the use of mixed logit models to reflect heterogeneity in capture recapture studies summary we examine issues in estimating population size n with capture recapture models when there is variable catchability among subjects we focus on a logistic normal mixed model for which the logit of the probability of capture is an additive function of a random subject and a fixed sampling occasion parameter when the probability of capture is small or the degree of heterogeneity is large the log likelihood surface is relatively flat and it is difficult to obtain much information about n we also discuss a latent class model and a log linear model that account for heterogeneity and show that the log linear model has greater scope models assuming homogeneity provide much narrower intervals for n but are usually highly overly optimistic the actual coverage probability being much lower than the nominal level

on flat systems behaviors and observable image representations in this short paper we propose a definition of flatness for systems not necessarily given in input state output representation a flat system is a system for which there exists a mapping such that the manifest system behavior is equal to the image of this mapping and such that the latent variable appearing in this image representation can be written as a function of the manifest variable and its derivatives up to some order for linear differential systems flatness is equivalent to controllability we will generalize the main theorem of levine and nguyen systems control lett 48 2003 69 to general linear differential systems

performance modeling and algorithm characterization for robust image segmentation this paper presents a probabilistic framework based on bayesian theory for the performance prediction and selection of an optimal segmentation algorithm the framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm the input image information content is measured in terms of image features while the candidate segmentation algorithm s behavioral characteristics are captured through the use of segmentation quality features gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the berkeley segmentation dataset using four candidate segmentation algorithms

a unified treatment of integer parameter models abstract it is shown that there is an analog to the score function the derivative of the log likelihood with respect to the parameter here called the difference score which plays an important theoretical role in integer parameter models a unified treatment of integer parameter models can be obtained by recognizing that many commonly used models have a linear difference score lds it is established that many of the exact results available for exponential family models also hold for lds models in particular we find an information lower bound show the uniqueness of the maximum likelihood estimator establish monotone likelihood ratio and establish optimality properties in two sided tests it is also shown that if one has a multiparameter likelihood in which there is a conditional likelihood depending on n alone then these exact results apply there as well it is also established when and why the usual likelihood methodology will apply to a class of nonstandard models in which the natural approxima

adaptive segmentation of noisy and textured images abstract an image segmentation algorithm is described which is based on the integration of signal model parameter estimates and maximum a posteriori labelling the parameter estimation is based on either a maximum likelihood based method for a quadric signal model or a maximum pseudo likelihood based method for a gauss markov signal model the first case is applicable to standard grey level image segmentation as well as segmentation of shaded 3d surfaces while the second case is applicable to texture segmentation a key aspect of the algorithm is the incorporation of a coarse to fine processing strategy which limits the search for the optimum labelling at any one resolution to a subset of labellings which are consistent with the optimum labelling at the previous coarser resolution consistency is in terms of a prior label model which specifies the conditional probability of a given label in terms of the labelling at the previous level of resolution it is shown how such an approach leads to a simple relaxation procedure based on local pyramid node computations an extension of the algorithm is also described which performs accurate inter region boundary placement using a step wise refinement procedure based on a simple adaptive filter the problem of automatic determination of the number of regions is also addressed it is shown how a simple agglomerative clustering idea again based on pyramid node computations can effectively solve this problem

efficient bootstrap simulation on donne deux idees pour renforcer une simulation la premiere est d equilibrer les echantillons simules la seconde est de faire un usage explicite des approximations qui ne demandent pas de simulation

a martingale estimating equation for a capture recapture experiment in discrete time we use martingale theory and a method of moments technique to derive a class of estimators for the size of a closed population in a capture recapture experiment in discrete time where the capture probabilities on the successive capture occasions are allowed to be different the same capture probability is assumed to apply to all animals in the population on the same occasion explicit expressions are given for these estimators and their associated standard errors which involve only simple computation an optimal estimator for the population size is found asymptotic results are readily obtained by an application of a martingale central limit theorem two examples are given to compare the results

flatness for linear fractional systems with application to a thermal system this paper is devoted to the study of the flatness property of linear time invariant fractional systems in the framework of polynomial matrices of the fractional derivative operator we give a characterization of fractionally flat outputs and a simple algorithm to compute them we also obtain a characterization of the so called fractionally 0 flat outputs we then present an application to a two dimensional heated metallic sheet whose dynamics may be approximated by a fractional model of order 1 2 the trajectory planning of the temperature at a given point of the metallic sheet is obtained thanks to the fractional flatness property without integrating the system equations the pertinence of this approach is discussed on simulations

some basic structural properties of generalized linear systems abstract this paper shows how to give a kalman state variable realization to generalized linear systems simple characterizations of controllability observability of their duality and of a minimal realization are proposed our techniques based on elementary module theory are applicable to time varying systems as well as constant systems the note ends with a brief discussion of asymptotic observers in the constant case

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

multilist population estimation with incomplete and partial stratification multilist capture recapture methods are commonly used to estimate the size of elusive populations in many situations lists are stratified by distinguishing features such as age or sex stratification has often been used to reduce biases caused by heterogeneity in the probability of list membership among members of the population however it is increasingly common to find lists that are structurally not active in all strata we develop a general method to deal with cases when not all lists are active in all strata using an expectation maximization em algorithm we use a flexible log linear modeling framework that allows for list dependencies and differential probabilities of ascertainment in each list finally we apply our method of estimating population size to two examples

efficient graph based image segmentation this paper addresses the problem of segmenting an image into regions we define a predicate for measuring the evidence for a boundary between two regions using a graph based representation of the image we then develop an efficient segmentation algorithm based on this predicate and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties we apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph and illustrate the results with both real and synthetic images the algorithm runs in time nearly linear in the number of graph edges and is also fast in practice an important characteristic of the method is its ability to preserve detail in low variability image regions while ignoring detail in high variability regions

parameter based asymptotics in certain cases statistical methods based on standard maximum likelihood asymptotics become valid as the true parameter value approaches a boundary of the parameter space examples are given which motivate a general parameter based asymptotic theory and a result is obtained which covers such situations of particular interest are applications to stochastic process models

flat output characterization for linear systems using polynomial matrices this paper is devoted to the study of linear flat outputs for linear controllable time invariant systems in polynomial matrix form we characterize the transformations expressing the system variables in terms of a linear flat output and derivatives called defining matrices as the kernel of a polynomial matrix an application to trajectory planning is presented showing the usefulness of the present characterization

an objective approach to prior mass functions for discrete parameter spaces we present a novel approach to constructing objective prior distributions for discrete parameter spaces these types of parameter spaces are particularly problematic as it appears that common objective procedures to design prior distributions are problem specific we propose an objective criterion based on loss functions instead of trying to define objective probabilities directly we systematically apply this criterion to a series of discrete scenarios previously considered in the literature and compare the priors the proposed approach applies to any discrete parameter space making it appealing as it does not involve different concepts according to the model supplementary materials for this article are available online

robust path tracking using flatness for fractional linear mimo systems a thermal application this paper deals with robust path tracking using flatness principles extended to fractional linear mimo systems as soon as the path has been obtained by means of the fractional flatness a robust path tracking based on crone control is presented flatness in path planning is used to determine the controls to apply without integrating any differential equations when the trajectory is fixed in space and in time several developments have been made for fractional linear siso systems using a transfer function approach for fractional systems especially in mimo developments are still to be made throughout this paper flatness principles are applied using polynomial matrices for fractional linear mimo systems to illustrate the robustness performances a third generation multi scalar crone controller is compared to a pid one

image segmentation by a contrario simulation segmenting an image into homogeneous regions generally involves a decision criterion to establish whether two adjacent regions are similar decisions should be adaptive to get robust and accurate segmentation algorithms avoid hazardous a priori and have a clear interpretation we propose a decision process based on a contrario reasoning two regions are meaningfully different if the probability of observing such a difference in pure noise is very low since the existing analytical methods are intractable in our case we extend them to allow a mixed use of analytical computations and monte carlo simulations the resulting decision criterion is tested experimentally through a simple merging algorithm which can be used as a post filtering and validation step for existing segmentation methods

analysis of a multiple recapture census by computing conditional probabilities in a multiple recapture census of a closed population using fixed sample sizes the total number of different animals captured during the census is a sufficient statistic for the population sized conditional on the population size the exact probability distribution of this sufficient statistic may be calculated and an algorithm for doing this is given standard techniques are applied to describe methods for using the conditional probabilities to form median and interval estimates of the population size the methods are illustrated with examples

monte carlo confidence intervals des intervalles de confiance non parametriques obtenus par la methode de monte carlo sont utiles quand les intervalles obtenus par les methodes analytiques sont indisponibles ou ne correspondent pas au niveau nominal on fournit des formules permettant de generer un intervalle de niveau donne et de quantifier la variation due a la methode de monte carlo

flatness and defect of non linear systems introductory theory and examples we introduce flat systems which are equivalent to linear ones via a special type of feedback called endogenous their physical properties are subsumed by a linearizing output and they might be regarded as providing another nonlinear extension of kalman s controllability the distance to flatness is measured by a non negative integer the defect we utilize differential algebra where flatness and defect are best defined without distinguishing between input state output and other variables many realistic classes of examples are flat we treat two popular ones the crane and the car with n trailers the motion planning of which is obtained via elementary properties of plane curves the three non flat examples the simple double and variable length pendulums are borrowed from non linear physics a high frequency control strategy is proposed such that the averaged systems become flat

mark recapture models with parameters constant in time the jolly seber method which allows for both death and immigration is easy to apply but often requires a larger number of parameters to be estimated tha would otherwise be necessary if i survival rate phi or ii probability of capture p or iii both phi and p can be assumed constant over the experimental period models with a reduced number of parameters are desirable in the present paper maximum likelihood ml solutions for these three situations are derived from the general ml equations of jolly 1979 in sampling biological populations r m cormack g p patil and d s robson eds 277 282 a test is proposed for heterogeneity arising from a breakdown of assumptions in the general jolly seber model tests for constancy of phi and p are provided an example is given in which these models are fitted to data from a local butterfly population

capture recapture when time and behavioral response affect capture probabilities summary we consider a capture recapture model in which capture probabilities vary with time and with behavioral response two inference procedures are developed under the assumption that recapture probabilities bear a constant relationship to initial capture probabilities these two procedures are the maximum likelihood method both unconditional and conditional types are discussed and an approach based on optimal estimating functions the population size estimators derived from the two procedures are shown to be asymptotically equivalent when population size is large enough the performance and relative merits of various population size estimators for finite cases are discussed the bootstrap method is suggested for constructing a variance estimator and confidence interval an example of the deer mouse analyzed in otis et al 1978 wildlife monographs62 93 is given for illustration

estimation of survival in marked populations with possibly dependent sighting probabilities summary a simply parameterized class of models for the estimation of survival probability in a marked population is introduced which allows tests of dependence between sighting probabilities and constancy of survival or sighting probabilities estimation and hypothesis testing based on the likelihood function are discussed by simulation the asymptotic properties of the test for dependence are shown to hold for quite modest sample sizes the degree of bias in estimation of survival probabilities when dependence between sightings is not taken into account is shown to increase as the probability of a nonsighting following a nonsighting increases a comprehensive analysis of the data from a mark recapture experiment dealing with survival of abalone is given

multiresolution color image segmentation image segmentation is the process by which an original image is partitioned into some homogeneous regions in this paper a novel multiresolution color image segmentation mcis algorithm which uses markov random fields mrf s is proposed the proposed approach is a relaxation process that converges to the map maximum a posteriori estimate of the segmentation the quadtree structure is used to implement the multiresolution framework and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore maximize the map estimate the multiresolution scheme enables the use of different dissimilarity measures at different resolution levels consequently the proposed algorithm is noise resistant since the global clustering information of the image is required in the proposed approach the scale space filter ssf is employed as the first step the multiresolution approach is used to refine the segmentation experimental results of both the synthesized and real images are very encouraging in order to evaluate experimental results of both synthesized images and real images quantitatively a new evaluation criterion is proposed and developed

estimating the number of faults in a system abstract we consider a reliability system with an unknown number of faults which is observed for a fixed length of time failures are assumed to follow an exponential distribution with an unknown mean and we concentrate on estimation of the number of faults v the likelihood function considered as a function of v has its maximum at one of the boundary points the observed number of faults or infinity with substantial probability likelihood interval estimates and improved point estimates are discussed the harmonic mean of the endpoints of the likelihood interval turns out to be a reasonable point estimate we comment on some extensions of this problem suggested in the software reliability literature

on the computation of π flat outputs for linear time varying differential delay systems abstract we introduce a new definition of flatness for linear differential delay systems with time varying coefficients we characterize and 0 flat outputs and provide an algorithm to efficiently compute such outputs we present an academic example of motion planning to discuss the pertinence of the approach

inference on population size in binomial detectability models many models for biological populations including simple mark recapture models and distance sampling models involve a binomially distributed number n of observations x 1 x n on members of a population of size n two popular estimators of n where is a vector parameter are the maximum likelihood estimator and the conditional maximum likelihood estimator c c based on the conditional distribution of x 1 x n given n we derive the large n asymptotic distributions of and c c and give formulae for the biases of log and log c we show that the difference c is remarkably of order 1 and we give a simple formula for the leading part of this difference simulations indicate that in many cases this formula is very accurate and that confidence intervals based on the asymptotic distribution have excellent coverage an extension to product binomial models is given

efficiency of martingale methods in recapture studies summary the asymptotic variances of various estimators of the population size from a recapture study are calculated the estimation methods compared are maximum likelihood and a class of natural martingale estimators with both time variation and behavioural response it is found that i the martingale methods appropriate for time variation or behavioural response are fully efficient ii there is no loss of efficiency at all in allowing for time variation iii there is large loss of efficiency in allowing for behavioural response iv there is a natural martingale method that allows for both time variation and behavioural response

theoretical comparison of bootstrap confidence intervals we develop a unified framework within which many commonly used bootstrap critical points and confidence intervals may be discussed and compared in all seven different bootstrap methods are examined each being usable in both parametric and nonparametric contexts emphasis is on the way in which the methods cope with first and second order departures from normality percentile t and accelerated bias correction emerge as the most promising of existing techniques certain other methods are shown to lead to serious errors in coverage and position of critical point an alternative approach based on shortest bootstrap confidence intervals is developed we also make several more technical contributions in particular we confirm efron s conjecture that accelerated bias correction is second order correct in a variety of multivariate circumstances and give a simple interpretation of the acceleration constant

a remark on willems trajectory characterization of linear controllability abstract willems trajectory characterization of linear controllability is related to some module theoretic property

obtaining confidence limits on parameters of the jolly seber model for capture recapture data on propose quelques transformations de la methode pour les estimateurs des tailles de la population et des probabilites de survie

stabilizing binomial n estimators abstract the maximum likelihood estimator of the binomial parameter n can be highly unstable resulting in huge fluctuations in the estimate when the data are only slightly perturbed a method of assessing this sensitivity based on perturbing the log likelihood function is proposed and examined

filtering for texture classification a comparative study in this paper we review most major filtering approaches to texture feature extraction and perform a comparative study filtering approaches included are laws masks 1980 ring wedge filters dyadic gabor filter banks wavelet transforms wavelet packets and wavelet frames quadrature mirror filters discrete cosine transform eigenfilters optimized gabor filters linear predictors and optimized finite impulse response filters the features are computed as the local energy of the filter responses the effect of the filtering is highlighted keeping the local energy function and the classification algorithm identical for most approaches for reference comparisons with two classical nonfiltering approaches co occurrence statistical and autoregressive model based features are given we present a ranking of the tested approaches based on extensive experiments

tests point estimations and confidence sets for a capture recapture model abstract a capture recapture model that depends on the catch ability of the individuals in a closed population is considered a test is derived to decide if the capture efficiencies are the same for two capture attempts the test given assumes no a priori information about the population and can be used even if one is dealing with relatively small populations statistical inferences are drawn about the size of the population in particular a family of point estimators for the population size n is investigated and compared with the maximum likelihood estimator further confidence intervals for the capture efficiency e are combined with joint confidence regions for n and e to obtain confidence intervals for n it is found that this procedure yields confidence intervals for n whose actual confidence levels are not strongly dependent on the nuisance parameter e for n 200 the procedure given for finding confidence intervals for n is found to be more satisfactory than the standard approach of simply

quantifying precision of mark recapture estimates using the bootstrap and related methods summary bootstrap techniques yield variance estimates under any model for which parameter estimates can be calculated and are useful in cases where analytic variances are not available in closed form or are available only if more restrictive assumptions are made here the application of bootstrap techniques to mark recapture models is discussed the approach also allows generation of robust confidence intervals which extend beyond the permissible parameter range only if the mark recapture model itself allows out of range parameter estimates if an animal population is assumed to be closed i e no death birth or migration two further methods of obtaining confidence limits for population size are suggested the first is based on a robbins monro search for each limit and the second applies the concept of a randomisation or permutation test in the absence of nuisance parameters both methods are exact apart from monte carlo variation and the limitations imposed by a discrete distribution for the second if all possible permutations are enumerated monte carlo variation is eliminated

quantifying the effects of unequal catchabilities on jolly seber estimators via sample coverage using the concept of sample coverage we derive an approximation of the bias in the jolly seber population size estimators due to heterogeneity of capture probabilities the resulting bias is expressed as a function of sample coverage average capture probability and the coefficient of variation of individual capture probabilities the effect of unequal catchabilities on the jolly seber survival rate estimators is also briefly discussed we propose new population size estimators which incorporate the heterogeneity of capture probabilities a simulation study investigates the performance of the approximation formulas of biases and the proposed estimation procedure a real data set of male black kneed capsids discussed in jolly biometrika 52 225 247 1965 seber the estimation of animal abundance 1982 and burnham in estimation of analysis of insect populations pp 416 435 1989 illustrates the method

errors in inspection integer parameter maximum likelihood in a finite population abstract in a probability model for possible errors in the inspection of a finite lot containing defective items it is shown how maximum likelihood estimation is affected by whether the sampling is carried out with or without replacement these changes are examined by developing a method for simultaneous maximization over an integer and a scalar parameter a new method for the bounding of the integer solution is introduced the results show that the parameter estimates in the inspection model are generally stable under wide changes in the sampling framework conditions for instability of the solution are identified some apparently new relations for discrete distributions are derived in the process of the investigation

objective priors for discrete parameter spaces this article considers the development of objective prior distributions for discrete parameter spaces formal approaches to such development such as the reference prior approach often result in a constant prior for a discrete parameter which is questionable for problems that exhibit certain types of structure to take advantage of structure this article proposes embedding the original problem in a continuous problem that preserves the structure and then using standard reference prior theory to determine the appropriate objective prior four different possibilities for this embedding are explored and applied to a population size model the hypergeometric distribution the multivariate hypergeometric distribution the binomial beta distribution and the binomial distribution the recommended objective priors for the first third and fourth problems are new

contextual performance prediction for low level image analysis algorithms this paper explores a generic approach to predict the output accuracy of an algorithm without running it by a careful examination of the local context such a performance prediction will allow one to qualify the appropriateness of an algorithm to treat images with given properties contrast resolution noise richness in details contours or textures etc resulting either from experimental acquisition conditions or from a specific type of scene we have to answer the following question a context c being given at any site what will be the performance in our experiments c is described by three contextual variables gabor components entropy and signal noise ratio as initially proposed in the related work of chalmond and graffigne 1999 the prediction function is determined from training using a logistic regression model this technique is illustrated on aerial infrared images for two types of algorithm edge detection and displacement estimation

some practical aspects of a conditional likelihood approach to capture experiments the use of conditional likelihood methods in the analysis of capture data allows the modeling of capture probabilities in terms of observable characteristics of the captured individuals and the trapping occasions the resulting models may then be used to estimate the size of the population here the use of conditional likelihood procedures to construct models for capture probabilities is discussed and illustrated by an example

a lie backlund approach to equivalence and flatness of nonlinear systems a new system equivalence relation using the framework of differential geometry of jets and prolongations of infinite order is studied in this setting two systems are said to be equivalent if any variable of one system may be expressed as a function of the variables of the other system and of a finite number of their time derivatives this is a lie backlund isomorphism the authors prove that although the state dimension is not preserved the number of input channels is kept fixed they also prove that a lie backlund isomorphism can be realized by an endogenous feedback the differentially flat nonlinear systems introduced by the authors 1992 via differential algebraic techniques are generalized and the new notion of orbitally flat systems is defined they correspond to systems which are equivalent to a trivial one with time preservation or not the endogenous linearizing feedback is explicitly computed in the case of the vtol aircraft to track given reference trajectories with stability