a novel wavelet based generalized sidelobe canceller this paper presents a novel narrow band adaptive beamformer with the generalized sidelobe canceller gsc as the underlying structure the new beamformer employs a wavelet based approach for the design of the blocking matrix of the gsc which is now constituted by a set of regular m band wavelet filters such a construction of the blocking matrix can not only block the desired signals from the lower path as required provided the wavelet filters have sufficiently high regularity but it also encompasses the widely used one with ones and minus ones along the diagonals as a special case in addition it possesses two advantageous features first the eigenvalue spreads of the covariance matrices of the blocking matrix outputs as demonstrated in various scenarios are decreased as compared with those of previous approaches since the popular least mean squares lms algorithm has been notorious for its slow convergence rate the reduction of the eigenvalue spreads can in general accelerate the convergence speed of the succeeding lms algorithm second the new beamformer belongs to a specific type of partially adaptive beamformers wherein only a portion of the available degree of freedom is utilized in the adaptive processing as such the overall computational complexity is substantially reduced when compared to previous works the issues of choosing the parameters involved for superior performance are also addressed simulation results are furnished as well to justify this new approach

gene identification using true discovery rate degree of association sets and estimates corrected for regression to the mean analyses intended to identify genes with expression that is associated with some clinical outcome or state are often based on ranked p values from tests of point null hypotheses of no association van de wiel and kim take the innovative approach of testing the interval null hypotheses that the degree of association for a gene is less than some value of interest against the alternative that it is greater combining this idea with the false discovery rate controlling methods of storey taylor and siegmund gives a computationally simple way to identify true discovery rate degree of association tdrda sets of genes among which a specified proportion are expected to have an absolute association of a specified degree or more this leads to a gene ranking method that uses the maximum lower bound degree of association for which each gene belongs to a tdrda set estimates of each gene s actual degree of association with approximate correction for selection bias due to regression to the mean rm can be derived using simple bivariate normal theory and efron and tibshirani s empirical bayes approach for a given data set all possible tdrda sets can be displayed along with the gene ranking and the rm corrected estimates of degree of association in a concise graphical summary

on the implementation of mixed methods as nonconforming methods for second order elliptic problems in this paper we show that mixed finite element methods for a fairly general second order elliptic problem with variable coefficients can be given a nonmixed formulation lower order terms are treated so our results apply also to parabolic equations we define an approximation method by incorporating some projection operators within a standard galerkin method which we call a projection finite element method it is shown that for a given mixed method if the projection method s finite element space m h satisfies three conditions then the two approximation methods are equivalent these three conditions can be simplified for a single element in the case of mixed spaces possessing the usual vector projection operator we then construct appropriate nonconforming spaces m h for the known triangular and rectangular elements the lowest order raviart thomas mixed solution on rectangular finite elements in r 2 and r 3 on simplices or on prisms is then implemented as a nonconforming method modified in a simple and computationally trivial manner this new nonconforming solution is actually equivalent to a postprocessed version of the mixed solution a rearrangement of the computation of the mixed method solution through this equivalence allows us to design simple and optimal order multigrid methods for the solution of the linear system

a sharper bonferroni procedure for multiple tests of significance summary a simple procedure for multiple tests of significance based on individual p values is derived this simple procedure is sharper than holm s 1979 sequentially rejective procedure both procedures contrast the ordered p values with the same set of critical values holm s procedure rejects an hypothesis only if its p value and each of the smaller p values are less than their corresponding critical values the new procedure rejects all hypotheses with smaller or equal p values to that of any one found less than its critical value

total positivity properties of absolute value multinormal variables with applications to confidence interval estimates and related probabilistic inequalities total positivity properties of multivariate densities are useful in deducing positive dependence of random vector components and related probability inequalities in this paper we determine necessary and sufficient conditions for total positivity of absolute value multinormal variables the results are applied to obtain positive dependence and associated inequalities for the multinormal and related distributions e g the multivariate t and wishart distributions inequalities of this type yield bounds for multivariate confidence set probabilities

stepup procedures controlling generalized fwer and generalized fdr in many applications of multiple hypothesis testing where more than one false rejection can be tolerated procedures controlling error rates measuring at least k false rejections instead of at least one for some fixed k 1 can potentially increase the ability of a procedure to detect false null hypotheses the k fwer a generalized version of the usual familywise error rate fwer is such an error rate that has recently been introduced in the literature and procedures controlling it have been proposed a further generalization of a result on the k fwer is provided in this article in addition an alternative and less conservative notion of error rate the k fdr is introduced in the same spirit as the k fwer by generalizing the usual false discovery rate fdr a k fwer procedure is constructed given any set of increasing constants by utilizing the kth order joint null distributions of the p values without assuming any specific form of dependence among all the p values procedures controlling the k fdr are also developed by using the kth order joint null distributions of the p values first assuming that the sets of null and nonnull p values are mutually independent or they are jointly positively dependent in the sense of being multivariate totally positive of order two mtp2 and then discarding that assumption about the overall dependence among the p values

theory of regular m band wavelet bases orthonormal m band wavelet bases have been constructed and applied by several authors this paper makes three main contributions first it generalizes the minimal length k regular 2 band wavelets of daubechies 1988 to the m band case by deriving explicit formulas for k regular m band scaling filters several equivalent characterizations of k regularity are given and their significance explained second two approaches to the construction of the m 1 wavelet filters and associated wavelet bases are described one relies on a state space characterization with a novel technique to obtain the unitary wavelet filters the other uses a factorization approach third this paper gives a set of necessary and sufficient condition on the m band scaling filter for it to generate an orthonormal wavelet basis the conditions are very similar to those obtained by cohen 1990 and lawton 1990 for 2 band wavelets

an improved bonferroni procedure for multiple tests of significance summary a modification of the bonferroni procedure for testing multiple hypotheses is presented the method based on the ordered p values of the individual tests is less conservative than the classical bonferroni procedure but is still simple to apply a simulation study shows that the probability of a type i error of the procedure does not exceed the nominal significance level a for a variety of multivariate normal and multivariate gamma test statistics for independent tests the procedure has type i error probability equal to a the method appears particularly advantageous over the classical bonferroni procedure when several highly correlated test statistics are involved

performance analysis of derivative constraint adaptive arrays with pointing errors the performance of adaptive arrays with arbitrary order derivative constraints to avoid the signal nulling caused by the error in the steering angle is investigated the generalized sidelobe canceller gsc is used to evaluate the bearing responses of adaptive arrays with pointing errors under the assumption of equispaced linear arrays the legendre polynomial is used to derive a closed form formula for the sidelobe leakage factor the ratio in which the signal leaks into the gsc sidelobe cancelling branch moreover the effective beamwidth the maximum range of pointing inaccuracy allowable to keep the signal loss in array output less than a given value is derived for the gsc with a delay and sum quiescent beamformer such adaptive arrays can achieve the maximum output signal to noise ratio when the presteering is perfect and the performance is independent of the location of the phase origin numerical results are presented to confirm the analysis

superconvergent analysis of a nonconforming mixed finite element method for non stationary conduction convection problem abstract in this paper a backward euler mixed finite element mfe approximation scheme is presented for the non stationary conduction convection problem in which the constrained nonconforming rotated cnr q 1 element is used to approximate the velocity u the temperature t and the q 0 element to the pressure p based on the characters of these elements and some special skills i e mean value skill and a new transforming skill with respect to the superclose results of order o h 2 for u t in the broken h 1 norm and p in l 2 norm are deduced respectively here h is the subdivision parameter and the time step furthermore the global superconvergent results are obtained through the interpolation postprocessing technique finally numerical results are provided to confirm the theoretical analysis

sharp maximum norm error estimates for finite element approximations of the stokes problem in 2 d etude des approximations a elements finis des equations de stokes dans un domaine borne a l aide d une formulation mixte vitesse pression etablissement d estimations d erreur quasi optimales dans la norme maximum pour la vitesse son gradient et les champs de pression

the simes method for multiple hypothesis testing with positively dependent test statistics abstract the simes method for testing intersection of more than two hypotheses is known to control the probability of type i error only when the underlying test statistics are independent although this method is more powerful than the classical bonferroni method it is not known whether it is conservative when the test statistics are dependent this article proves that for multivariate distributions exhibiting a type of positive dependence that arise in many multiple hypothesis testing situations the simes method indeed controls the probability of type i error this extends some results established very recently in the special case of two hypotheses

correlated z values and the accuracy of large scale statistical estimates we consider large scale studies in which there are hundreds or thousands of correlated cases to investigate each represented by its own normal variate typically a z value a familiar example is provided by a microarray experiment comparing healthy with sick subjects expression levels for thousands of genes this paper concerns the accuracy of summary statistics for the collection of normal variates such as their empirical cdf or a false discovery rate statistic it seems like we must estimate an n by n correlation matrix n the number of cases but our main result shows that this is not necessary good accuracy approximations can be based on the root mean square correlation over all n n 1 2 pairs a quantity often easily estimated a second result shows that z values closely follow normal distributions even under nonnull conditions supporting application of the main theorem practical application of the theory is illustrated for a large leukemia microarray study

a convergence condition for the quadrilateral wilson element the paper deals with the convergence properties of the nonconforming quadrilateral wilson element which violates the patch test the convergence of the element is proved under a certain condition on mesh subdivisions without any modifications of the variational formulation this result extends the range of applicability of wilson s element the necessity of the proposed condition is also discussed

a stagewise rejective multiple test procedure based on a modified bonferroni test summary simes 1986 has proposed a modified bonferroni procedure for the test of an overall hypothesis which is the combination of n individual hypotheses in contrast to the classical bonferroni procedure it is not obvious how statements about individual hypotheses are to be made for this procedure in the present paper a multiple test procedure allowing statements on individual hypotheses is proposed it is based on the principle of closed test procedures marcus peritz gabriel 1976 and controls the multiple level a

fiber density estimation from single q shell diffusion imaging by tensor divergence diffusion weighted magnetic resonance imaging provides information about the nerve fiber bundle geometry of the human brain while the inference of the underlying fiber bundle orientation only requires single q shell measurements the absolute determination of their volume fractions is much more challenging with respect to measurement techniques and analysis unfortunately the usually employed multi compartment models cannot be applied to single q shell measurements because the compartment s diffusivities cannot be resolved this work proposes an equation for fiber orientation densities that can infer the absolute fraction up to a global factor this equation which is inspired by the classical mass preservation law in fluid dynamics expresses the fiber conservation associated with the assumption that fibers do not terminate in white matter simulations on synthetic phantoms show that the approach is able to derive the densities correctly for various configurations experiments with a pseudo ground truth phantom show that even for complex brain like geometries the method is able to infer the densities correctly in vivo results with 81 healthy volunteers are plausible and consistent a group analysis with respect to age and gender show significant differences such that the proposed maps can be used as a quantitative measure for group and longitudinal analysis

performance measures for optimal constrained beamformers in this paper the closed form expression is derived which relates the output signal to interference plus noise ratio to the minimum mean square error of constrained beamformers for narrowband and wideband signal environments

superconvergence analysis of low order nonconforming finite element methods for variational inequality problem with displacement obstacle abstract superconvergence analysis of nonconforming finite element methods fems are discussed for solving the second order variational inequality problem with displacement obstacle the elements employed have a common typical character i e the consistency error can reach order o h 3 2 nearly 1 2 order higher than their interpolation error when the exact solution of the considered problem belongs to h 5 2 for any e 0 by making full use of special properties of the element s interpolations and bramble hilbert lemma the superconvergence error estimates of order o h 3 2 in the broken h1 norm are derived finally some numerical results are provided to confirm the theoretical results

some probability inequalities for ordered rm mtp sb 2 random variables a proof of the simes conjecture some new probability inequalities involving the ordered components of an mtp 2 random vector are derived which provide an analytical proof of an important conjecture in the field of multiple hypothesis testing this conjecture has been mostly validated so far using simulation

dependency and false discovery rate asymptotics some effort has been undertaken over the last decade to provide conditions for the control of the false discovery rate by the linear step up procedure lsu for testing n hypotheses when test statistics are dependent in this paper we investigate the expected error rate eer and the false discovery rate fdr in some extreme parameter configurations when n tends to infinity for test statistics being exchangeable under null hypotheses all results are derived in terms of p values in a general setup we present a series of results concerning the interrelation of simes rejection curve and the limiting empirical distribution function of the p values main objects under investigation are largest limiting crossing points between these functions which play a key role in deriving explicit formulas for eer and fdr as specific examples we investigate equi correlated normal and t variables in more detail and compute the limiting eer and fdr theoretically and numerically a surprising limit behavior occurs if these models tend to independence

multiple tests based on a gaussian approximation of the unitary events method with delayed coincidence count the unitary events ue method is one of the most popular and efficient methods used over the past decade to detect patterns of coincident joint spike activity among simultaneously recorded neurons the detection of coincidences is usually based on binned coincidence count grun 1996 which is known to be subject to loss in synchrony detection grun diesmann grammont riehle aertsen 1999 this defect has been corrected by the multiple shift coincidence count grun et al 1999 the statistical properties of this count have not been further investigated until this work the formula being more difficult to deal with than the original binned count first we propose a new notion of coincidence count the delayed coincidence count which is equal to the multiple shift coincidence count when discretized point processes are involved as models for the spike trains moreover it generalizes this notion to nondiscretized point processes allowing us to propose a new gaussian approximation of the count since unknown parameters are involved in the approximation we perform a plug in step where unknown parameters are replaced by estimated ones leading to a modification of the approximating distribution finally the method takes the multiplicity of the tests into account via a benjamini and hochberg approach benjamini hochberg 1995 to guarantee a prescribed control of the false discovery rate we compare our new method mtgaue multiple tests based on a gaussian approximation of the unitary events and the ue method proposed in grun et al 1999 over various simulations showing that mtgaue extends the validity of the previous method in particular mtgaue is able to detect both profusion and lack of coincidences with respect to the independence case and is robust to changes in the underlying model furthermore mtgaue is applied on real data

performance analysis of a wavelet based generalized sidelobe canceller in this paper we carry out a detailed performance analysis of the recently addressed wavelet based generalized sidelobe canceller gsc which employs a set of p regular m band wavelet filters in the design of the blocking matrix involved we begin our analysis by developing a closed form expression of the output signal to interference plus noise ratio sinr of partially adaptive beamformers with the gsc as the underlying structure under a one jammer or multiple jammer environment this new expression is shown to encompass several previous works as special cases we then examine the relationship between the choices of the parameters p and m in the wavelet based gsc and the corresponding output sinr based on this expression some guidelines for the choices of these parameters can thus be induced to achieve superior performance simulation results are also furnished to justify these suggestions

conditional association and unidimensionality in monotone latent variable models latent variable models represent the joint distribution of observable variables in terms of a simple structure involving unobserved or latent variables usually assuming the conditional independence of the observable variables given the latent variables these models play an important role in educational measurement and psychometrics in sociology and in population genetics and are implicit in some work on systems reliability we study a broad class of latent variable models namely the monotone unidimensional models in which the latent variable is a scalar the observable variables are conditionally independent given the latent variable and the conditional distribution of the observables given the latent variable is stochastically increasing in the latent variable all models in this class imply a new strong form of positive dependence among the observable variables namely conditional positive association this positive dependence condition may be used to test whether any model in this class can provide an adequate fit to observed data various applications generalizations and a numerical example are discussed

introduction to causal inference the goal of many sciences is to understand the mechanisms by which variables came to take on the values they have that is to find a generative model and to predict what the values of those variables would be if the naturally occurring mechanisms were subject to outside manipulations the past 30 years has seen a number of conceptual developments that are partial solutions to the problem of causal inference from observational sample data or a mixture of observational sample and experimental data particularly in the area of graphical causal modeling however in many domains problems such as the large numbers of variables small samples sizes and possible presence of unmeasured causes remain serious impediments to practical applications of these developments the articles in the special topic on causality address these and other problems in applying graphical causal modeling algorithms this introduction to the special topic on causality provides a brief introduction to graphical causal modeling places the articles in a broader context and describes the differences between causal inference and ordinary machine learning classification and prediction problems

detection of the number of signals using the benjamini hochberg procedure this paper presents a novel approach to detect multiple signals embedded in noisy observations from a sensor array we formulate the detection problem as a multiple hypothesis test to control the global level of the multiple test we apply the false discovery rate fdr criterion proposed by benjamini and hochberg compared to the classical familywise error rate fwe criterion the fdr controling procedure leads to a significant gain in power for large size problems in addition we apply the bootstrap technique to estimate the observed significance level required by the fdr controling procedure simulations show that the fdr controling procedure always provides higher probability of correct detection than the fwe controling procedure furthermore the reliability of the proposed test procedure is not affected by the gain in power of the test

testing the conditional independence and monotonicity assumptions of item response theory when item characteristic curves are nondecreasing functions of a latent variable the conditional or local independence of item responses given the latent variable implies nonnegative conditional covariances between all monotone increasing functions of a set of item responses given any function of the remaining item responses this general result provides a basis for testing the conditional independence assumption without first specifying a parametric form for the nondecreasing item characteristic curves the proposed tests are simple have known asymptotic null distributions and possess certain optimal properties in an example the conditional independence hypothesis is rejected for all possible forms of monotone item characteristic curves

improving the reliability of causal discovery from small data sets using argumentation we address the problem of improving the reliability of independence based causal discovery algorithms that results from the execution of statistical independence tests on small data sets which typically have low reliability we model the problem as a knowledge base containing a set of independence facts that are related through pearl s well known axioms statistical tests on finite data sets may result in errors in these tests and inconsistencies in the knowledge base we resolve these inconsistencies through the use of an instance of the class of defeasible logics called argumentation augmented with a preference function that is used to reason about and possibly correct errors in these tests this results in a more robust conditional independence test called an argumentative independence test our experimental evaluation shows clear positive improvements in the accuracy of argumentative over purely statistical tests we also demonstrate significant improvements on the accuracy of causal structure discovery from the outcomes of independence tests both on sampled data from randomly generated causal models and on real world data sets

nonconforming finite elements for the stokes problem on obtient un nouveau resultat de stabilite pour l approximation du probleme de stokes stationnaire avec des elements finis non conformes

nonconforming finite element approximation of crystalline microstructure we consider a class of nonconforming nite element approxima tions of a simply laminated microstructure which minimizes the nonconvex variational problem for the deformation of martensitic crystals which can un dergo either an orthorhombic to monoclinic double well or a cubic to tetrag onal triple well transformation we rst establish a series of error bounds in terms of elastic energies for the l 2 approximation of derivatives of the de formation in the direction tangential to parallel layers of the laminate for the l 2 approximation of the deformation for the weak approximation of the de formation gradient for the approximation of volume fractions of deformation gradients and for the approximation of nonlinear integrals of the deformation gradient we then use these bounds to give corresponding convergence rates for quasi optimal nite element approximations

two dimensional wavelet based generalized sidelobe canceller with subband selection scheme we present two approaches to the design of two dimensional 2 d blocking matrices of the generalized sidelobe canceller gsc the first approach is an extension of the wavelet based partially adaptive beamformer considered in it can be shown that this 2 d blocking matrix with the regularity p nulls out the first p 1 order taylor s series expansion of the desired signal hence the derivative constraints can be used to establish a flatter mainlobe response the second method uses the subband decomposition characteristic of these wavelets only the principal subband component psc of the decomposed data is kept for adaptive process as such the overall computational complexity is substantially reduced the resulting adaptive beamformer is less sensitive to steering angle errors

support vector machines for dyadic data we describe a new technique for the analysis of dyadic data where two sets of objects row and column objects are characterized by a matrix of numerical values that describe their mutual relationships the new technique called potential support vector machine p svm is a large margin method for the construction of classifiers and regression functions for the column objects contrary to standard support vector machine approaches the p svm minimizes a scale invariant capacity measure and requires a new set of constraints as a result the p svm method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square we then describe two complementary regularization schemes the first scheme improves generalization performance for classification and regression tasks the second scheme leads to the selection of a small informative set of row support objects and can be applied to feature selection benchmarks for classification regression and feature selection tasks are performed with toy data as well as with several real world data sets the results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets in addition a theoretical justification is provided for the new approach

joint mean and covariance estimation with unreplicated matrix variate data abstractit has been proposed that complex populations such as those that arise in genomics studies may exhibit dependencies among observations as well as among variables this gives rise to the challenging problem of analyzing unreplicated high dimensional data with unknown mean and dependence structures matrix variate approaches that impose various forms of inverse covariance sparsity allow flexible dependence structures to be estimated but cannot directly be applied when the mean and covariance matrices are estimated jointly we present a practical method utilizing generalized least squares and penalized inverse covariance estimation to address this challenge we establish consistency and obtain rates of convergence for estimating the mean parameters and covariance matrices the advantages of our approaches are i dependence graphs and covariance structures can be estimated in the presence of unknown mean structure ii the mean structure becomes more efficiently estimated when accounting for

accumulation tests for fdr control in ordered hypothesis testing abstractmultiple testing problems arising in modern scientific applications can involve simultaneously testing thousands or even millions of hypotheses with relatively few true signals in this article we consider the multiple testing problem where prior information is available for instance from an earlier study under different experimental conditions that can allow us to test the hypotheses as a ranked list to increase the number of discoveries given an ordered list of n hypotheses the aim is to select a data dependent cutoff k and declare the first k hypotheses to be statistically significant while bounding the false discovery rate fdr generalizing several existing methods we develop a family of accumulation tests to choose a cutoff k that adapts to the amount of signal at the top of the ranked list we introduce a new method in this family the hingeexp method which offers higher power to detect true signals compared to existing techniques our theoretical results prove that these metho

the control of the false discovery rate in multiple testing under dependency benjamini and hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems a simple procedure was given there as an fdr controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate we prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses this condition for positive dependency is general enough to cover many problems of practical interest including the comparisons of many treatments with a single control multivariate normal test statistics with positive correlation matrix and multivariate t furthermore the test statistics may be discrete and the tested hypotheses composite without posing special difficulties for all other forms of dependency a simple conservative modification of the procedure controls the false discovery rate thus the range of problems for which a procedure with proven fdr control can be offered is greatly increased 1 1 simultaneous hypotheses testing the control of the increased type i error when testing simultaneously a family of hypotheses is a central issue in the area of multiple comparisons rarely are we interested only in whether all hypotheses are jointly true or not which is the test of the intersection null hypothesis in most applications we infer about the individual hypotheses realizing that some of the tested hypotheses are usually true we hope not all and some are not we wish to decide which ones are not true indicating statistical discoveries an important such problem is that of multiple endpoints in a clinical trial a new treatment is compared with an existing one in terms of a large number of potential benefits endpoints

steady state analysis of the generalized sidelobe canceller by adaptive noise cancelling techniques narrow band adaptive noise cancelling techniques are used to study the generalized sidelobe canceller gsc a general form of linearly constrained adaptive beamforming structure in an environment which consists of a look direction signal one jammer and additive receiver noise exact expressions are derived for the wiener solution the steady state output signal to interference plus noise ratio sinr 0 and performance improvement due to adaptation pia defined as the ratio of sinr 0 after adaptation to sinr0 before adaptation these expressions are in terms of the signal directions and power levels an arbitrary array geometry and a general signal blocking matrix next easily evaluated scalar equations for pia are given for two classes of constraints the first is constant gain in the look direction and the second is constant gain plus a main beam zero first derivative in the look direction under the further assumption of an equally spaced line array even simpler equations for pia result and are used to show that for jammers arriving outside the heamwidth between first nulls bwfn region of the unadapted beampattern the introduction of the additional main beam zero first derivative constraint leads to negligible degradation in pia

a direct approach to false discovery rates summary multiple hypothesis testing involves guarding against much more complicated errors than single hypothesis testing whereas we typically control the type i error rate for a single hypothesis test a compound error rate is controlled for multiple hypothesis tests for example controlling the false discovery rate fdr traditionally involves intricate sequential p value rejection methods based on the observed data whereas a sequential p value method fixes the error rate and estimates its corresponding rejection region we propose the opposite approach we fix the rejection region and then estimate its corresponding error rate this new approach offers increased applicability accuracy and power we apply the methodology to both the positive false discovery rate pfdr and fdr and provide evidence for its benefits it is shown that pfdr is probably the quantity of interest over fdr also discussed is the calculation of the q value the pfdr analogue of the p value which eliminates the need to set the error rate beforehand as is traditionally done some simple numerical examples are presented that show that this new approach can yield an increase of over eight times in power compared with the benjamini hochberg fdr method

weighted inf sup condition and pointwise error estimates for the stokes problem convergence of mixed finite element approximations to the stokes problem in the primitive variables is examined in maximum norm quasi optimal pointwise error estimates are derived for discrete spaces satisfying a weighted inf sup condition similar to the babuska brezzi condition the usual techniques employed to prove the inf sup condition in energy norm can be easily extended to the present situation thus providing several examples to our abstract framework

eigenstructure based partially adaptive array design a procedure is presented for designing partially adaptive arrays having nearly fully adaptive performance under steady state conditions theory predicts that the required adaptive dimension is less than or equal to the rank of the spatially temporally correlated portion of the interference correlation matrix for arbitrary linearly constrained minimum variance beamformers knowledge of the eigenstructure of the interference correlation matrix is required to implement a beamformer with this adaptive dimension to avoid adaptive estimation of the eigenstructure the eigenstructure of an averaged correlation matrix which spans the interference scenarios of interest is utilized and the adaptive dimension is given by the rank of the averaged correlation matrix construction of a transformation for reducing adaptive dimension is addressed simulations illustrating the utility of the eigenstructure approach are provided

analysis of a class of nonconforming finite elements for crystalline microstructures an analysis is given for a class of nonconforming lagrange type finite elements which have been successfully utilized to approximate the solution of a variational problem modeling the deformation of martensitic crystals with microstructure these elements were first proposed and analyzed in 1992 by rannacher and turek for the stokes equation our analysis highlights the features of these elements which make them effective for the computation of microstructure new results for superconvergence and numerical quadrature are also given

on the accuracy of nonconforming quadrilateral q 1 element approximation for the navier stokes problem this paper deals with the nonconforming quadrilateral q1 element approximation of the navier stokes equation by using the so called velocity pressure mixed formulation a convergence condition is presented for the nonconforming stokes element it is shown that the condition is not only sufficient for the convergence but also in some sense is necessary the quasi optimal maximum norm error estimates of the velocity and its first derivatives and of the pressure are derived for the nonconforming quadrilateral q1 element for the stationary navier stokes problem the analysis is based on the weighted inf sup condition and the technique of weighted sobolev norm

an efficient nfem for optimal control problems governed by a bilinear state equation abstract in this paper a nonconforming finite element method nfem is proposed for the constrained optimal control problems ocps governed by a bilinear state equation the state and adjoint state are approximated by the nonconforming e q 1 r o t element and the control is approximated by the orthogonal projection through the state and adjoint state some superclose and superconvergence properties are obtained by full use of the distinguish characters of this e q 1 r o t element such as the interpolation operator equals the ritz projection and the consistency error is one order higher than its interpolation error in the broken energy norm finally some numerical results are provided to verify the theoretical analysis

controlling the false discovery rate a practical and powerful approach to multiple testing summary the common approach to the multiplicity problem calls for controlling the familywise error rate fwer this approach though has faults and we point out a few a different approach to problems of multiple significance testing is presented it calls for controlling the expected proportion of falsely rejected hypotheses the false discovery rate this error rate is equivalent to the fwer when all hypotheses are true but is smaller otherwise therefore in problems where the control of the false discovery rate rather than that of the fwer is desired there is potential for a gain in power a simple sequential bonferronitype procedure is proved to control the false discovery rate for independent test statistics and a simulation study shows that the gain in power is substantial the use of the new procedure and the appropriateness of the criterion are illustrated with examples

the output sinr of the linearly constrained beamformer the authors derive expressions of the output signal to interference plus noise ratio sinr of the linearly constrained narrowband beamformer in noncoherent situations using a vector approach numerical results are included

outlier detection and false discovery rates for whole genome dna matching we define a statistic called the matching statistic for locating regions of the genome that exhibit excess similarity among cases when compared to controls such regions are reasonable candidates for harboring disease genes we find the asymptotic distribution of the statistic while accounting for correlations among sampled individuals we then use the benjamini and hochberg false discovery rate fdr method for multiple hypothesis testing to find regions of excess sharing the p values for each region involve estimated nuisance parameters under appropriate conditions we show that the fdr method based on p values and with estimated nuisance parameters asymptotically preserves the fdr property finally we apply the method to a pilot study on schizophrenia

sophisticated merging over random partitions a scalable and robust causal discovery approach scalable causal discovery is an essential technology to a wide spectrum of applications including biomedical studies and social network evolution analysis to tackle the difficulty of high dimensionality a number of solutions are proposed in the literature generally dividing the original variable domain into smaller subdomains by computation intensive partitioning strategies these approaches usually suffer significant structural errors when the partitioning strategies fail to recognize true causal edges across the output subdomains such a structural error accumulates quickly with the growing depth of recursive partitioning due to the lack of correction mechanism over causally connected variables when they are wrongly divided into two subdomains finally jeopardizing the robustness of the integrated results this paper proposes a completely different strategy to solve the problem powered by a lightweight random partitioning scheme together with a carefully designed merging algorithm over results from the random partitions based on the randomness properties of the partitioning scheme we design a suite of tricks for the merging algorithm in order to support propagation based significance enhancement maximal acyclic subgraph causal ordering and order sensitive redundancy elimination theoretical studies as well as empirical evaluations verify the genericity effectiveness and scalability of our proposal on both simulated and real world causal structures when the scheme is used in combination with a variety of causal solvers known effective on smaller domains