recovering periodic impulsive signals through skewness maximization maximizing the skewness of a measured signal by adaptive filtering to reveal hidden periodic impulses is proposed as a preprocessing method periodic impulsive signals are modeled by harmonically related sinusoids to prove that amplitude and phase distortion from a transfer function effects of sinusoidal interferences and noise can be compensated for by a linear filter the convergence behavior of the skewness maximization algorithm is analyzed to show that it is possible to recover the original harmonic structure with an unknown fundamental frequency by achieving maximum skewness in the given signal it is shown that maximizing the skewness always results in a subspace containing only a single harmonic family defect detection in rolling element bearings is presented as an application example and as a comparative study against kurtosis maximization

a new approach to multipath correction of constant modulus signals an adaptive digital filtering algorithm that can compensate for both frequency selective multipath and interference on constant envelope modulated signals is presented the method exploits the fact that multipath reception and various interference sources generate incidental amplitude modulation on the received signal a class of so called constant modulus performance functions is developed which sense this am term but are insensitive to the angle modulation simple adaptive algorithms for finite impulse response fir digital filters are developed which employ a gradient search of the performance function one of the resulting algorithms is simulated for the example of an fm signal degraded by specular multipath propagation substantial improvements in noise power ratio npr are observed e g 25 db with moderately rapid convergence time these results are then extended to include tonal interference on a fm signal and intersymbol interference on a qpsk data signal

regression models for relative survival four approaches to estimating a regression model for relative survival using the method of maximum likelihood are described and compared the underlying model is an additive hazards model where the total hazard is written as the sum of the known baseline hazard and the excess hazard associated with a diagnosis of cancer the excess hazards are assumed to be constant within pre specified bands of follow up the likelihood can be maximized directly or in the framework of generalized linear models minor differences exist due to for example the way the data are presented individual aggregated or grouped and in some assumptions e g distributional assumptions the four approaches are applied to two real data sets and produce very similar estimates even when the assumption of proportional excess hazards is violated the choice of approach to use in practice can therefore be guided by ease of use and availability of software we recommend using a generalized linear model with a poisson error structure based on collapsed data using exact survival times the model can be estimated in any software package that estimates glms with user defined link functions including sas stata s plus and r and utilizes the theory of generalized linear models for assessing goodness of fit and studying regression diagnostics

splines in statistics abstract this is a survey article that attempts to synthesize a broad variety of work on splines in statistics splines are presented as a nonparametric function estimating technique after a general introduction to the theory of interpolating and smoothing splines splines are treated in the nonparametric regression setting the method of cross validation for choosing the smoothing parameter is discussed and the general multivariate regression surface estimation problem is addressed an extensive discussion of splines as nonparametric density estimators is followed by a discussion of their role in time series analysis a comparison of the spline and isotonic regression methodologies leads to a formulation of a hybrid estimator the closing section provides a brief overall summary and formulates a number of open unsolved problems relating to splines in statistics

assessing time by covariate interactions in proportional hazards regression models using cubic spline functions proportional hazards or cox regression is a popular method for modelling the effects of prognostic factors on survival use of cubic spline functions to model time by covariate interactions in cox regression allows investigation of the shape of a possible covariate time dependence without having to specify a specific functional form cubic spline functions allow one to graph such time by covariate interactions to test formally for the proportional hazards assumption and also to test for non linearity of the time by covariate interaction the functions can be fitted with existing software using relatively few parameters the regression coefficients are estimated using standard maximum likelihood methods

an overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death we propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors the baseline hazard was modelled until 10 years follow up using parametric continuous functions six models including cubic regression splines were considered and the akaike information criterion was used to select the final model this approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information splines were also used to model simultaneously non linear effects of continuous covariates and time dependent hazard ratios this led to a graphical representation of the hazard ratio that can be useful for clinical interpretation estimates of these models were obtained by likelihood maximization we showed that these estimates could be also obtained using standard algorithms for poisson regression

a measure of the tracking capability of recursive stochastic algorithms with constant gains a criterion is given for measuring the tracking capability of recursive algorithms when applied to slowly time varying systems the optimal gain for a given disturbance is also calculated this criterion is seen to have some connection with the fisher information matrix and allows us to select a priori the best algorithm for identifying a given unknown parameter which may be subject to smooth unknown disturbances examples of applications are given in the areas of identification theory and data communication theory

a relative survival regression model using b spline functions to model non proportional hazards relative survival a method for assessing prognostic factors for disease specific mortality in unselected populations is frequently used in population based studies however most relative survival models assume that the effects of covariates on disease specific mortality conform with the proportional hazards hypothesis which may not hold in some long term studies to accommodate variation over time of a predictor s effect on disease specific mortality we developed a new relative survival regression model using b splines to model the hazard ratio as a flexible function of time without having to specify a particular functional form our method also allows for testing the hypotheses of hazards proportionality and no association on disease specific hazard accuracy of estimation and inference were evaluated in simulations the method is illustrated by an analysis of a population based study of colon cancer

a feedback approach to the steady state performance of fractionally spaced blind adaptive equalizers this paper proposes a new approach to the analysis of the steady state performance of constant modulus algorithms cma which are among the most popular adaptive schemes for blind equalization a major feature of the proposed feedback approach is that it bypasses the need for working directly with the weight error covariance matrix in so doing approximate expressions for the steady state mean square error of several cm algorithms are derived including cma2 2 cma1 2 normalized cma and a new normalized cma variant with less bias a comparison among the various algorithms is also performed along with several simulation results the conclusions confirm the superior performance of cma2 2

a simple error estimator and adaptive procedure for practical engineerng analysis a new error estimator is presented which is not only reasonably accurate but whose evaluation is computationally so simple that it can be readily implemented in existing finite element codes the estimator allows the global energy norm error to be well estmated and alos gives a good evaluation of local errors it can thus be combined with a full adaptive process of refinement or more simply provide guidance for mesh redesign which allows the user to obtain a desired accuracy with one or two trials when combined with an automatic mesh generator a very efficient guidance process to analysis is avaiable estimates other than the energy norm have successfully been applied giving for instance a predetermined accuracy of stresses

wavelet deconvolution with noisy eigenvalues over the last decade there has been a lot of interest in wavelet vaguelette methods for the recovery of noisy signals or images in motion blur nonlinear wavelet estimators are known to have good adaptive properties and to outperform linear approximations over a wide range of signals and images see e g the recent waved method of johnstone kerkyacharian picard and raimondo 2004 or the forward method of neelamani choi and baraniuk 2004 and also fan and koo 2002 in the density setting in the deblurring setting wavelet vaguelette methods rely on the complete knowledge of a convolution operator s eigenvalues this is an unlikely situation in practice however a more realistic scenario such as would arise when passing the fourier basis as an input signal through a linear time invariant system is to imagine that one also observes a set of noisy eigenvalues in this paper we define a version of the waved estimator which is near optimal when used with noisy eigenvalues a key feature of our method includes a data driven method for choosing the fine resolution level in waved estimation asymptotic theory is illustrated with a wide range of finite sample examples

identification of linear stochastic systems via second and fourth order cumulant matching the identification problem for time invariant single input single output linear stochastic systems driven by non gaussian white noise is considered the system is not restricted to be minimum phase and it is allowed to contain all pass components a least squares criterion that involves matching the second and the fourth order cumulant functions of the noisy observations is proposed knowledge of the probability distribution of the driving noise is not required an order determination criterion that is a modification of the akaike information criterion is also proposed strong consistency of the proposed estimator is proved under certain sufficient conditions simulation results are presented to illustrate the method

a new error estimation approach based on fuzzy logic system for h r adaptive boundary element method abstract conventional adaptive boundary element method cannot be universally applied to solve many more problems than the subject it discussed and different error estimation formulas need to be designed for varied problems this paper put forward a new error analysis method based on the fuzzy logic system which is able to make error estimation effectively using human expert experience and solve the two classical elasticity problems in conjunction with the h r adaptive boundary element method numerical examples have illustrated the effectiveness superiority and potential of a fuzzy logic approach in the adaptive boundary element method

convergence analysis of self adaptive equalizers a theoretical analysis of self adaptive equalization for data transmission is carried out starting from known convergence results for the corresponding trained adaptive filter the development relies on a suitable ergodicity model for the sequence of observations at the output of the transmission channel thanks to the boundedness of the decision function used for data recovery it can be proved that the algorithm is bounded strong convergence results can be reached when a perfect noiseless equalizer exists the algorithm will converge to it if the eye pattern is initially open otherwise convergence may take place towards certain other stationary points of the algorithm for which domains of attraction have been defined some of them will result in a poor error rate the case of a noisy channel exhibits limit points for the algorithm that differ from those of the classical trained algorithm the stronger the noise the greater the difference is one of the principal results of this study is the proof of the stability of the usual decision feedback algorithms once the learning period is over

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

improving the tracking capability of adaptive filters via convex combination as is well known hessian based adaptive filters such as the recursive least squares algorithm rls for supervised adaptive filtering or the shalvi weinstein algorithm swa for blind equalization converge much faster than gradient based algorithms such as the least mean squares algorithm lms or the constant modulus algorithm cma however when the problem is tracking a time variant filter the issue is not so clear cut there are environments for which each family presents better performance given this we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability we show the potential of this combination and provide a unified theoretical model for the steady state excess mean square error for convex combinations of gradient and hessian based algorithms assuming a random walk model for the parameter variations the proposed model is valid for algorithms of the same or different families and for supervised lms and rls or blind cma and swa algorithms

goodness of fit of relative survival models additive regression models are preferred over multiplicative models in the analysis of relative survival data such preferences are mainly grounded in practical experience with mostly cancer registries data where the basic assumption of the additivity of hazards is more likely to be met also the interpretation of coefficients is more meaningful in additive than in multiplicative models nonetheless the question of goodness of fit of the assumed model must still be addressed and while there is an abundance of methods to check the goodness of fit of multiplicative models the respective arsenal for additive models is almost empty we propose here a variety of procedures for testing the null hypothesis of a good fit these are based on partial residuals defined similarly to schoenfeld residuals familiar for cox model diagnostics the tests have appropriate sizes under the null hypothesis and good power under different alternatives we investigate their performance through simulations and apply the methods to data from a study into survival of colon cancer patients

flexible modeling of the effects of continuous prognostic factors in relative survival relative survival methods permit separating the effects of prognostic factors on disease related excess mortality from their effects on other causes natural mortality even when individual causes of death are unknown as in conventional crude survival accurate assessment of prognostic factors requires testing and possibly modeling of non proportional effects and for continuous covariates of non linear relationships with the hazard we propose a flexible extension of the additive hazards relative survival model in which the observed all causes mortality hazard is represented by a sum of disease related excess and natural mortality hazards in our flexible model the three functions representing i the baseline hazard for excess mortality ii the time dependent effects and iii for continuous covariates non linear effects on the logarithm of this hazard are all modeled by low dimension cubic regression splines non parametric likelihood ratio tests are proposed to test the time dependent and non linear effects the accuracy of the estimated functions is evaluated in multivariable simulations to illustrate the new insights offered by the proposed model we apply it to re assess the effects of patient age and of secular trends on disease related mortality in colon cancer

a self adaptive co ordinate transformation for efficient numerical evaluation of general boundary element integrals almost all general purpose boundary element computer packages include a curved geometry modelling capability thus numerical quadrature schemes play an important role in the efficiency of programming the technique the present work discusses this problem in detail and introduces efficient means of computing singular or nearly singular integrals currently found in two dimensional axisymmetric and three dimensional applications emphasis is given to a new third degree polynomial transformation which was found greatly to improve the accuracy of gaussian quadrature scheme s within the near singularity range the procedure can easily be implemented into existing be codes and presents the important feature of being self adaptive i e it produces a variable lumping of the gauss stations toward the singularity depending on the minimum distance from the source point to the element the self adaptiveness of the scheme also makes it inactive when not useful large source distances which makes it very safe for general usage

proportional excess hazards assume that the excess risk of an individual over a known background rate can be well approximated by the proportional hazards model such might be the case for a cohort at risk of dying from causes that are independent of those that affect the general population we propose estimators for both the regression parameters and the nonparametric baseline hazard function the estimators are shown to be consistent asymptotically normal and efficient analogues of the wald score and likelihood ratio test are provided the relevance of the asymptotic results to samples of sizes 500 and 100 is illustrated by simulation the model is used to estimate the prognostic significance of stage and histology in adults with non hodgkin s lymphoma

binomial regression with monotone splines a psychometric application abstract a binomial regression function p x models the probability of rj successes in nj trials as a function of the values of an observed covariate xj and or a latent variable j j 1 j this article explores the use of monotone regression splines to define p and applies them to the representation of test items as functions of examinee ability some illustrative data suggest that the flexibility of monotone splines permits the detection of item characteristics not observable using logistic based or log linear approaches a simulation study indicates that estimates of both item characteristic curves and ability are reasonably precise for numbers of items and examinees typical of large university lectures given a set of such binomial regression functions it can be useful to study the principal components of functional variation the extension of multivariate principal components analysis to permit the analysis of many item characteristic curves is described

bussgang blind deconvolution for impulsive signals many blind deconvolution algorithms have been designed to extract digital communications signals corrupted by intersymbol interference isi such algorithms generally fail when applied to signals with impulsive characteristics such as acoustic signals while it is possible to stabilize such procedures in many cases by imposing unit norm constraints on the adaptive equalizer coefficient vector these modifications require costly divide and square root operations in this paper we provide a theoretical analysis and explanation as to why unconstrained bussgang type algorithms are generally unsuitable for deconvolving impulsive signals we then propose a novel modification of one such algorithm the sato algorithm to enable it to deconvolve such signals our approach maintains the algorithmic simplicity of the sato algorithm requiring only additional multiplies and adds to implement sufficient conditions on the source signal distribution to guarantee local stability of the modified sato algorithm about a deconvolving solution are derived computer simulations show the efficiency of the proposed approach as compared with various constrained and unconstrained blind deconvolution algorithms when deconvolving impulsive signals

low complexity data reusing methods in adaptive filtering most adaptive filtering algorithms couple performance with complexity over the last 15 years a class of algorithms termed affine projection algorithms have given system designers the capability to tradeoff performance with complexity by changing parameters and the size scale of data used to update the coefficients of an adaptive filter but without fundamentally changing the algorithm structure a system designer can radically change the performance of the adaptive algorithm this paper discusses low complexity data reusing algorithms that are closely related to affine projection algorithms this paper presents various low complexity and highly flexible schemes for improving convergence rates of adaptive algorithms that utilize data reusing strategies all of these schemes are unified by a row projection framework in existence for more than 65 years this framework leads to the classification of all data reusing and affine projection methods for adaptive filtering into two categories the kaczmarz and cimmino methods simulation and convergence analysis results are presented for these methods under a number of conditions they are compared in terms of convergence rate performance and computational complexity

normalized adaptive channel equalizer based on minimal symbol error rate existing minimum symbol error rate equalizers were derived based on the symbol error rate objective function due to the complexity of the objective function the derivation is not straightforward in this paper we present a new approach to derive the minimum symbol error rate adaptive equalizers the problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol error rate minimization the constrained optimization problem then is solved with the lagrange multiplier method which results in an adaptive algorithm with normalization simulation results show that the proposed algorithm outperforms the existing adaptive minimum symbol error rate equalizer in convergence speed and steady state performance

solution of elasticity problems by a self adaptive mesh refinement technique for boundary element computation a self adaptive mesh refinement technique is developed for boundary element solutions of the two dimensional navier s equation the method is based on error reduction and applied on the element and global level to estimate the residual error associated with each mesh results for several problems employing constant two dimensional isotropic elastostatic boundary elements are presented

flexible estimation of survival curves conditional on non linear and time dependent predictor effects prognostic studies often estimate survival curves for patients with different covariate vectors but the validity of their results depends largely on the accuracy of the estimated covariate effects to avoid conventional proportional hazards and linearity assumptions flexible extensions of cox s proportional hazards model incorporate non linear nl and or time dependent td covariate effects however their impact on survival curves estimation is unclear our primary goal is to develop and validate a flexible method for estimating individual patients survival curves conditional on multiple predictors with possibly nl and or td effects we first obtain maximum partial likelihood estimates of nl and td effects and use backward elimination to select statistically significant effects into a final multivariable model we then plug the selected nl and td estimates in the full likelihood function and estimate the baseline hazard function and the resulting survival curves conditional on individual covariate vectors the td and nl functions and the log hazard are modeled with unpenalized regression b splines in simulations our flexible survival curve estimates were unbiased and had much lower mean square errors than the conventional estimates in real life analyses of mortality after a septic shock our model improved significantly the deviance likelihood ratio test 84 8 df 20 p 0 0001 and changed substantially the predicted survival for several subjects

avoiding divergence in the shalvi weinstein algorithm the most popular algorithms for blind equalization are the constant modulus algorithm cma and the shalvi weinstein algorithm swa it is well known that swa presents a higher convergence rate than cma at the expense of higher computational complexity if the forgetting factor is not sufficiently close to one if the initialization is distant from the optimal solution or if the signal to noise ratio is low swa can converge to undesirable local minima or even diverge in this paper we show that divergence can be caused by an inconsistency in the nonlinear estimate of the transmitted signal or when the algorithm is implemented in finite precision by the loss of positiveness of the estimate of the autocorrelation matrix or by a combination of both in order to avoid the first cause of divergence we propose a dual mode swa in the first mode of operation the new algorithm works as swa in the second mode it rejects inconsistent estimates of the transmitted signal assuming the persistence of excitation condition we present a deterministic stability analysis of the new algorithm to avoid the second cause of divergence we propose a dual mode lattice swa which is stable even in finite precision arithmetic and has a computational complexity that increases linearly with the number of adjustable equalizer coefficients the good performance of the proposed algorithms is confirmed through numerical simulations

fast newton transversal filters a new class of adaptive estimation algorithms a class of adaptive algorithms for the estimation of fir finite impulse response transversal filters is presented the main characteristic of this class is the fast computation of the gain vector needed for the adaptation of the transversal filters the method for deriving these algorithms is based on the assumption that the input signal is autoregressive of order m where m can be much smaller than the order of the filter to be estimated under this assumption the covariance matrix of the input signal is estimated by extending in a min max way the m order sample covariance matrix this estimate can be regarded as a generalization of the diagonal covariance matrix used in lms and leads to an efficient computation of the gain needed for the adaptation the new class of algorithms contains the lms and the fast versions of ls as special cases the complexity changes linearly with m starting from the complexity of the lms for m 0 and ending at the complexity of the fast versions of ls

new approaches for error estimation and adaptivity for 2d potential boundary element methods this work presents two new error estimation approaches for the bem applied to 2d potential problems the first approach involves a local error estimator based on a gradient recovery procedure in which the error function is generated from differences between smoothed and non smoothed rates of change of boundary variables in the local tangential direction the second approach involves the external problem formulation and gives both local and global measures of error depending on a choice of the external evaluation point these approaches are post processing procedures both estimators show consistency with mesh refinement and give similar qualitative results the error estimator using the gradient recovery approach is more general as this formulation does not rely on an optimal choice of an external parameter this work presents also the use of a local error estimator in an adaptive mesh refinement procedure this r refinement approach is based on the minimization of the standard deviation of the local error estimate a non linear programming procedure using a feasible point method is employed using lagrange multipliers and a set of active constraints the optimization procedure produces finer meshes close to a singularity and results that are consistent with the problem physics copyright 2002 john wiley sons ltd

the least mean fourth lmf adaptive algorithm and its family new steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth etc sense during adaptation the weights undergo exponential relaxation toward their optimal solutions time constants have been derived and surprisingly they turn out to be proportional to the time constants that would have been obtained if the steepest descent least mean square lms algorithm of widrow and hoff had been used the new gradient algorithms are insignificantly more complicated to program and to compute than the lms algorithm their general form is w j 1 w j 2 mu k epsilon j 2k 1 x j where w j is the present weight vector w j 1 is the next weight vector epsilon j is the present error x j is the present input vector mu is a constant controlling stability and rate of convergence and 2k is the exponent of the error being minimized conditions have been derived for weight vector convergence of the mean and of the variance for the new gradient algorithms the behavior of the least mean fourth lmf algorithm is of special interest in comparing this algorithm to the lms algorithm when both are set to have exactly the same time constants for the weight relaxation process the lmf algorithm under some circumstances will have a substantially lower weight noise than the lms algorithm it is possible therefore that a minimum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm this intriguing concept has implications for all forms of adaptive algorithms whether they are based on steepest descent or otherwise

joint estimation of time dependent and non linear effects of continuous covariates on survival in order to yield more flexible models the cox regression model lambda t x lambda 0 t exp betax has been generalized using different non parametric model estimation techniques one generalization is the relaxation of log linearity in x lambda t x lambda 0 t exp r x another is the relaxation of the proportional hazards assumption lambda t x lambda 0 t exp beta t x these generalizations are typically considered independently of each other we propose the product model lambda t x lambda 0 t exp beta t r x which allows for joint estimation of both effects and investigate its properties the functions describing the time dependent beta t and non linear r x effects are modelled simultaneously using regression splines and estimated by maximum partial likelihood likelihood ratio tests are proposed to compare alternative models simulations indicate that both the recovery of the shapes of the two functions and the size of the tests are reasonably accurate provided they are based on the correct model by contrast type i error rates may be highly inflated and the estimates considerably biased if the model is misspecified applications in cancer epidemiology illustrate how the product model may yield new insights about the role of prognostic factors

dynamic regression hazards models for relative survival a natural way of modelling relative survival through regression analysis is to assume an additive form between the expected population hazard and the excess hazard due to the presence of an additional cause of mortality within this context the existing approaches in the parametric semiparametric and non parametric setting are compared and discussed we study the additive excess hazards models where the excess hazard is on additive form this makes it possible to assess the importance of time varying effects for regression models in the relative survival framework we show how recent developments can be used to make inferential statements about the non parametric version of the model this makes it possible to test the key hypothesis that an excess risk effect is time varying in contrast to being constant over time in case some covariate effects are constant we show how the semiparametric additive risk model can be considered in the excess risk setting providing a better and more useful summary of the data estimators have explicit form and inference based on a resampling scheme is presented for both the non parametric and semiparametric models we also describe a new suggestion for goodness of fit of relative survival models which consists on statistical and graphical tests based on cumulative martingale residuals this is illustrated on the semiparametric model with proportional excess hazards we analyze data from the trace study using different approaches and show the need for more flexible models in relative survival

a model combining excess and relative mortality for population based studies considering expected mortality provides an attractive approach to analyse mortality of population based cohorts of patients presenting with a chronic disease two classes of methods are available either modelling the excess mortality using an additive hazard model or modelling the relative mortality using a multiplicative hazard model because these two models are informative to look for factors associated with mortality related to a chronic disease we developed an alternative model modelling both the excess and the relative mortality we generalised andersen and vaeth s model to fit covariates and obtain directly an estimation of the excess mortality ratio and relative mortality ratio for each covariate we assessed the performances of the combined model by using simulations and it appeared satisfactorily we illustrate the combined model by data collected in patients presenting with end stage renal disease and treated by dialysis the combined model offers the possibility of performing pure additive and multiplicative models and thus to compare their log likelihoods the combined model appears useful to select one of these pure models or to conclude to the need of modelling both excess and relative mortality in this latter case our model enabled better describing the effect of covariates on the excess and relative mortality

normalized sliding window constant modulus and decision directed algorithms a link between blind equalization and classical adaptive filtering by minimizing a deterministic criterion of the constant modulus cm type or of the decision directed dd type we derive normalized stochastic gradient algorithms for blind linear equalization be of qam systems these algorithms allow us to formulate cm and dd separation principles which help obtain a whole family of cm or dd be algorithms from classical adaptive filtering algorithms we focus on the algorithms obtained by using the affine projection adaptive filtering algorithm apa their increased convergence speed and ability to escape from local minima of their cost function make these algorithms very promising for be applications

multicategorical spline model for item response theory additional information contained in incorrect responses calls for a multicategorical rather than a binary analysis of multiple choice data a nonparametric divided by total model for joint maximum likelihood estimation of probability of choice functions for particular responses and of latent ability is proposed the model approximates probability functions by rational splines some illustrative examples of real test data analysis and the results of a monte carlo study are presented

error indicators for adaptive mesh refinement in the boundary element method a new approach in this paper a new approach to the development of a posteriori error indicators for the direct boundary element method bem is presented it takes advantage of a unique opportunity offered by the collocation bem that is the possibility of obtaining more than one numerical solution from a given boundary element mesh unlike other approaches the error indicator introduced in the present paper does not require any explicit evaluation of the residual of the approximate solution in order to test the capability of the proposed error indicators to actually control a selective mesh refinement process an h adaptive refinement strategy is also described together with the criteria for refinement and for the termination of the adaptive process full details on the whole numerical implementation for two dimensional problems are provided in particular there is described a direct method for the numerical evaluation of cauchy principal value integrals numerical examples show that pointwise convergence is always obtained in a few steps and the final meshes look nearly as if they were designed by an expert analyst

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

a set of algorithms linking nlms and block rls algorithms this paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares nlms and the block recursive least squares brls algorithms all these algorithms use small block lengths thus allowing easy implementation and small input output delay it is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares lms algorithm while converging much faster a precise evaluation of the arithmetic complexity is provided and the adaptive behavior of the algorithm is analyzed simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the nlms algorithm the conclusions of the theoretical analysis are checked by simulations illustrating that even in the case where noise is added to the reference signal the proposed algorithm allows altogether a faster convergence and a lower residual error than the nlms algorithm finally a sample by sample version of this algorithm is outlined which is the link between the nlms and recursive least squares rls algorithms

a new h adaptive refinement scheme for the boundary element method using local reanalysis abstract this paper presents a new way of estimating the discretization error in the boundary element method bem based on the concept of local reanalysis lr the principle of lr is implemented for the particular case of two dimensional potential problems in an attempt to find a technique to calculate a cheaply computable predicted solution to use in the evaluation of the quality of the boundary element results the error estimators are defined from the difference between the bem and lr solutions which measures the sensitivity of the boundary element results to a local change in the mesh an h adaptive method using lr is developed and different numerical experiments are carried out to test its performance

adaptive boundary element methods for strongly elliptic integral equations integral operators are nonlocal operators the operators defined in boundary integral equations to elliptic boundary value problems however are pseudo differential operators on the boundary and therefore provide additional pseudolocal properties these allow the successful application of adaptive procedures to some boundary element methods in this paper we analyze these methods for general strongly elliptic integral equations and obtain a posteriori error estimates for boundary element solutions we also apply these methods to nodal collocation with odd degree splines some numerical examples show that these adaptive procedures are reliable and effective