local empirical likelihood inference for varying coefficient density ratio models based on case control data in this article we develop a varying coefficient density ratio model for case control studies the case and control samples come from two different distributions under the model assumption the ratio of the two densities is related to the linear combination of covariates with varying coefficients through a known function a special case is the exponential tilt model where the log ratio of the two densities is a linear function of covariates we propose a local empirical likelihood el approach to estimate the nonparametric coefficient functions under some regularity assumptions the proposed estimators are shown to be consistent and asymptotically normally distributed the sieve empirical likelihood ratio selr test statistic for detecting whether the varying coefficients are really constant and other related hypotheses is constructed and it follows approximately a chi squared distribution we introduce a modified bootstrap procedure to estimate the null distribution of the selr when sample size is s

hypothesis testing in a mixture case control model summary we consider a problem of testing mixture proportions using two sample data one from group one and the other from a mixture of groups one and two with unknown proportion for being in group two various statistical applications including microarray study infectious epidemiological studies case control studies with contaminated controls clinical trials allowing nonresponders genetic studies for gene mutation and fishery applications can be formulated in this setup under the assumption that the log ratio of probability density functions from the two groups is linear in the observations we propose a generalized score test statistic to test the mixture proportion under some regularity conditions it is shown that this statistic converges to a weighted chi squared random variable under the null hypothesis of 0 where the weight depends only on the sampling fraction of both groups the permutation method is used to provide more reliable finite sample approximation simulation results and two real data applications are presented

recognition of spatial point patterns abstract complex patterns may sometimes be efficiently represented by a small number of points extracted from the image on the basis of atypical properties of neighborhoods of the points in this paper we describe theoretical and experimental results on the use of sequences of interpoint distances extracted from a noisy point image to match the image with one of a set of prototypes minimal spanning tree and nearest neighbor distances are used for these computations the methods applied are invariant under translation and rotation of the image and could be made invariant to scale changes several distance measures between classes are defined sufficient conditions for the separability of classes are given in terms of these measures

intensity based image registration by nonparametric local smoothing image registration is used widely in applications for mapping one image to another existing image registration methods are either feature based or intensity based feature based methods first extract relevant image features and then find the geometrical transformation that best matches the two corresponding sets of features extracted from the two images because identification and extraction of image features is often a challenging and time consuming process intensity based image registration by which the mapping transformation is estimated directly from the observed image intensities of the two images has received much attention recently in the literature most existing intensity based image registration methods estimate the mapping transformation globally by solving a minimization maximization problem defined by the two entire images to register to this end it needs to be assumed that the mapping transformation has a certain type of parametric form or it is a continuous bivariate function satisfying certain regularity conditions in this paper we propose a novel intensity based image registration method using nonparametric local smoothing by this method the mapping transformation at a given pixel is estimated locally in a neighborhood after certain image features are accommodated in the estimation due to the flexibility of local smoothing this method does not require any parametric form for the mapping transformation it even allows the transformation to be a discontinuous function numerical examples show that it is effective in various applications

a distributed hierarchical genetic algorithm for efficient optimization and pattern matching in this paper we propose a new approach in genetic algorithm called distributed hierarchical genetic algorithm dhga for optimization and pattern matching it is eventually a hybrid technique combining the advantages of both distributed and hierarchical processes in exploring the search space the search is initially distributed over the space and then in each subspace the algorithm works in a hierarchical way the entire space is essentially partitioned into a number of subspaces depending on the dimensionality of the space this is done in order to spread the search process more evenly over the whole space in each subspace the genetic algorithm is employed for searching and the search process advances from one hypercube to a neighboring hypercube hierarchically depending on the convergence status of the population and the solution obtained so far the dimension of the hypercube and the resolution of the search space are altered with iterations thus the search process passes through variable resolution coarse to fine search space both analytical and empirical studies have been carried out to evaluate the performance between dhga and distributed conventional ga dcga for different function optimization problems further the performance of the algorithms is demonstrated on problems like pattern matching and object matching with edge map

matching perspective views of a polyhedron using circuits we present a novel approach for finding corresponding points between two line drawings extracted from perspective views of a moving object whose surface is composed of planar polygons in our approach each circuit of the drawings is encoded with a boundary shape code which we call the rlcc code run length code of convex and concave strings then a clustering technique is used to obtain the matching result recursively a series of measures are taken to make the algorithm tolerate considerable dissimilarities which may exist between the two drawings such as missing lines scale differences rotation perspective shape distortions etc experimental results are presented

elastic maximal matching abstract in this paper we address the problem of matching objects which are subject to deformation the issue of determining the best elastic match is discussed and a scoring strategy evolved the matching process results in finding a label for each feature of the search object from the features of the database object a special label null is used to indicate the absence of a matching feature matching proceeds by backtracking preprocessing is done to reduce search space

dot pattern processing using voronoi neighborhoods a sound notion of the neighborhood of a point is essential for analyzing dot patterns the past work in this direction has concentrated on identifying pairs of points that are neighbors examples of such methods include those based on a fixed radius k nearest neighbors minimal spanning tree relative neighborhood graph and the gabriel graph this correspondence considers the use of the region enclosed by a point s voronoi polygon as its neighborhood it is argued that the voronoi polygons possess intuitively appealing characteristics as would be expected from the neighborhood of a point geometrical characteristics of the voronoi neighborhood are used as features in dot pattern processing procedures for segmentation matching and perceptual border extraction using the voronoi neighborhood are outlined extensions of the voronoi definition to other domains are discussed

calculation of power and sample size with bounded outcome scores the two sample wilcoxon rank sum test is the most popular non parametric test for the comparison of two samples when the underlying distributions are not normal although the underlying distributions need not be known in detail to calculate the null distribution of the test statistic parametric assumptions are often made to determine the power of the test or the sample size we encountered difficulties with this approach in the planning of a recent clinical trial in stroke patients it is shown that for power and sample size estimation it can be dangerous to apply the classical formulae routinely especially with outcome scores having a u shaped or a j shaped distribution as an example we have taken the barthel index a quality of life outcome measure in stroke patients further we have investigated alternative methods by means of monte carlo simulation the distributional characteristics of the estimated powers were compared our findings suggest more appropriate computer software is necessary for the calculation of power and sample size when efficacy is measured by a non parametric method

stable adaptive fuzzy control of nonlinear systems a direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control is capable of incorporating fuzzy if then control rules directly into the controllers and guarantees the global stability of the resulting closed loop system in the sense that all signals involved are uniformly bounded is developed the specific formula for the bounds is provided so that controller designers can determine the bounds based on their requirements the direct adaptive fuzzy controller is used to regulate an unstable system to the origin and to control the duffing chaotic system to track a trajectory the simulation results show that the controller worked without using any fuzzy control rules and that after fuzzy control rules were incorporated the adaptation speed became much faster it is shown explicitly how the supervisory control forces the state to remain within the constraint set and how the adaptive fuzzy controller learns to regain control

fast algorithm for point pattern matching invariant to translations rotations and scale changes abstract based on 2 d cluster approach a fast algorithm for point pattern matching is proposed to effectively solve the problems of optimal matches between two point pattern under geometrical transformation and correctly identify the missing or spurious points of patterns theorems and algorithms are developed to determine the matching pairs support of each point pair and its transformation parameters scaling s and rotation on a two parameter space s experiments are conducted both on real and synthetic data the experimental results show that the proposed matching algorithm can handle translation rotation and scaling differences under noisy or distorted condition the computational time is just about 0 5 s for 50 to 50 point matching on sun 4 workstation

stable fuzzy adaptive control for a class of nonlinear systems abstract in this paper two adaptive fuzzy control schemes for a class of nonlinear systems are proposed each scheme employs a fuzzy control with an adaptive law and a compensation control it is proved that the closed loop system is asymptotically stable based on lyapunov synthesis approach while the identification of the controlled plant is unnecessary finally these schemes are applied to control a chaotic system the simulation results show that our schemes are superior to the scheme of 12

the use of bootstrap methods for estimating sample size and analysing health related quality of life outcomes health related quality of life hrqol measures are increasingly used in trials as primary outcome measures investigators are now asking statisticians for advice on how to plan and analyse studies using such outcomes hrqol outcomes like the sf 36 are usual measured on an ordinal scale although most investigators assume that there exists an underlying continuous latent variable and that the actual measured outcomes the ordered categories reflect contiguous intervals along this continuum the ordinal scaling of hrqol measures means they tend to generate data that have discrete bounded and skewed distributions thus standard methods of analysis that assume normality and constant variance may not be appropriate for this reason conventional statistical advice would suggest non parametric methods be used to analyse hrqol data the bootstrap is one such computer intensive non parametric method for estimating sample sizes and analysing data we describe three methods of estimating sample sizes for two group cross sectional comparisons of hrqol outcomes we then compared the power of the three methods for a two group cross sectional study design using bootstrap simulation the results showed that under the location shift alternative hypothesis conventional methods of sample size estimation performed well particularly whitehead s method whitehead s method is recommended if the hrqol outcome has a limited number of discrete values 7 and or the expected proportion of cases at either of the bounds is high if a pilot data set is readily available then bootstrap simulation will provide a more accurate and reliable estimate than conventional methods finally we used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters in an example data set we then compared and contrasted the bootstrap with standard methods of analysing hrqol outcomes in the data set studied with the sf 36 outcome the use of the bootstrap for estimating sample sizes and analysing hrqol data produces results similar to conventional statistical methods these results suggest that bootstrap methods are not more appropriate for analysing hrqol outcome data than standard methods

stability analysis and design of fuzzy control systems abstract the stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed first we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams we derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks in order to preserve some properties in a connection of fuzzy blocks continuous piecewise polynomial membership functions are defined secondly a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of lyapunov s direct method we give an important fact based on this condition thirdly we propose a new design technique of a fuzzy controller the fuzzy block diagrams and the stability analysis are applied to the design problems of a model based fuzzy controller

choice of test for comparing two groups with particular application to skewed outcomes we consider a clinical trial where a skewed outcome variable is to be compared between two groups while comparison of sample means may lack power we show that power also depends on the nature of the anticipated treatment effect for any given distribution in the control arm there is a family of true distributions in the intervention arm for which the most powerful test is a comparison of arithmetic means similar results hold for a comparison of geometric means and approximately for the wilcoxon rank sum test and a comparison of medians we discuss how these methods could be used in planning the analysis of a clinical trial in which the intervention effect alters the shape of the distribution these ideas are illustrated by a trial in community psychiatry where the primary outcome days in hospital was highly skewed but the intervention was mainly expected to reduce the frequency of values in the tail we show that a comparison of sample means is a reasonable choice in this case despite the skewness

a modified wilcoxon test for non negative distributions with a clump of zeros comparing two samples with a continuous non negative score e g a utility score over 0 1 with a substantial proportion say 50 per cent scoring 0 presents distributional problems for most standard tests a wilcoxon rank test can be used but the large number of ties reduces power i propose a new test the wilcoxon rank sum test performed after removing an equal and maximal number of 0 s from each sample this test recovers much of the power compared with a directional modification of a two part test proposed by lachenbruch the truncated wilcoxon has similar power when the non zero scores are independent of the proportion of zeros but unlike the two part test the truncated wilcoxon is relatively unaffected when these processes are dependent

labeled point pattern matching by delaunay triangulation and maximal cliques abstract in many problems of pattern recognition objects in an image may be efficiently represented by a set of labeled points this paper describes a method for matching labeled point patterns with one of the prototypes the delaunay triangulation is used to partition a point pattern into a set of triangles and the largest maximal clique of the consistency graph for each triangle is used to obtain the largest set of mutually consistent point pairs the proposed method is invariant under affine transformation of point patterns and allows for additions and deletions of points and some random perturbations in their relative locations the method is applied to the matching of constellations

some power considerations when deciding to use transformations conventional wisdom suggests that for small data sets having substantial skew one should attempt to determine the correct distributional form if possible and apply statistical methods appropriate for that distribution transformations such as the log or square root are often used if an appropriate distributional form cannot be determined a distribution free procedure such as a rank transformation or a randomization test procedure can be used to better appreciate the effect of such alternatives on both the type i error and power of detecting differences between treatment groups simulation studies were conducted for responses having specific gamma g r theta and log normal ln m v distributions the gamma and log normal distributions were selected so that they had the same first two moments a simple two group design was assumed the reference group always had an average disease level mu 3 0 mu r theta for gamma mu m for log normal and the treatment group always had means whose reductions ranged from 0 per cent to 50 per cent the effect of distributional type and the degree of skewness was investigated by varying the population parameter values six statistical test procedures were compared for the gamma distributions all test procedures were robust relative to the type i error the ump test based on a ratio of sample means produced the greatest power for all combinations of n r and rt the power losses associated with the randomization test the t test on original scale and the t test on the square root scale were very small 3 per cent to 6 per cent in absolute value for n 10 and 15 and less than 2 per cent for group sizes of 25 or more the power loss associated with the t test on the log scale was much larger ranging from 5 per cent to 10 per cent smaller power than the t test on original scale the wilcoxon rank test produced similar results to that of the log t test for small samples the power for the shifted log x c test increased monotonically to the asymptotic value of the orig t test the same five test procedures based on differences in sample means were then compared for the corresponding log normal distributions the ump test that is log x produced the highest power there was very little power lost for the sqrt t test the loss in power varied between 2 per cent and 5 per cent for the rank test abstract truncated at 400 words

comparison of two treatments with skewed ordinal responses in clinical studies the proportional odds model is widely used to compare treatment efficacies when the responses are categorically ordered however this model has been shown to be inappropriate when the proportional odds assumption is invalid mainly because it is unable to control the type i error rate in such circumstances to remedy this problem the latent normal model was recently promoted and has been demonstrated to be superior to the proportional odds model however the application of the latent normal model is limited to compare treatments with similar underlying distributions except possibly their means and variances when the underlying distributions are very different in skewness both of the aforementioned procedures suffer from the undesirable inflation of the type i error rate to solve the problem for clinical studies with ordinal responses we provide a viable solution that relies on the use of the latent weibull distribution which is a member of the log location scale family the proposed model is able to control the type i error rate regardless of the degree of skewness of the treatment responses in addition the power of the test also outperforms that of the latent normal model the testing procedure draws on newly developed theoretical results related to latent distributions from the location scale family the testing procedure is illustrated with two clinical examples

a semiparametric two component compound mixture model and its application to estimating malaria attributable fractions summary malaria remains a major epidemiologic problem in many developing countries malaria is defined as the presence of parasites and symptoms usually fever due to the parasites in endemic areas an individual may have symptoms attributable either to malaria or to other causes from a clinical viewpoint it is important to correctly diagnose an individual who has developed symptoms so that the appropriate treatments can be given from an epidemiologic and economic viewpoint it is important to determine the proportion of malaria affected cases in individuals who have symptoms so that policies on intervention program can be developed once symptoms have developed in an individual the diagnosis of malaria can be based on the analysis of the parasite levels in blood samples however even a blood test is not conclusive as in endemic areas many healthy individuals can have parasites in their blood slides therefore data from this type of study can be viewed as coming from a mixture distribution with the components corresponding to malaria and nonmalaria cases a unique feature in this type of data however is the fact that a proportion of the nonmalaria cases have zero parasite levels therefore one of the component distributions is itself a mixture distribution in this article we propose a semiparametric likelihood approach for estimating the proportion of clinical malaria using parasite level data from a group of individuals with symptoms our approach assumes the density ratio for the parasite levels in clinical malaria and nonclinical malaria cases can be modeled using a logistic model we use empirical likelihood to combine the zero and nonzero data the maximum semiparametric likelihood estimate is more efficient than existing nonparametric estimates using only the frequencies of zero and nonzero data on the other hand it is more robust than a fully parametric maximum likelihood estimate that assumes a parametric model for the nonzero data simulation results show that the performance of the proposed method is satisfactory the proposed method is used to analyze data from a malaria survey carried out in tanzania

semiparametric estimation and inference for distributional and general treatment effects summary there is a large literature on methods of analysis for randomized trials with noncompliance which focuses on the effect of treatment on the average outcome the paper considers evaluating the effect of treatment on the entire distribution and general functions of this effect for distributional treatment effects fully non parametric and fully parametric approaches have been proposed the fully non parametric approach could be inefficient but the fully parametric approach is not robust to the violation of distribution assumptions we develop a semiparametric instrumental variable method based on the empirical likelihood approach our method can be applied to general outcomes and general functions of outcome distributions and allows us to predict a subject s latent compliance class on the basis of an observed outcome value in observed assignment and treatment received groups asymptotic results for the estimators and likelihood ratio statistic are derived a simulation study shows that our estimators of various treatment effects are substantially more efficient than the currently used fully non parametric estimators the method is illustrated by an analysis of data from a randomized trial of an encouragement intervention to improve adherence to prescribed depression treatments among depressed elderly patients in primary care practices

computer intensive methods for tests about the mean of an asymmetrical distribution abstract for one sided tests about the mean of a skewed distribution the t test is asymptotically robust for validity however it can be quite inaccurate and inefficient with small sample sizes results presented here confirm that a procedure due to johnson should be preferred to the t test when the parent distribution is asymmetrical because it reduces the probability of type i error in cases where the t test has an inflated type i error rate and it is more powerful in other situations but if the skewness is severe and the sample size is small then johnson s test can also be appreciably inaccurate for such situations computer intensive test procedures using bootstrap resampling are proposed and with an extensive monte carlo study it is shown that these procedures are remarkably robust and can result in reduced probabilities of type i and type ii errors compared to johnson s test

point matching using asymmetric neural networks abstract matching control points is an important step in many pattern recognition applications the matching problem is formulated under translation and rotation as a 0 1 integer programming problem and an artificial neural network is proposed for approximately solving it the solution to the 0 1 integer programming problem is obtained as the high gain limit point of the continuous network the network is capable of handling distortion and noise and can use both interpoint distance information and feature properties associated with the points the results obtained by the network compare favourably with that of the relaxation method of ton and jain ieee trans geosci remote sensing 27 642 651 1989

the log transformation is special the logarithmic log transformation is a simple yet controversial step in the analysis of positive continuous data measured on an interval scale situations where a log transformation is indicated will be reviewed this paper contends that the log transformation should not be classed with other transformations as it has particular advantages problems with using the data themselves to decide whether or not to transform will be discussed it is recommended that log transformed analyses should frequently be preferred to untransformed analyses and that careful consideration should be given to use of a log transformation at the protocol design stage