on global small amplitude solutions to systems of cubic nonlinear klein gordon equations with different mass terms in one space dimension abstract we consider the cauchy problem for systems of cubic nonlinear klein gordon equations with different mass terms in one space dimension we prove some result concerning the global existence of small amplitude solutions and their asymptotic behavior as a consequence we see that the condition for small data global existence is actually influenced by the difference of masses in some cases

a general class of pattern mixture models for nonignorable dropout with many possible dropout times in this article we consider the problem of fitting pattern mixture models to longitudinal data when there are many unique dropout times we propose a marginally specified latent class pattern mixture model the marginal mean is assumed to follow a generalized linear model whereas the mean conditional on the latent class and random effects is specified separately because the dimension of the parameter vector of interest the marginal regression coefficients does not depend on the assumed number of latent classes we propose to treat the number of latent classes as a random variable we specify a prior distribution for the number of classes and calculate approximate posterior model probabilities in order to avoid the complications with implementing a fully bayesian model we propose a simple approximation to these posterior probabilities the ideas are illustrated using data from a longitudinal study of depression in hiv infected women

an alternative parameterization of the general linear mixture model for longitudinal data with non ignorable drop outs this paper considers the mixture model methodology for handling non ignorable drop outs in longitudinal studies with continuous outcomes recently hogan and laird have developed a mixture model for non ignorable drop outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop out that is the mean response is linear in time other covariates and drop out time and their interactions one of the key attractions of the mixture modelling approach to drop outs is that it is relatively easy to explore the sensitivity of results to model specification however the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available but require marginalization of the distribution of outcome over drop out times furthermore although a linear model is assumed for the conditional mean of the outcome vector given time of drop out after marginalization the unconditional mean of the outcome vector is not in general linear in the regression parameters as a result it is not possible to parsimoniously describe the effects of covariates on the marginal distribution of the outcome in terms of regression coefficients the need to explicitly average over the distribution of the drop out times and the absence of regression coefficients that describe the effects of covariates on the outcome are two unappealing features of the mixture modelling approach in this paper we describe a particular parameterization of the general linear mixture model that circumvents both of these problems

definition and extraction of stable points from fingerprint images this paper introduces a new idea to define and extract singular points sps including cores and deltas from fingerprint images by a zone could be in theorem that describes harmonic relationships of a sp and its neighbors according to the theorem we can view sps as stable points attractors and rejectors by their rotations estimate core s direction and restore sp offset the proposed method is validated in the following two hierarchical aspects one is to test the reliability and accuracy of coarse sp detection by means of two indices ri and ac and the other is the accuracy of core s location and the stability of core s direction in terms of improved fingercode matching experiments on partial fvc2004 data sets show that it works well and outperforms three other methods

fingerprint pattern classification abstract this paper presents a new method for fingerprint classification in this method fingerprint images are divided into 32 32 subregions to obtain direction pattern next the relaxation smoothing process with singularity detection and convergency checking is performed starting from the singular regions found feature parameters of the fingerprint are obtained by extracting major flow line traces the result of the experiments shows that this approach is capable of classifying fingerprint patterns into more than ten categories

matching conditional and marginal shapes in binary random intercept models using a bridge distribution function summary random effects logistic regression models are often used to model clustered binary response data regression parameters in these models have a conditional subject specific interpretation in that they quantify regression effects for each cluster very often the logistic functional shape conditional on the random effects does not carry over to the marginal scale thus parameters in these models usually do not have an explicit marginal population averaged interpretation we study a bridge distribution function for the random effect in the random intercept logistic regression model under this distributional assumption the marginal functional shape is still of logistic form and thus regression parameters have an explicit marginal interpretation the main advantage of this approach is that likelihood inference can be obtained for either marginal or conditional regression inference within a single model framework the generality of the results and some properties of the bridge distribution functions are discussed an example is used for illustration

pixel level singular point detection from multi scale gaussian filtered orientation field singular point as a global feature plays an important role in fingerprint recognition inconsistent detection of singular points apparently gives an affect to fingerprint alignment classification and verification accuracy this paper proposes a novel approach to pixel level singular point detection from the orientation field obtained by multi scale gaussian filters initially a robust pixel level orientation field is estimated by a multi scale averaging framework then candidate singular points in pixel level are extracted from the complex angular gradient plane derived directly from the pixel level orientation field the candidate singular points are finally validated via a cascade framework comprised of nested poincare indices and local feature based classification experimental results over the fvc 2000 db2 confirm that the proposed method achieves robust and accurate orientation field estimation and consistent pixel level singular point detection the experimental results exhibit a low computational cost with better performance thus the proposed method can be employed in real time fingerprint recognition

systematic methods for the computation of the directional fields and singular points of fingerprints the first subject of the paper is the estimation of a high resolution directional field of fingerprints traditional methods are discussed and a method based on principal component analysis is proposed the method not only computes the direction in any pixel location but its coherence as well it is proven that this method provides exactly the same results as the averaged square gradient method that is known from literature undoubtedly the existence of a completely different equivalent solution increases the insight into the problem s nature the second subject of the paper is singular point detection a very efficient algorithm is proposed that extracts singular points from the high resolution directional field the algorithm is based on the poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points furthermore a method is presented to estimate the orientation of the extracted singular points the accuracy of the methods is illustrated by experiments on a live scanned fingerprint database

marginally specified logistic normal models for longitudinal binary data summary likelihood based inference for longitudinal binary data can be obtained using a generalized linear mixed model breslow n and clayton d g 1993 journal of the american statistical association88 9 25 wolfinger r and o connell m 1993 journal of statistical computation and simulation48 233 243 given the recent improvements in computational approaches alternatively fitzmaurice and laird 1993 biometrika80 141 151 molenberghs and lesaffre 1994 journal of the american statistical association89 633 644 and heagerty and zeger 1996 journal of the american statistical association91 1024 1036 have developed a likelihood based inference that adopts a marginal mean regression parameter and completes full specification of the joint multivariate distribution through either canonical and or marginal higher moment assumptions each of these marginal approaches is computationally intense and currently limited to small cluster sizes in this manuscript an alternative parameterization of the logistic normal random effects model is adopted and both likelihood and estimating equation approaches to parameter estimation are studied a key feature of the proposed approach is that marginal regression parameters are adopted that still permit individual level predictions or contrasts an example is presented where scientific interest is in both the mean response and the covariance among repeated measurements

smoothing spline based score tests for proportional hazards models we propose score type tests for the proportional hazards assumption and for covariate effects in the cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate the tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero we show that the tests have a size close to the nominal level and good power against general alternatives and we apply them to data from a cancer clinical trial

a nonlinear orientation model for global description of fingerprints a nonlinear orientation model for the representation of the orientation matrix of a fingerprint image is presented a general procedure to obtain the set of parameters describing this model is shown particularly a piecewise linear orientation function is analysed the model compared with a previous zero pole model shows substantial decrease in the orientation error the application of this model for efficient identity verification based on the ridge orientation is explored

a system of quadratic nonlinear klein gordon equations in 2d abstract we consider the system of quadratic nonlinear klein gordon equations t 2 v 1 v 1 m 1 2 v 1 v 1 v 2 t 2 v 2 v 2 m 2 2 v 2 v 1 2 in two space dimensions under the mass condition such that 2 m 1 m 2 we construct the scattering operator in an almost natural weighted sobolev class h 1 1 or the scattering problem in lower order sobolev class h 4 3 1 h 1 where 0 is small

on the spatial distribution of fingerprint singularities fingerprint singularities play an important role in several fingerprint recognition and classification systems although some general relationships and constraints about the location of singularities in the different fingerprint classes are well known to the best of our knowledge no statistical models have been developed until now this paper studies the spatial distributions of singularity locations in nature and derives from a representative dataset of labelled samples the probability density functions of the four main fingerprint classes the results obtained can be directly exploited to improve the accuracy of many techniques relying on the position of singularities as confirmed by the results of two experiments on fingerprint classification and synthesis

detection of singular points in fingerprint images abstract this paper describes a technique for the detection of singular points in a fingerprint image the singular points namely the core and the delta points are used as points of registration in fingerprint matching this method makes use of structural information culled from the directional histograms of the directional image of a fingerprint the usage of the histogram allows for a fairly high degree of noise tolerance

fingerprint reference point detection for image retrieval based on symmetry and variation reference point plays an important role in fingerprint identification systems the reference point is widely used for the fingerprint retrieval in large scale databases this paper proposes a novel algorithm for detecting a convex core point as a unique reference point consistently and accurately for all types of fingerprints in order to detect robust core point candidates a modified complex filter called semi radial symmetry filter is proposed to detect correctly rotational symmetries of core points moreover a vertical orientation variation feature called voriv feature is proposed to remove spurious core points and concave core points therefore the proposed technique computes the variation and symmetry combined energy vscome then the reference point is located by searching the global vscome maximum the experimental results on the public database fvc2004 db1 set a show that the proposed technique exhibits a very high robustness and gets the best performance in comparing with other approaches in literature

a class of pattern mixture models for normal incomplete data summary likelihood based methods are developed for analyzing a random sample on two continuous variables when values of one of the variables are missing normal maximum likelihood estimates when values are missing completely at random were derived by anderson 1957 they are also maximum likelihood providing the missing data mechanism is ignorable in rubin s 1976 sense that the mechanism depends only on observed data a new class of pattern mixture models little 1993 is described for the situation where missingness is assumed to depend on an arbitrary unspecified function of a linear combination of the two variables maximum likelihood for models in this class is straightforward and yields the estimates of anderson 1957 when missingness depends solely on the completely observed variable and the estimates of brown 1990 when missingness depends solely on the incompletely observed variable another choice of linear combination yields estimates from complete case analysis large sample and bayesian methods are described for this model the data do not supply information about the ratio of the coefficients of the linear combination that controls missingness if this ratio is not welldetermined based on prior knowledge a prior distribution can be specified and bayesian inference is then readily accomplished alternatively sensitivity of inferences can be displayed for a variety of choices of the ratio

weighted parzen windows for pattern classification this paper introduces the weighted parzen window classifier the proposed technique uses a clustering procedure to find a set of reference vectors and weights which are used to approximate the parzen window kernel estimator classifier the weighted parzen window classifier requires less computation and storage than the full parzen window classifier experimental results showed that significant savings could be achieved with only minimal if any error rate degradation for synthetic and real data sets

reversible jump markov chain monte carlo computation and bayesian model determination markov chain monte carlo methods for bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure they have therefore not been available for application to bayesian model determination where the dimensionality of the parameter vector is typically not fixed this paper proposes a new framework for the construction of reversible markov chain samplers that jump between parameter subspaces of differing dimensionality which is flexible and entirely constructive it should therefore have wide applicability in model determination problems the methodology is illustrated with applications to multiple change point analysis in one and two dimensions and to a bayesian comparison of binomial experiments

marginalized transition models and likelihood inference for longitudinal categorical data summary marginal generalized linear models are now frequently used for the analysis of longitudinal data semiparametric inference for marginal models was introduced by liang and zeger 1986 biometrics73 13 22 this article develops a general parametric class of serial dependence models that permits likelihood based marginal regression analysis of binary response data the methods naturally extend the first order markov models of azzalini 1994 biometrika81 767 775 and prove computationally feasible for long series

a novel algorithm for detecting singular points from fingerprint images fingerprint analysis is typically based on the location and pattern of detected singular points in the images these singular points cores and deltas not only represent the characteristics of local ridge patterns but also determine the topological structure i e fingerprint type and largely influence the orientation field in this paper we propose a novel algorithm for singular points detection after an initial detection using the conventional poincare index method a so called doric feature is used to remove spurious singular points then the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model based orientation field reconstructed using the singular points a core delta relation is used as a global constraint for the final selection of singular points experimental results show that our algorithm is accurate and robust giving better results than competing approaches the proposed detection algorithm can also be used for more general 2d oriented patterns such as fluid flow motion and so forth

modeling longitudinal data with nonignorable dropouts using a latent dropout class model summary in longitudinal studies with dropout pattern mixture models form an attractive modeling framework to account for nonignorable missing data however pattern mixture models assume that the components of the mixture distribution are entirely determined by the dropout times that is two subjects with the same dropout time have the same distribution for their response with probability one as that is unlikely to be the case this assumption made lead to classification error in addition if there are certain dropout patterns with very few subjects which often occurs when the number of observation times is relatively large pattern specific parameters may be weakly identified or require identifying restrictions we propose an alternative approach which is a latent class model the dropout time is assumed to be related to the unobserved latent class membership where the number of classes is less than the number of observed patterns a regression model for the response is specified conditional on the latent variable this is a type of shared parameter model where the shared parameter is discrete parameter estimates are obtained using the method of maximum likelihood averaging the estimates of the conditional parameters over the distribution of the latent variable yields estimates of the marginal regression parameters the methodology is illustrated using longitudinal data on depression from a study of hiv in women