blind channel equalization and spl epsiv approximation algorithms we show that a blind equalizer can be obtained without estimating any statistics based on the received data if the constellation alphabet is known the idea is to formulate the blind channel equalization problem into a quadratic optimization with binary constraints then efficient spl epsiv approximation algorithms are presented and applied to find the equalizer coefficients the proposed approach blindly equalizes the channel at symbol baud rates does not rely on estimating any statistics applies to nonminimum phase channels and converges to an spl epsiv neighborhood of a global optimum simulation results support the theoretical analysis

analysis of embryonic development with a model for under or overdispersion relative to binomial variation summary observations from a study of the development of ovulations into embryos for texel sheep are analysed with a model for count data that are under or overdispersed relative to binomial variation the analysis is based on maximum quasi likelihood mccullagh and nelder 1989 generalized linear models 2nd edition london chapman and hall following an approach suggested by williams 1982 applied statistics 31 144 148 the dispersion parameter is developed as a combination of a variance component representing shared maternal effects and a correlation typically negative between ovulations within ewes the number of ovulations the binomial denominator is included as an explanatory variable

an evaluation of some methods for fitting dose response models to quantal response developmental toxicology data the analysis of quantal response developmental toxicology data by dose response modeling is discussed with emphasis on methods that avoid exact distributional assumptions these methods quasi likelihood bootstrapping and jackknifing are contrasted with analyses based on the beta binomial distribution for the resampling procedures dose response models are fit under a binomial likelihood a justification for this choice of estimator in resampling plans is given based on an extension of the standard results for asymptotic normality and consistency of maximum likelihood estimators this justification depends only on the true distribution of the data having the usual binomial expectation a quasi likelihood approach is also considered in which simple assumptions about the intralitter correlation structure are made quasi likelihood methods are in theory asymptotically robust to misspecification of the intralitter correlation structure the practical implications of these asymptotic results are evaluated in a simulation study

monte carlo deconvolution of digital signals guided by the inverse filter digital deconvolution concerns the restoration of an underlying discrete signal from a blurred noisy observation sequence the problem can be formulated in a bayesian framework as is usual in the bayesian context the computation of relevant posterior quantities is the major challenge previous work made substantial progress toward making this computation feasible most notably through the gibbs sampling approach of chen and li and the sequential importance sampling approach of liu and chen yet there is room for improvement because both global monte carlo strategies can be very slow to reach the target distribution we propose two new sampling strategies for efficient restoration of digital signals the key idea is to use inverse filtering to transform the posterior integration into a problem that is substantially more local than the direct formulation leading to stable monte carlo procedures that are very fast to converge in the final section we consider extensions to the case of blind deconvoluti

finite alphabet information and multivariate blind deconvolution and identification of linear systems this article addresses the problem of estimating a multivariate linear system from its output when the input is an unobservable sequence of random vectors with finite alphabet distribution by explicitly utilizing the finite alphabet property an estimation method is proposed under the traditional inverse filtering paradigm as a generalization of a univariate method that has been studied previously identifiability of multivariate systems by the proposed method is proved mathematically under very mild conditions that can be satisfied even if the input is nonstationary and has both cross channel and serial statistical dependencies statistical super efficiency in estimating both parametric and nonparametric systems is also established for an alphabet based cost function

adaptive regulation of high order lower triangular systems an adding a power integrator technique abstract in this paper a new adaptive control technique called adding a power integrator is proposed for a class of linearly parametrized high order nonlinear systems that are of a lower triangular structure but neither feedback linearizable nor affine in the control input using the adaptive adding a power integrator technique as a basic design tool we show how to explicitly construct in a recursive manner a smooth adaptive controller that solves the problem of adaptive regulation with global stability the development of the adding a power integrator technique leads to a significant relaxation on the system nonlinearities such as feedback linearizability and affineness which are known to be two standard hypotheses made in the existing nonlinear adaptive control schemes

adding one power integrator a tool for global stabilization of high order lower triangular systems a new feedback design tool called adding a power integrator is introduced and used to solve the problem of global robust stabilization for a significant class of uncertain nonlinear systems that are of a lower triangular form but neither necessarily feedback linearizable fully or partially nor affine in the control input and therefore cannot be dealt with via conventional approaches sufficient conditions are presented under which a globally stabilizing smooth state feedback control law can be explicitly constructed using repeatedly the adding one power integrator technique this novel design technique permits removal of the feedback linearizability and linearity of the control input which have been two common assumptions so far in the literature of nonlinear control

a scaled feedback stabilization of power integrator triangular systems using unbounded time varying scaling of the states we design c1 feedback laws for power integrator triangular systems which globally asymptotically stabilize gas the origin despite the uncontrollability of the linearization with bounded scaling the feedback laws achieve global practical stability gps for a trade off between gas gps of the origin and unboundedness boundedness of the scaling we construct a dynamic version of these feedback laws

on the asymptotic behaviour of the pseudolikelihood ratio test statistic this paper concerns the asymptotic distribution of the likehood ratio statistic t for testing h 0 0 based on the pseudolikelihood l where is a simple estimator of we show that the asymptotic distribution of t under h 0 is a weighted sum of indepepdent x 1 2 variables where the weights involve the asymptotic joint covariance matrix of and the score function for some sufficient conditions are provided for the limiting distribution to be x 2 the result is extended to allow 0 to be a boundary value of the parameter space and to be misspecified in l we also examine the issue of power loss when is misspecified in l several examples including variance component models multivariate survival models genetic linkage analysis and the behrens fisher problem are presented to demonstrate the scope of the problems considered and to illustrate the results

the impact of litter effects on dose response modeling in teratology the fitting of dose response models to teratology data involving littermates in order to generate estimates of teratogenic risk is receiving increasing attention as a potential alternative to the safety factor approach to risk estimation in this paper we utilize the beta binomial distribution to introduce varying degrees of intralitter correlation and for purposes of illustration consider a logistic dose response model that describes the logit of risk as a straight line function of ln dose the biases and exact and asymptotic variances of the maximum likelihood estimators of the intercept and slope are studied by simulation as a function of the intralitter correlation structure

blind deconvolution of discrete linear systems we study the blind deconvolution problem in the case where the input noise has a finite discrete support and the transfer linear system is not necessarily minimum phase we propose a new family of estimators built using algebraic considerations the estimates are consistent under very wide assumptions the input signal need not be independently distributed the cardinality of the finite support may be estimated simultaneously we consider in particular ar systems in this case we prove that the estimator of the parameters is perfect a s with a finite number of observations

on the use of stochastic ordering to test for trend with clustered binary data we introduce the use of stochastic ordering for defining treatment related trend in clustered exchangeable binary data for both when cluster sizes are fixed and when they vary randomly in the latter case there is a well documented tendency for such data to be sparse a problem we address by making an assumption of interpretability or equivalently marginal compatibility our procedures are based on a representation of the joint distribution of binary exchangeable random variables by a saturated model and may hence be considered nonparametric the definition of trend by stochastic ordering is proposed to ensure a flexibility that allows for various forms of monotone increases in response to the cluster as a whole to be included in the evaluation of the trend we obtain maximum likelihood estimates of probability functions under stochastic ordering using mixture likelihood based algorithms since the data are sparse we avoid the use of asymptotic results and obtain p values of the likelihood ratio procedures by permutation resampling we demonstrate how the proposed framework can be used in risk assessment and provide comparisons with existing procedures

estimation bias using the beta binomial distribution in teratology kupper et al 1986 biometrics 42 85 98 considered the fitting of dose response regressions to litter proportions in teratology experiments they found that the estimators which maximise the beta binomial likelihood become biased when the intralitter correlation is incorrectly assumed to be homogeneous this paper considers the nature of this bias and its implications for the analysis of litter proportions in toxicology

profile likelihood based confidence interval of the intraclass correlation for binary outcome data sampled from clusters the intraclass correlation in binary outcome data sampled from clusters is an important and versatile measure in many biological and biomedical investigations properties of the different estimators of the intraclass correlation based on the parametric semi parametric and nonparametric approaches have been studied extensively mainly in terms of bias and efficiency see for example ridout et al biometrics 1999 55 137 148 paul et al journal of statistical computation and simulation 2003 73 507 523 and lee statistical modelling 2004 4 113 126 but little attention has been paid to extending these results to the problem of the confidence intervals in this article we generalize the results of the four point estimators by constructing asymptotic confidence intervals obtaining closed form asymptotic and sandwich variance expressions of those four point estimators it appears from simulation results that the asymptotic confidence intervals based on these four estimators have serious under coverage to remedy this we introduce the fisher s z transformation approach on the intraclass correlation coefficient the profile likelihood approach based on the beta binomial model and the hybrid profile variance approach based on the quadratic estimating equation for constructing the confidence intervals of the intraclass correlation for binary outcome data as assessed by monte carlo simulations these confidence interval approaches show significant improvement in the coverage probabilities moreover the profile likelihood approach performs quite well by providing coverage levels close to nominal over a wide range of parameter combinations we provide applications to biological data to illustrate the methods copyright 2012 john wiley sons ltd

iss property for time varying systems and application to partial static feedback stabilization and asymptotic tracking a concept of input to state stability iss for time varying control systems is introduced that constitutes extension of the well known notion concerning the autonomous case we use this concept to derive sufficient conditions for global stabilization of triangular systems by means of a time varying smooth feedback and to achieve asymptotic tracking of unbounded signals

deconvolution and estimation of transfer function phase and coefficients for nongaussian linear processes nongaussian linear processes are considered it is shown that the phase of the transfer function can be estimated under broad conditions this is not true of gaussian linear processes and in this sense gaussian linear processes are atypical the asymptotic behavior of a phase estimate is determined the phase estimates make use of bispectral estimates these ideas are applied to a problem of deconvolution which is effective even when the transfer function is not minimum phase a number of computational illustrations are given

blind identification and deconvolution of linear systems driven by binary random sequences the problem of blind identification and deconvolution of linear systems with independent binary inputs is addressed to solve the problem a linear system is applied to the observed data and adjusted so as to produce binary outputs it is proved that the system coincides with the inverse of the unknown system with scale and shift ambiguities whether it is minimum or nonminimum phase these results are derived for nonstationary independent binary inputs of infinite or finite length based on these results an identification method is proposed for parametric linear systems it is shown that under some mild conditions a consistent estimator of the parameter can be obtained by minimizing a binariness criterion for the output data unlike many other blind identification and deconvolution methods this criterion handles nonstationary signals and does not utilize any moment information of the inputs three numerical examples are presented to demonstrate the effectiveness of the proposed method

backstepping design for time varying nonlinear systems with unknown parameters abstract a simple backstepping design procedure is proposed and sufficient conditions for global partial state and dynamic feedback stabilization for a class of triangular systems with unknown time varying parameters are derived

quantitative risk assessment for teratological effects abstract this article presents a quantitative procedure for using a benchmark dose to obtain low dose risk estimates for reproductive and developmental toxic effects this procedure combines the best features of the previously proposed methods for handling litter effects for teratology data and the currently used methods for quantitative risk assessment the beta binomial distribution is used to account for litter effects and the weibull dose response model is used for modeling teratogenic effects a benchmark dose defined to be the lowest dose at which the excess risk does not exceed 1 with 95 confidence is proposed to replace the no observed effect level noel the noel is generally the highest experimental dose that is not statistically different from the control the noel approach does not use experimental data effectively for quantitative risk estimation in this article a lower limit on the safe dose is estimated by linearly extrapolating downward from the benchmark dose this procedure is

extended generalized estimating equations for clustered data abstract typically analysis of data consisting of multiple observations on a cluster is complicated by within cluster correlation estimating equations for generalized linear modeling of clustered data have recently received much attention this article proposes an extension to the generalized estimating equation method proposed by liang and zeger which treats within cluster correlations as nuisance parameters using ideas from extended quasi likelihood estimating equations for regression and association parameters are provided simultaneously the resulting estimators are proven to be asymptotically normal and consistent under certain conditions the consistency of regression estimators allows incorrect modeling of the correlation among repeated responses the method is illustrated with an analysis of data from a developmental toxicity study

estimating logistic regression parameters for bivariate binary data summary consider bivariate binary data with possibly different covariates for each marginal binary observation suppose that the correlation between paired observations is a nuisance and the interest lies in estimating logistic regression parameters associated with the covariates we consider the estimator obtained by using the independence estimating equation with corrected variance this estimator can be very efficient and robust compared with maximum likelihood or the estimator of liang and zeger some comments are made concerning higher dimensional multivariate data

identification of nonminimum phase systems using higher order statistics a method is presented for identification of linear time variant nonminimum phase systems when only output data are available the input sequence need not be independent but it must be non gaussian with some special properties described in the test the authors model a finite dimensional system as an arma autoregressive moving average rational function of known orders but the special cases of ar ma and all pass models are also considered to estimate the parameters of their model the authors utilize both second and higher order statistics of the output which may be contaminated by additive zero mean gaussian white noise of unknown variance the parameter estimators obtained are proved under mild conditions to be consistent simulations verify the performance of the proposed method in the case of relatively low signal to noise ratios and when there is a model order mismatch

estimating intraclass correlation for binary data summary this paper reviews many different estimators of intraclass correlation that have been proposed for binary data and compares them in an extensive simulation study some of the estimators are very specific while others result from general methods such as pseudo likelihood and extended quasi likelihood estimation the simulation study identifies several useful estimators one of which does not seem to have been considered previously for binary data estimators based on extended quasi likelihood are found to have a substantial bias in some circumstances

two random effects models for multivariate binary data we consider two regression models for multivariate binary data in the case that the marginal success probabilities are unequal the first model is based on an additive random effect operating on the probability scale the second model assumes a discrete random effect operates on the logit scale consequently the interpretation of the regression parameters differs for the two models their use is illustrated by application to some dental data

sufficient conditions for closed loop asymptotic controllability and stabilization by smooth time varying feedback integrator for general finite dimensional time varying nonlinear systems we derive sufficient conditions for closed loop asymptotic controllability and asymptotic stabilization by means of a smooth ordinary time varying feedback integrator the main hypotheses are based on the existence of an almost smooth time varying control lyapunov function the corresponding results generalize earlier works in the literature concerning dynamic stabilization for autonomous systems

the evaluation of integrals of the form f t exp t 2 dt application to logistic normal models abstract logistic normal distributions and related functions arise in a variety of statistical applications of current interest including binary measurement error models and the analysis of teratogenicity experiments analytic intractability has led to the development of numerous approximations to the desired forms often with consequences that have not been well studied a method is developed to compute these forms to arbitrary accuracy and comparative calculations are made that show when the common numerical alternative 20 point gaussian quadrature begins to fail by using a simple matrix transformation this method can be used with multiple covariate regression models of the logistic normal form we conducted a simulation study that compares the ability of 20 point gaussian quadrature and our new method to obtain the maximum likelihood estimator of relative risk in the logistic normal measurement error model using standard subroutines to maximize the likelihood equations 27 of 50 trials failed to

blind restoration of linearly degraded discrete signals by gibbs sampling this paper addresses the problem of simultaneous parameter estimation and restoration of discrete valued signals that are blurred by an unknown fir filter and contaminated by additive gaussian white noise with unknown variance assuming that the signals are stationary markov chains with known state space but unknown initial and transition probabilities bayesian inference of all unknown quantities is made from the blurred and noisy observations a monte carlo procedure called the gibbs sampler is employed to calculate the bayesian estimates simulation results are presented to demonstrate the effectiveness of the method

estimating the number of clusters for the analysis of correlated binary response variables from unbalanced data dominating the literature on sample size determination are papers concerned with observations collected from independent responses we discuss in this paper under the common correlation model for binary responses the issue of estimating the number of groups when the sampling units are clusters of variable sizes

an interval estimate for the interclass correlation in beta binomial sampling the beta binomial distribution has been frequently applied to model intraclass correlation among dichotomous responses in cluster sampling to avoid using sophisticated numerical procedures to calculate maximum likelihood estimates many authors have proposed different moment estimators most of them focused their discussion however on point estimation only in this paper we have developed a closed form interval estimate for the intraclass correlation and have shown it has asymptotically correct coverage we have further used a monte carlo simulation to evaluate the performance of the proposed interval estimation in a variety of situations we have found that when the number of clusters is moderate 50 the proposed interval estimation performs reasonably well except for the situation where the intraclass correlation is large and the underlying mean response probability is small even when the number of clusters is as small as 20 the proposed interval estimation may still be useful in the situation where the intraclass correlation is small we have also extended the proposed interval estimate to accommodate stratified sampling in which each stratum has a different mean response probability but has a common intraclass correlation

identification of noisy linear systems with discrete random input we propose a new method for the blind deconvolution of a discrete linear system perturbed with additive noise the method comes from a characterization of discrete variables when perturbed with additive noise with unknown variance together with a characterization of this variance through hankel matrix based on this probabilistic description an estimator is proposed for the inverse system and the variance of the noise these estimators are shown to be consistent under weak assumptions whatever the signal to noise ratio is in particular the input signal needs not be independently distributed numerical examples demonstrate the effectiveness of the method even when nonstationary signals are used as inputs

estimation and testing with overdispersed proportions using the beta logistic regression model of heckman and willis summary methods are presented for modeling dose related effects in proportion data when extra binomial variability is a concern motivation is taken from experiments in developmental toxicology where similarity among conceptuses within a litter leads to intralitter correlations and to overdispersion in the observed proportions appeal is made to the well known beta binomial distribution to represent the overdispersion from this an exponential function of the linear predictor is used to model the dose response relationship the specification was introduced previously for econometric applications by heckman and willis it induces a form of logistic regression for the mean response together with a reciprocal biexponential model for the intralitter correlation large sample likelihood based methods for estimating and testing the joint proportion correlation response are studied a developmental toxicity data set illustrates the methods

adding an integrator for the stabilization problem abstract we study the relationship between the following two properties p1 the system x f x y y v is locally asymptotically stabilizable and p2 the system x f x u is locally asymptotically stabilizable where x r n y r dayawansa martin and knowles have proved that these properties are equivalent if the dimension n 1 here using the so called control lyapunov function approach a we propose another more constructive and somewhat simpler proof of dayawansa martin and knowles s result b we show that in general p1 does not imply p2 for dimensions n larger than 1 c we prove that p2 implies p1 if some extra assumptions are added like homogeneity of the system by using the latter result recursively we obtain a sufficient condition for the local asymptotic stabilizability of systems in a triangular form

tree structured logistic model for over dispersed binomial data with application to modeling developmental effects this article proposes tree structured logistic regression modeling for over dispersed binomial data recursive partitioning is performed using a combination of statistical tests and residual analysis the splitting criterion in cross validation is based on the deviance function a nested grid algorithm to estimate the bootstrap parameters is developed the regression tree procedure provides a new approach for exploring in detail the relationship between the binomial response and explanatory variables the proposed procedure is used to model the relationship between the incidence of malformation and dose and fetal weight using data from a developmental experiment conducted at the national center for toxicological research a conditional gaussian chain model is used to account for the effect of fetal weight by dose

a parametric model for cluster correlated categorical data summary a fully parametric copula model for symmetric dependent clustered categorical data is discussed the model accommodates any marginal regression models of interest and admits a broad range of within cluster association the form of the distribution is independent of cluster size and may be used to model data with varying cluster sizes the model contains an association parameter that is estimated from the data to give a measure of the strength of the within cluster association and also a test of independence two examples are given to illustrate the methods

adaptive control of nonlinearly parameterized systems the smooth feedback case studies global adaptive control of nonlinearly parameterized systems with uncontrollable linearization using a parameter separation technique and the tool of adding a power integrator we develop a feedback domination design approach for the explicit construction of a smooth adaptive controller that solves the problem of global state regulation in contrast to the existing results in the literature a key feature of our adaptive regulator is its minimum order property namely no matter how big the number of unknown parameters is the order of the dynamic compensator is identical to one and is therefore minimal as a consequence global state regulation of feedback linearizable systems with nonlinear parameterization is achieved by one dimensional adaptive controllers without imposing any extra e g convex concave conditions on the unknown parameters

maximum standardized cumulant deconvolution of non gaussian linear processes a linear process is represented as a driving white noise convolved with a system response sequence the concept of natural peakedness of a system response sequence is defined and its properties are investigated utilizing natural peakedness the convergence theory of maximum standardized cumulant deconvolution is established and the uniqueness theorem of non gaussian linear process representations is proved in addition autoregressive models on a countable abelian group are defined and the relation between cumulant deconvolution and autoregressive models is given

source separation when the input sources are discrete or have constant modulus in this paper we present a new method for the source separation problem when some prior information on the input sources is available more specifically we study the situation where the distributions of the input signals are discrete or are concentrated on a circle the method is based on easy properties of hankel forms and on the divisibility of gaussian distributions in both situations we prove that the estimator converges in absence of noise or if we know the first moments of the noise up to its scale moreover in the absence of noise the estimate converges with a finite number of observations

explicit formulas of feedback stabilizers for a class of triangular systems with uncontrollable linearization for a class of nonlinear systems having triangular structure we provide explicit formulas for feedback controllers that exhibit global stabilization the global part of our analysis is mainly based on a version of sontag s input to state stability property and constitutes a generalization of previous works of the second author

pseudolikelihood modeling of multivariate outcomes in developmental toxicology abstract the primary goal of this article is to determine benchmark doses based on the ethylene glycol study which comprises data from a developmental toxicity study in mice because the data involve a vector of malformation indicators a flexible model for multivariate clustered data is required an exponential family model is considered and pseudolikelihood based inferential tools are proposed hence avoiding excessive computational requirements

constructive nonsmooth stabilization of triangular systems the problem of local asymptotic continuous feedback stabilization of single input nonlinear systems is considered here the aim is to explicitly construct a continuous asymptotically stabilizing feedback for a class of nonlinear systems that is known to be continuous feedback asymptotically stabilizable but for which the known results do not provide an effective method to compute the stabilizer computer simulations are included to show practical applicability of our approach

local polynomial estimation in multiparameter likelihood models abstract the nonparametric regression technique of local polynomial fitting is extended to multiparameter likelihood models some well known appealing features of local polynomial smoothers such as the behavior at the boundary are shown to carry over to the multiparameter case asymptotic consistency and normality of the resulting estimators are derived under suitable regularity conditions this work is motivated by the need for a nonparametric alternative to parametric dose response models for clustered binary data probability models for clustered binary response data include a success probability parameter and one or more correlation parameters the proposed local polynomial estimators can play an important role as a diagnostic tool or to suggest the form of the functional relationships in parametric likelihood models as an illustration it is shown how the local likelihood estimation procedure can be implemented for fitting a dose response curve based on the beta binomial model a data example and

on the use of the quasi likelihood method in teratological experiments this paper concerns the impact of litter effects on the inference of dose response relationships in teratological experiments with binary response kupper et al 1986 biometrics 42 85 98 concluded that when intra litter correlations are dose dependent the use of a common intra litter correlation likelihood based on the beta binomial distribution could lead to severe bias in estimation of the parameter beta which characterizes the dose response relationship we show here that the problems of bias and coverage probability for beta could still be substantial when one uses the likelihood with heterogeneous intra litter correlations we then examine through a simulation study the performance of the quasi likelihood method wedderburn 1974 biometrika 61 439 447 and recommend that this method with a common intra litter correlation parameter be used when the number of litters is small or modest

cluster sampling techniques in quantal response teratology and developmental toxicity studies summary this paper presents a model free approach for evaluating teratology and developmental toxicity data involving clustered binary responses in teratology studies a major statistical problem arises from the effect of intralitter correlation or the potential for littermates to respond similarly some statistical methods impose strict distributional assumptions to account for extra binomial variation while others rely on nonparametric resampling and empirical variance estimation techniques quasi likelihood methods and generalized estimating equations gee which model the marginal mean variance relationship also avoid strict distributional assumptions the proposed approach often used to analyze complex sample survey data is based on a first order taylor series approximation and a between cluster variance estimation procedure yielding consistent variance estimates for binomial based proportions and regression coefficients from dose response models the cluster sample technique presented here in the context of a logistic dose response model incorporates many of the advantages of quasi likelihood methods are valid for any underlying nested correlation structure and are adaptable to a variety of analytical settings the results of a simulation study show the cluster sample technique to be a viable competitor to gee methods currently receiving attention

bias corrected maximum likelihood estimator of the intraclass correlation parameter for binary data a popular model to analyse over under dispersed proportions is to assume the extended beta binomial model with dispersion intraclass correlation parameter phi and then to estimate this parameter by maximum likelihood however it is well known that maximum likelihood estimate mle may be biased when the sample size n or the total fisher information is small in this paper we obtain a bias corrected maximum likelihood bcml estimator of the intraclass correlation parameter and compare it by simulation in terms of bias and efficiency with the mle an estimator q 2 based on optimal quadratic estimating equations of crowder and recommended by paul et al and a double extended quasi likelihood deql estimator proposed by lee the bcml estimator has superior bias and efficiency properties in most instances analyses of a set of toxicological data from paul and a set of medical data pertaining to chromosomal abnormalities among survivors of the atomic bomb in hiroshima from otake and prentice show in general much improvement in standard errors of the bcml estimates over the other three estimates

a full likelihood procedure for analysing exchangeable binary data a full likelihood procedure is proposed for analyzing correlated binary data under the assumption of exchangeability the binomial and beta binomial models are shown to occur as special cases correspondingly respectively to the choice of degenerate and beta mixing distributions for a finite exchangeable binary sequence of random variables expressions for the joint distribution moments and correlations of all orders are derived maximum likelihood estimates of the moments of all orders are computed and used to estimate correlations and the distribution of the number of responses in a cluster in an application to developmental toxicology data analysis the procedure introduced is compared with a beta binomial and a generalized estimating equation procedure in which mean response and intralitter correlation are linked to dose

a continuous feedback approach to global strong stabilization of nonlinear systems we present a continuous feedback approach to the problem of global strong stabilization for genuinely nonlinear systems that may not be stabilized even locally by any smooth feedback we describe conditions under which it is possible to prove while no smooth controllers exist the existence of continuous state feedback control laws that achieve global strong stability gss in the sense of kurzweil 1956 the proof is constructive and carried out by developing a machinery which combines the theory of homogeneous systems with the idea of adding a power integrator for the explicit construction of globally stabilizing continuous controllers we then illustrate by means of examples how this machinery can be used to overcome the topological obstruction caused by smooth feedback and hence resulting in new solutions to a variety of open control problems including global stabilization of an underactuated unstable two degree of freedom mechanical system

blind deconvolution of linear systems with multilevel nonstationary inputs a method is proposed to deal with the problem of blind deconvolution of a special non gaussian linear process in which the input to the linear system is a real or complex valued multilevel random sequence that satisfies certain regularity conditions the gist of the method is to apply a linear filter to the observed process and adjust the filter until a multilevel output is obtained it is shown that the deconvolution problem can be solved with only scale rotation and shift ambiguities if the output sequence of the filter contains a subsequence that converges weakly to a multilevel random variable a cost function is proposed so that any minimizer of the cost function provides a solution to the deconvolution problem moreover when the process is parametric it is shown that a consistent estimator of the parameter can be obtained by minimizing an empirical criterion the estimation accuracy is shown to depend on the tail behavior of the inverse system which in many cases decays exponentially as the sample size grows special consideration is given to applications in the equalization of digital communications systems

new criteria for blind deconvolution of nonminimum phase systems channels a necessary and sufficient condition for blind deconvolution without observing the input of nonminimum phase linear time invariant systems channels is derived based on this condition several optimization criteria are proposed and their solution is shown to correspond to the desired response these criteria involve the computation only of second and fourth order moments implying a simple tap update procedure the proposed methods are universal in the sense that they do not impose any restrictions on the probability distribution of the unobserved input sequence it is shown that in several important cases e g when the additive noise is gaussian the proposed criteria are essentially unaffected

an exact trend test for correlated binary data the problem of testing a dose response relationship in the presence of exchangeably correlated binary data has been addressed using a variety of models most commonly used approaches are derived from likelihood or generalized estimating equations and rely on large sample theory to justify their inferences however while earlier work has determined that these methods may perform poorly for small or sparse samples there are few alternatives available to those faced with such data we propose an exact trend test for exchangeably correlated binary data when groups of correlated observations are ordered this exact approach is based on an exponential model derived by molenberghs and ryan 1999 and ryan and molenberghs 1999 and provides natural analogues to fisher s exact test and the binomial trend test when the data are correlated we use a graphical method with which one can efficiently compute the exact tail distribution and apply the test to two examples

analysis of a nonparametric blind equalizer for discrete valued signals an investigation is carried out to identify the variables that may affect the numerical properties of an inverse filtering method of blind equalization for linear channels with discrete input the analysis is under a nonparametric framework in which all coefficients of the inverse filter equalizer can be freely chosen it reveals in particular that the filter length plays two contradictory roles increasing the length always helps improve the accuracy of inverse filtering but when the filter is too long the numerical properties of the method may deteriorate other influential variables include the constellation and possibly time varying probability distribution of the input signals the method is also shown to be highly efficient for nonparametric channel estimation as was shown for estimating parametric channels simulations are carried out to verify the analytical findings concerning the numerical properties

a joint estimation approach for two tone image deblurring by blind deconvolution a new statistical method is proposed for deblurring two tone images i e images with two unknown grey levels that are blurred by an unknown linear filter the key idea of the proposed method is to adjust a deblurring filter until its output becomes two tone two optimization criteria are proposed for the adjustment of the deblurring filter a three step iterative algorithm tsia is also proposed to minimize the criteria it is proven mathematically that by minimizing either of the criteria the original nonblurred image along with the blur filter will be recovered uniquely only with possible scale shift ambiguities at high snr the recovery is guaranteed not only for i i d images but also for correlated and nonstationary images it does not require a priori knowledge of the statistical parameters or the tone values of the original image neither does it require a priori knowledge of the phase or other special information e g fir symmetry nonnegativity etc about the blur filter numerical experiments are carried out to test the method on synthetic and real images

design effects for binary regression models fitted to dependent data dependent data such as arise with cluster sampling typically yield variances of parameter estimates which are larger than would be provided by a simple random sample of the same size this variance inflation factor is called the design effect of the estimator design effects have been derived for cluster sampling designs using simple estimators such as means and proportions and also for linear regression coefficient estimators in this paper we show that a method to derive design effects for linear regression estimators extends to generalized linear models for binary responses in particular some simple expressions for design effects in the linear regression model provide accurate approximations for binary regression models such as those based on the logistic probit and complementary log log links we corroborate our findings with two examples and some simulation studies