenhausen the proof is based on an em approximative form of the team guessing lemma for em sequences of random variables this result may be of interest also for team guessing the hyperbola bound is also tight for exact joint reproductions and arbitrarily small but positive excess rate theorem 2 the proof of this result uses our covering lemma

synchronizability determined by coupling strengths and topology on complex networks we investigate in depth the synchronization of coupled oscillators on top of complex networks with different degrees of heterogeneity within the context of the kuramoto model in a previous paper phys rev lett 98 034101 2007 we unveiled how for fixed coupling strengths local patterns of synchronization emerge differently in homogeneous and heterogeneous complex networks here we provide more evidence on this phenomenon extending the previous work to networks that interpolate between homogeneous and heterogeneous topologies we also introduce details of the path towards synchronization for the evolution of clustering in the synchronized patterns finally we investigate the synchronization of networks with modular structure and conclude that in these cases local synchronization is first attained at the most internal level of organization of modules progressively evolving to the outer levels as the coupling constant is increased the present work introduces parameters that are proved to be useful for the characterization of synchronization phenomena in complex networks

parametric empirical bayes inference theory and applications abstract this article reviews the state of multiparameter shrinkage estimators with emphasis on the empirical bayes viewpoint particularly in the case of parametric prior distributions some successful applications of major importance are considered recent results concerning estimates of error and confidence intervals are described and illustrated with data

achievable rate region for three user discrete broadcast channel based on coset codes we consider the problem of developing coding techniques and deriving achievable rate regions for discrete memoryless broadcast channels with three receivers 3 dbc we begin by identifying a novel vector additive 3 dbc for which we characterize an upper bound on the the largest achievable rate region based on unstructured codes henceforth referred to as pmb mathscr u mathcal m region we propose a coding technique based on coset codes that yield an achievable rate triple not contained within pmb mathscr u mathcal m region we generalize the proposed coding technique using a new ensemble of codes partitioned coset codes pcc containing both empirical and algebraic properties and evaluate its performance to derive an achievable rate region for the general 3 dbc the new elements in this derivation are binning and joint typicality encoding and decoding of statistically correlated pccs we validate the utility of this technique by identifying non additive instances of 3 dbc for which the proposed coding techniques based on pcc yield strictly larger rates

empirical bayes estimation in finite population sampling abstract empirical bayes methods are becoming increasingly popular in statistics robbins 1955 introduced the method in the context of nonparametric estimation of a completely unspecified prior distribution subsequently the method has been explored very successfully in a series of articles by efron and morris 1973 1975 1977 in a parametric framework in the efron morris setup a family of parametric distributions is used as possible priors but only when one or more of the parameters of the family of prior distributions is estimated from the data morris 1983 listed a number of areas where empirical bayes methods are used one of the main features of empirical bayes analysis is to borrow strength from the ensemble that is use information from similar sources in constructing estimators and predictors in addition to the most directly available source of information there are some situations in finite population sampling where such methods might be suitable for instance in many repetitive surve

capacity results for the discrete memoryless network a discrete memoryless network dmn is a memoryless multiterminal channel with discrete inputs and outputs a sequence of inner bounds to the dmn capacity region is derived by using code trees capacity expressions are given for three classes of dmns 1 a single letter expression for a class with a common output 2 a two letter expression for a binary symmetric broadcast channel bc with partial feedback and 3 a finite letter expression for push to talk dmns the first result is a consequence of a new capacity outer bound for common output dmns the third result demonstrates that the common practice of using a time sharing random variable does not include all time sharing possibilities namely time sharing of channels several techniques for improving the bounds are developed 1 causally conditioned entropy and directed information simplify the inner bounds 2 code trellises serve as simple code trees 3 superposition coding and binning with code trees improves rates numerical computations show that the last technique enlarges the best known rate regions for a multiple access channel mac and a bc both with feedback in addition to the rate bounds a sequence of inner bounds to the dmn reliability function is derived a numerical example for a two way channel illustrates the behavior of the error exponents

on the design consistency property of hierarchical bayes estimators in finite population sampling we obtain a limit of a hierarchical bayes estimator of a finite population mean when the sample size is large the limit is in the sense of ordinary calculus where the sample observations are treated as fixed quantities our result suggests a simple way to correct the hierarchical bayes estimator to achieve design consistency a well known property in the traditional randomization approach to finite population sampling we also suggest three different measures of uncertainty of our proposed estimator

generalized synchronization of chaos in directionally coupled chaotic systems synchronization of chaotic systems is frequently taken to mean actual equality of the variables of the coupled systems as they evolve in time we explore a generalization of this condition which equates dynamical variables from one subsystem with a function of the variables of another subsystem this means that synchronization implies a collapse of the overall evolution onto a subspace of the system attractor in full space we explore this idea in systems where a response system bold y ital t is driven with the output of a driving system bold x ital t but there is no feedback to the driver we lose generality but gain tractability with this restriction to investigate the existence of the synchronization condition bold y ital t phi bold x ital t we introduce the idea of mutual false nearest neighbors to determine when closeness in response space implies closeness in driving space the synchronization condition also implies that the response dynamics is determined by the drive alone and we provide tests for this as well examples are drawn from computer simulations on various known cases of synchronization and on data from nonlinear electrical circuits determining the presence of generalized synchronization will be quite important when one has only scalarmore observations from the drive and from the response systems since the use of time delay or other embedding methods will produce imperfect coordinates in which strict equality of the synchronized variables is unlikely to transpire less

multiple description quantization via gram schmidt orthogonalization the multiple description md problem has received considerable attention as a model of information transmission over unreliable channels a general framework for designing efficient md quantization schemes is proposed in this paper we provide a systematic treatment of the el gamal cover egc achievable md rate distortion region and show it can be decomposed into a simplified egc segc region and a superimposed refinement operation furthermore any point in the segc region can be achieved via a successive quantization scheme along with quantization splitting for the quadratic gaussian case the proposed scheme has an intrinsic connection with the gram schmidt orthogonalization which implies that the whole gaussian md rate distortion region is achievable with a sequential dithered lattice based quantization scheme as the dimension of the optimal lattice quantizers becomes large moreover this scheme is shown to be universal for all independent and identically distributed i i d smooth sources with performance no worse than that for an i i d gaussian source with the same variance and asymptotically optimal at high resolution a class of md scalar quantizers in the proposed general framework is also constructed and is illustrated geometrically the performance is analyzed in the high resolution regime which exhibits a noticeable improvement over the existing md scalar quantization schemes

achievable rates for multiple descriptions consider a sequence of independent identically distributed i i d random variables x l x 2 cdots x n and a distortion measure d x i x i on the estimates x i of x i two descriptions i x in 1 2 cdots 2 nr 1 and j x in 1 2 cdots 2 nr 2 are given of the sequence x x 1 x 2 cdots x n from these two descriptions three estimates i x x2 j x and hat x o i x j x are formed with resulting expected distortions e frac 1 n sum n k 1 d x k hat x mk d m m 0 1 2 we find that the distortion constraints d 0 d 1 d 2 are achievable if there exists a probability mass distribution p x p hat x 1 hat x 2 hat x 0 x with ed x hat x m leq d m such that r 1 i x hat x 1 r 2 i x hat x 2 where i cdot denotes shannon mutual information these rates are shown to be optimal for deterministic distortion measures

an achievable rate region based on coset codes for multiple access channel with states we prove that the ensemble of the nested coset codes built on finite fields achieves the capacity of arbitrary discrete memoryless point to point channels exploiting its algebraic structure we develop a coding technique for communication over general discrete multiple access channel with channel state information distributed noncausally at the transmitters we build an algebraic coding framework for this problem using the ensemble of abelian group codes and thereby derive a new achievable rate region we identify non additive and non symmetric examples for which the proposed achievable rate region is strictly larger than the one achievable using random unstructured codes

synchronization analysis of delayed complex networks with time varying couplings abstract in this paper a new method is presented to analyze the linear stability of the synchronized state in arbitrarily coupled complex dynamical systems with time delays the coupling configurations are not restricted to the symmetric and irreducible connections or the non negative off diagonal links the stability criteria are obtained by using lyapunov krasovskii functional method and subspace projection method these criteria reveal the relationship between coupling matrices and stability of the dynamical networks

chaos synchronization of general complex dynamical networks recently it has been demonstrated that many large scale complex dynamical networks display a collective synchronization motion here we introduce a time varying complex dynamical network model and further investigate its synchronization phenomenon based on this new complex network model two network chaos synchronization theorems are proved we show that the chaos synchronization of a time varying complex network is determined by means of the inner coupled link matrix the eigenvalues and the corresponding eigenvectors of the coupled configuration matrix rather than the conventional eigenvalues of the coupled configuration matrix for a uniform network especially we do not assume that the coupled configuration matrix is symmetric and its off diagonal elements are nonnegative which in a way generalizes the related results existing in the literature

modeling the evolution of weighted networks we present a general model for the growth of weighted networks in which the structural growth is coupled with the edges weight dynamical evolution the model is based on a simple weight driven dynamics and a weights reinforcement mechanism coupled to the local network growth that coupling can be generalized in order to include the effect of additional randomness and nonlinearities which can be present in real world networks the model generates weighted graphs exhibiting the statistical properties observed in several real world systems in particular the model yields a nontrivial time evolution of vertices properties and scale free behavior with exponents depending on the microscopic parameters characterizing the coupling rules very interestingly the generated graphs spontaneously achieve a complex hierarchical architecture characterized by clustering and connectivity correlations varying as a function of the vertices degree

a novel definition of generalized synchronization on networks and a numerical simulation example this paper develops a novel definition of generalized synchronization on complex networks consisting of systems evolving in a chaotic or regular fashion with two usual methods for detecting generalized synchronization two criteria for generalized synchronization on networks are advanced some complex dynamical behaviors are discussed briefly on the basis of numerical simulations of a real network example

sum capacity of gaussian vector broadcast channels this paper characterizes the sum capacity of a class of potentially nondegraded gaussian vector broadcast channels where a single transmitter with multiple transmit terminals sends independent information to multiple receivers coordination is allowed among the transmit terminals but not among the receive terminals the sum capacity is shown to be a saddle point of a gaussian mutual information game where a signal player chooses a transmit covariance matrix to maximize the mutual information and a fictitious noise player chooses a noise correlation to minimize the mutual information the sum capacity is achieved using a precoding strategy for gaussian channels with additive side information noncausally known at the transmitter the optimal precoding structure is shown to correspond to a decision feedback equalizer that decomposes the broadcast channel into a series of single user channels with interference pre subtracted at the transmitter

writing on dirty paper corresp a channel with output y x s z is examined the state s sim n 0 qi and the noise z sim n 0 ni are multivariate gaussian random variables i is the identity matrix the input x in r n satisfies the power constraint l n sum i 1 n x i 2 leq p if s is unknown to both transmitter and receiver then the capacity is frac 1 2 ln 1 p n q nats per channel use however if the state s is known to the encoder the capacity is shown to be c ast frac 1 2 ln 1 p n independent of q this is also the capacity of a standard gaussian channel with signal to noise power ratio p n therefore the state s does not affect the capacity of the channel even though s is unknown to the receiver it is shown that the optimal transmitter adapts its signal to the state s rather than attempting to cancel it

a time varying complex dynamical network model and its controlled synchronization criteria today complex networks have attracted increasing attention from various fields of science and engineering it has been demonstrated that many complex networks display various synchronization phenomena in this note we introduce a time varying complex dynamical network model we then further investigate its synchronization phenomenon and prove several network synchronization theorems especially we show that synchronization of such a time varying dynamical network is completely determined by the inner coupling matrix and by the eigenvalues and the corresponding eigenvectors of the coupling configuration matrix of the network

empirical bayes estimation for the finite population mean on the current occasion abstract many finite populations which are sampled repeatedly change slowly over time then estimation of finite population characteristics for the current occasion l may be improved by the use of data from previous surveys in this article we investigate the use of empirical bayes procedures based on two superpopulation models each model has the same first stage the values of the population units on the ith occasion are a random sample from the normal distribution with mean i and variance 2 i at the second stage we assume that either a 1 l are a random sample from the normal distribution with mean and variance 2 or b given 2 i and i has the normal distribution with mean and variance 2 i independently for each i whereas the 2 i are a random sample from the inverse gamma distribution with parameters 2 and 2 in a the 2 i and 2 are assumed to be unknown whereas in b and are unknown we develop empirical bayes point estimators and confide

paths to globally generalized synchronization in scale free networks we apply the auxiliary system approach to study paths to globally generalized synchronization in scale free networks of identical chaotic oscillators including h enon maps logistic maps and lorentz oscillators as the coupling strength ensuremath epsilon between nodes of the network is increased transitions from partially to globally generalized synchronization and intermittent behaviors near the synchronization thresholds are found the generalized synchronization starts from the hubs of the network and then spreads throughout the whole network with the increase of ensuremath epsilon our result is useful for understanding the synchronization process in complex networks

achieving 1 2 log 1 snr on the awgn channel with lattice encoding and decoding we address an open question regarding whether a lattice code with lattice decoding as opposed to maximum likelihood ml decoding can achieve the additive white gaussian noise awgn channel capacity we first demonstrate how minimum mean square error mmse scaling along with dithering lattice randomization techniques can transform the power constrained awgn channel into a modulo lattice additive noise channel whose effective noise is reduced by a factor of spl radic 1 snr snr for the resulting channel a uniform input maximizes mutual information which in the limit of large lattice dimension becomes 1 2 log 1 snr i e the full capacity of the original power constrained awgn channel we then show that capacity may also be achieved using nested lattice codes the coarse lattice serving for shaping via the modulo lattice transformation the fine lattice for channel coding we show that such pairs exist for any desired nesting ratio i e for any signal to noise ratio snr furthermore for the modulo lattice additive noise channel lattice decoding is optimal finally we show that the error exponent of the proposed scheme is lower bounded by the poltyrev exponent

some bayesian and non bayesian procedures for the analysis of comparative experiments and for small area estimation computational aspects frequentist properties and relationships abstract the estimation of a treatment contrast from experimental data and the estimation of a small area mean are special cases of the prediction of the realization of a linear combination of fixed and random effects in a possibly unbalanced two part mixed linear model in this article a bayesian approach to point and interval prediction is presented and its computational requirements are examined differences between the bayesian approach and the traditional classical approach are discussed in general terms and in addition in terms of two examples taken from the literature 1 the comparison of drug formulations in a bioavailability trial westlake and 2 the estimation of corn crop areas using satellite data battese harter and fuller some deficiencies in the classical approach are pointed out and the bayesian approach is considered from a frequentist perspective it is shown via a monte carlo study that for certain noninformative choices of the prior distribution the frequentist prop

using empirical bayes methods in biopharmaceutical research a compound sampling model where a unit specific parameter is sampled from a prior distribution and then observed are generated by a sampling distribution depending on the parameter underlies a wide variety of biopharmaceutical data for example in a multi centre clinical trial the true treatment effect varies from centre to centre observed treatment effects deviate from these true effects through sampling variation knowledge of the prior distribution allows use of bayesian analysis to compute the posterior distribution of clinic specific treatment effects frequently summarized by the posterior mean and variance more commonly with the prior not completely specified observed data can be used to estimate the prior and use it to produce the posterior distribution an empirical bayes or variance component analysis in the empirical bayes model the estimated prior mean gives the typical treatment effect and the estimated prior standard deviation indicates the heterogeneity of treatment effects in both the bayes and empirical bayes approaches estimated clinic effects are shrunken towards a common value from estimates based on single clinics this shrinkage produces more efficient estimates in addition the compound model helps structure approaches to ranking and selection provides adjustments for multiplicity allows estimation of the histogram of clinic specific effects and structures incorporation of external information this paper outlines the empirical bayes approach coverage will include development and comparison of approaches based on parametric priors for example a gaussian prior with unknown mean and variance and non parametric priors discussion of the importance of accounting for uncertainty in the estimated prior comparison of the output and interpretation of fixed and random effects approaches to estimating population values estimating histograms and identification of key considerations in the use and interpretation of empirical bayes methods

detecting generalized synchronization between chaotic signals a kernel based approach a unified framework for analysing generalized synchronization in coupled chaotic systems from data is proposed the key of the proposed approach is the use of the kernel methods recently developed in the field of machine learning several successful applications are presented which show the capability of the kernel based approach for detecting generalized synchronization and dynamical change of the coupling strength between two chaotic systems can be captured by the proposed approach it is also discussed how the kernel parameter is suitably chosen from data

quasi linear bayes estimation in stratified finite populations in this paper a bayesian least squares approximation is proposed for descriptive inference in a finite population when a categorical auxiliary variable is known a hierarchical model ii analysis of variance is assumed the solution consists of a projection on the vector of group totals and on the between and within sums of squares the approximation can be seen as a normal approximation of the joint distribution of the statistic and of the parameter of interest conditional on two discrete variables these denote attribution to one group and selection in the sample the main consequence of the conditioning is that conjectures about non normality can be taken into account the solution is compared with others that have been published

n channel symmetric multiple descriptions part i n k source channel erasure codes in this two part paper we present a new achievable rate region for the general n channel symmetric multiple descriptions problem in part i inspired by the concept of maximum distance separable mds erasure channel codes we consider a special case of this rate region where the source is encoded into n descriptions each with rate r these descriptions are transmitted over n bandwidth constrained and errorless channels during transmission a subset of these channels can break down thus erasing the corresponding descriptions the decoder is interested in recovering the source with the reception of at least k descriptions thus the encoder is allowed to sample only one realization of this breakdown process during the entire transmission for gaussian sources we have the following interesting result when any k descriptions arrive the achievable distortion exactly matches the optimal distortion rate performance corresponding to a source rate of kr bits with the reception of any m k descriptions the source reconstruction quality is strictly better the improvement being nearly linear in the number of descriptions received

generalized linear models for small area estimation abstract bayesian methods have been used quite extensively in recent years for solving small area estimation problems particularly effective in this regard has been the hierarchical or empirical bayes approach which is especially suitable for a systematic connection of local areas through models however the development to date has mainly concentrated on continuous valued variates often the survey data are discrete or categorical so that hierarchical or empirical bayes techniques designed for continuous variates are inappropriate this article considers hierarchical bayes generalized linear models for a unified analysis of both discrete and continuous data a general theorem is provided that ensures the propriety of posteriors under diffuse priors this result is then extended to the case of spatial generalized linear models the hierarchical bayes procedure is implemented via markov chain monte carlo integration techniques two examples one featuring spatial correlation structure are given to illu

impulsive practical tracking synchronization of networked uncertain lagrangian systems without and with time delays abstract this paper presents a procedure for studying tracking synchronization of networked lagrangian systems via impulsive control where each agent is allowed to be nonidentical even uncertain dynamics some algebraic criteria for tracking synchronization without and with time delays are established respectively it turns out that these criteria can provide a novel impulsive control strategy to synchronize globally networked lagrangian systems to a given time varying target trajectory with a desired tracking error bound called as practical tracking synchronization a distinctive feature of the developed control strategy is fully to take into account the effects of impulsive constraints and thereby to yield impulsive synchronization motion of networked lagrangian systems provided that each agent instantaneously interacts with its neighbors only at some discrete moments as a direct application of the theoretical results practical tracking synchronization of nonidentical 3 dof mobile robots without and with time delays is discussed in detail simulation results illustrate and visualize the effectiveness and feasibility of the proposed control technique

a unified jackknife theory for empirical best prediction with m estimation the paper presents a unified jackknife theory for a fairly general class of mixed models which includes some of the widely used mixed linear models and generalized linear mixed models as special cases the paper develops jackknife theory for the important but so far neglected prediction problem for the general mixed model for estimation of fixed parameters a jackknife method is considered for a general class of m estimators which includes the maximum likelihood residual maximum likelihood and anova estimators for mixed linear models and the recently developed method of simulated moments estimators for generalized linear mixed models for both the prediction and estimation problems a jackknife method is used to obtain estimators of the mean squared errors mse asymptotic unbiasedness of the mse estimators is shown to hold essentially under certain moment conditions simulation studies undertaken support our theoretical results 1 introduction due to the advent of high speed computers and powerful software computer oriented statistical methods including various resampling methods have received considerable attention in recent years as statisticians are constantly facing complex problems the jackknife method is one such simple resampling method which is very popular among survey samplers primarily due

adaptive pinning synchronization in fractional order complex dynamical networks abstract synchronization of general complex dynamical networks with fractional order dynamical nodes is addressed in this paper based on the stability theory of fractional order differential systems and adaptive pinning control some sufficient local asymptotical synchronization criteria and global asymptotical ones are derived respectively which succeed in solving the problem about how many nodes are need to be controlled and how much coupling strength should be applied to ensure the synchronization of the entire fractional order networks the obtained results are more general and effective than those reported moreover the coupling configuration matrices and the inner coupling matrices are not assumed to be symmetric and irreducible finally a numerical simulation is presented to demonstrate the validity and feasibility of the proposed synchronization criteria