sufficient dimension reduction via squared loss mutual information estimation the goal of sufficient dimension reduction in supervised learning is to find the low dimensional subspace of input features that contains all of the information about the output values that the input features possess in this letter we propose a novel sufficient dimension reduction method using a squared loss variant of mutual information as a dependency measure we apply a density ratio estimator for approximating squared loss mutual information that is formulated as a minimum contrast estimator on parametric or nonparametric models since cross validation is available for choosing an appropriate model our method does not require any prespecified structure on the underlying distributions we elucidate the asymptotic bias of our estimator on parametric models and the asymptotic convergence rate on nonparametric models the convergence analysis utilizes the uniform tail bound of a u process and the convergence rate is characterized by the bracketing entropy of the model we then develop a natural gradient algorithm on the grassmann manifold for sufficient subspace search the analytic formula of our estimator allows us to compute the gradient efficiently numerical experiments show that the proposed method compares favorably with existing dimension reduction approaches on artificial and benchmark data sets

algorithm for direct numerical simulation of emulsion flow through a granular material a multipole accelerated 3d boundary integral algorithm capable of modelling an emulsion flow through a granular material by direct multiparticle multidrop simulations in a periodic box is developed and tested the particles form a random arrangement at high volume fraction rigidly held in space including the case of an equilibrium packing in mechanical contact deformable drops with non deformed diameter comparable with the particle size squeeze between the particles under a specified average pressure gradient the algorithm includes recent boundary integral desingularization tools especially important for drop solid and drop drop interactions the hebeker representation for solid particle contributions and unstructured surface triangulations with fixed topology multipole acceleration with two levels of mesh node decomposition entire drop solid surfaces and patches is a significant improvement over schemes used in previous purely multidrop simulations it remains efficient at very high resolutions 10 4 10 5 triangular elements per surface and has no lower limitation on the number of particles or drops such resolutions are necessary in the problem to alleviate lubrication difficulties especially for near critical squeezing conditions as well as using 10 4 time steps and an iterative solution at each step both for contrast and matching viscosities examples are shown for squeezing of 25 40 drops through an array of 9 14 solids with the total volume fraction of 70 for particles and drops the flow rates for the drop and continuous phases are calculated extensive convergence testing with respect to program parameters triangulation multipole truncation etc is made

adaptive unstructured volume remeshing ii application to two and three dimensional level set simulations of multiphase flow in part i adaptive unstructured volume remeshing i the method j comput phys in press we presented an adaptive remeshing algorithm that automatically adjusts the size of the elements of meshes of unstructured triangles 2d and unstructured tetrahedra 3d with time and position in the computational domain in order to efficiently resolve the relevant physical scales here we illustrate the performance of an implementation of the algorithm in finite element level set simulations of deformable droplet and fluid fluid interface interactions breakup and coalescence in multiphase flows the wide range of length scales characterizing the dynamics are accurately resolved as demonstrated by comparison to experiments and to theoretical and sharp interface boundary integral numerical results the computational cost is found to be competitive even with respect to boundary integral methods for the first time using an interface capturing level set method we successfully simulate the inertia driven impact and rebound of a liquid droplet from a liquid interface and find agreement with recent experimental results

detecting the presence of mixing with multiscale maximum likelihood a test of homogeneity tries to decide whether observations come from a single distribution or from a mixture of several distributions a powerful theory has been developed for the case where the component distributions are members of an exponential family when no parametric assumptions are appropriate the standard approach is to test for bimodality which is known not to be very sensitive for detecting heterogeneity to develop a more sensitive procedure this article builds on an approach employed in sampling literature and models the component distributions as logarithmically concave densities it is shown how this leads to a special semiparametric model in which the homogeneity problem is equivalent to testing whether a parameter c equals zero versus the alternative that c 0 this setup leads naturally to a novel multiscale maximum likelihood procedure where the multiscale character reflects the desirable property of adaptivity to the unknown value of the parameter c under the alternative to en

jump discontinuities of semilinear strictly hyperbolic systems in two variables creation and propagation the creation and propagation of jump discontinuities in the solutions of semilinear strictly hyperbolic systems is studied in the case where the initial data has a discrete set xi i 1n of jump discontinuities lets be the smallest closed set which satisfies i s is a union of forward characteristics ii s contains all the forward characteristics from the points xi i 1n iii if two forward characteristics ins intersect then all forward characteristics from the point of intersection lie ins

propagation of singularities for semilinear hyperbolic initial boundary value problems in one space dimension on etudie la propagation et la reflexion de la regularite pour des systemes semi lineaires strictement hyperboliques a 1 variable d espace

global risk bounds and adaptation in univariate convex regression we consider the problem of nonparametric estimation of a convex regression function phi 0 we study the risk of the least squares estimator lse under the natural squared error loss we show that the risk is always bounded from above by n 4 5 modulo logarithmic factors while being much smaller when phi 0 is well approximable by a piecewise affine convex function with not too many affine pieces in which case the risk is at most 1 n up to logarithmic factors on the other hand when phi 0 has curvature we show that no estimator can have risk smaller than a constant multiple of n 4 5 in a very strong sense by proving a local minimax lower bound we also study the case of model misspecification where we show that the lse exhibits the same global behavior provided the loss is measured from the closest convex projection of the true regression function in the process of deriving our risk bounds we prove new results for the metric entropy of local neighborhoods of the space of univariate convex functions these results which may be of independent interest demonstrate the non uniform nature of the space of univariate convex functions in sharp contrast to classical function spaces based on smoothness constraints

an efficient algorithm for calculating multiparticle thermal interaction in a concentrated dispersion of spheres abstract the boundary value problem of heat conduction through a multiparticle system in a medium of a different conductivity e is considered the particle set is a random arrangement of n equisized spheres of conductivity in a cubic cell continued periodically into all space the mean temperature gradient is given the problem is reduced to an infinite set of equations for the coefficients of the temperature expansion into spherical harmonics on the interfaces the algorithm exploits the idea that only the interaction of the low frequency harmonics is long ranged to construct the economical truncation as well as the rotational transformations of spherical harmonics to promote the efficient iterative solution the method is capable of providing high resolution for arbitrary conductivity ratio e and enabling large scale simulations of the effective conductivity of highly concentrated dispersions with various microstructures even on a small computer

a fast algorithm for simulating vesicle flows in three dimensions vesicles are locally inextensible fluid membranes that can sustain bending in this paper we extend the study of veerapaneni et al s k veerapaneni d gueyffier g biros d zorin a numerical method for simulating the dynamics of 3d axisymmetric vesicles suspended in viscous flows journal of computational physics 228 19 2009 7233 7249 to general non axisymmetric vesicle flows in three dimensions although the main components of the algorithm are similar in spirit to the axisymmetric case spectral approximation in space semi implicit time stepping scheme important new elements need to be introduced for a full 3d method in particular spatial quantities are discretized using spherical harmonics and quadrature rules for singular surface integrals need to be adapted to this case an algorithm for surface reparameterization is needed to ensure stability of the time stepping scheme and spectral filtering is introduced to maintain reasonable accuracy while minimizing computational costs to characterize the stability of the scheme and to construct preconditioners for the iterative linear system solvers used in the semi implicit time stepping scheme we perform a spectral analysis of the evolution operator on the unit sphere by introducing these algorithmic components we obtain a time stepping scheme that circumvents the stability constraint on the time step and achieves spectral accuracy in space we present results to analyze the cost and convergence rates of the overall scheme to illustrate the applicability of the new method we consider a few vesicle flow interaction problems a single vesicle in relaxation sedimentation shear flows and many vesicle flows

on estimating a density using hellinger distance and some other strange facts on s interesse ici aux possibles vitesses d estimation d une densite a support compact dans m sous des hypotheses de regularite lorsque la perte est mesuree par le carre de la distance de hellinger on regardera aussi le cas connu des normes mathbb l q pour 1 q 2 et le risque est le risque minimax sur la famille on donne une methode generale permettant de traiter les problemes dans le cadre de la theorie de l approximation sous des conditions concernant l entropie metrique et l e capacite des familles a estimer les rapports entre regularite et entropie metrique etant bien connus nous pourrons aussi traiter les cas classiques et d autres qui le sont moins sous des conditions de bornes inferieures les vitesses sont celles observees pour la norme mathbb l q mais elles different dans le cas general on montre aussi que les restrictions sur la compacite du support ou la regularite sont indispensables et que leur absence mene a l impossibilite d obtenir une estimation raisonnable en ce sens que n importe quelle suite d estimateurs sera arbitrairement mauvaise en un point au moins un resultat analogue est vrai sous des conditions de regularite

maximum likelihood estimation of a multi dimensional log concave density fn is shown to have smaller mean integrated squared error compared with kernel based methods even when we allow the use of a theoret ical optimal fixed bandwidth for the kernel estimator that would not be available in practice we also present a real data clustering example which shows that our methodology can be used in conjunction with the expectation maximization algorithm to fit finite mixtures of log concave densities

the tight constant in the dvoretzky kiefer wolfowitz inequality let hat f n denote the empirical distribution function for a sample of n i i d random variables with distribution function f in 1956 dvoretzky kiefer and wolfowitz proved that p big sqrt n sup x hat f n x f x lambda big leq c exp 2 lambda 2 where c is some unspecified constant we show that c can be taken as 1 as conjectured by birnbaum and mccarty in 1958 provided that exp 2 lambda 2 leq frac 1 2 in particular the two sided inequality p big sqrt n sup x hat f n x f x lambda big leq 2 exp 2 lambda 2 holds without any restriction on lambda in the one sided as well as in the two sided case the constants cannot be further improved

rates of convergence for minimum contrast estimators we shall present here a general study of minimum contrast estimators in a nonparametric setting although our results are also valid in the classical parametric case for independent observations these estimators include many of the most popular estimators in various situations such as maximum likelihood estimators least squares and other estimators of the regression function estimators for mixture models or deconvolution the main theorem relates the rate of convergence of those estimators to the entropy structure of the space of parameters optimal rates depending on entropy conditions are already known at least for some of the models involved and they agree with what we get for minimum contrast estimators as long as the entropy counts are not too large but under some circumstances large entropies or changes in the entropy structure due to local perturbations the resulting the rates are only suboptimal counterexamples are constructed which show that the phenomenon is real for non parametric maximum likelihood or regression this proves that under purely metric assumptions our theorem is optimal and that minimum contrast estimators happen to be suboptimal

risk bounds in isotonic regression nonasymptotic risk bounds are provided for maximum likelihood type isotonic estimators of an unknown nondecreasing regression function with general average loss at design points these bounds are optimal up to scale constants and they imply uniform n 1 3 consistency of the ell p risk for unknown regression functions of uniformly bounded variation under mild assumptions on the joint probability distribution of the data with possibly dependent observations

some inequalities for information divergence and related measures of discrimination inequalities which connect information divergence with other measures of discrimination or distance between probability distributions are used in information theory and its applications to mathematical statistics ergodic theory and other scientific fields we suggest new inequalities of this type often based on underlying identities as a consequence we obtain certain improvements of the well known pinsker inequality our study depends on two measures of discrimination called capacitory discrimination and triangular discrimination the discussion contains references to related research and comparison with other measures of discrimination e g ali silvey csiszar 1996 1966 divergences and in particular the hellinger distance

approximations of semicontinuous functions with applications to stochastic optimization and statistical estimation upper semicontinuous usc functions arise in the analysis of maximization problems distributionally robust optimization and function identification which includes many problems of nonparametric statistics we establish that every usc function is the limit of a hypo converging sequence of piecewise affine functions of the difference of max type and illustrate resulting algorithmic possibilities in the context of approximate solution of infinite dimensional optimization problems in an effort to quantify the ease with which classes of usc functions can be approximated by finite collections we provide upper and lower bounds on covering numbers for bounded sets of usc functions under the attouch wets distance the result is applied in the context of stochastic optimization problems defined over spaces of usc functions we establish confidence regions for optimal solutions based on sample average approximations and examine the accompanying rates of convergence examples from nonparametric statistics illustrate the results

donsker type theorems for nonparametric maximum likelihood estimators let mathcal p be a nonparametric probability model consisting of smooth probability densities and let hat p n be the corresponding maximum likelihood estimator based on n independent observations each distributed according to the law mathbb p with hat mathbb p n denoting the measure induced by the density hat p n define the stochastic process hat nu n f longmapsto sqrt n int fd hat mathbb p n mathbb p where f ranges over some function class mathcal f we give a general condition for donsker classes mathcal f implying that the stochastic process hat nu n is asymptotically equivalent to the empirical process in the space ell infty mathcal f of bounded functions on mathcal f this implies in particular that hat nu n converges in law in ell infty mathcal f to a mean zero gaussian process we verify the general condition for a large family of donsker classes mathcal f we give a number of applications convergence of the probability measure hat mathbb p n to mathbb p at rate sqrt n in certain metrics metrizing the topology of weak star convergence a unified treatment of convergence rates of the mle in a continuous scale of sobolev norms sqrt n efficient estimation of nonlinear functionals defined on mathcal p limit theorems at rate sqrt n for the maximum likelihood estimator of the convolution product mathbb p ast p

concentration inequalities and asymptotic results for ratio type empirical processes let be a class of measurable functions on a measurable space s mathcal s with values in 0 1 and let pn n 1 i 1n xi be the empirical measure based on an i i d sample x1 xn from a probability distribution p on s mathcal s we study the behavior of suprema of the following type sup r n textless sigma p f leq delta n frac p n f pf phi sigma p f where pf var1 2pf and is a continuous strictly increasing function with 0 0 using talagrand s concentration inequality for empirical processes we establish concentration inequalities for such suprema and use them to derive several results about their asymptotic behavior expressing the conditions in terms of expectations of localized suprema of empirical processes we also prove new bounds for expected values of sup norms of empirical processes in terms of the largest pf and the l2 p norm of the envelope of the function class which are especially suited for estimating localized suprema with this technique we extend to function classes most of the known results on ratio type suprema of empirical processes including some of alexander s results for vc classes of sets we also consider applications of these results to several important problems in nonparametric statistics and in learning theory including general excess risk bounds in empirical risk minimization and their versions for l2 regression and classification and ratio type bounds for margin distributions in classification

an efficient algorithm for hydrodynamical interaction of many deformable drops an efficient and accurate 3d algorithm for dynamical simulations of many deformable drops with strong hydrodynamical interactions at zero reynolds numbers is developed the drop to medium viscosity ratio and the bond number are arbitrary and the drops are subject to gravity with stationary triply periodic boundary conditions the algorithm at each step is a hybrid of boundary integral and economical multipole techniques with extensive use of rotational transformations and economical truncation of multipole expansions to optimize near field interactions a significant part of the code is the new best paraboloid spline technique for calculating the normal vectors and curvatures on drop surfaces which greatly improves the quality of long time simulations examples show the phenomenon of clustering in a concentrated sedimenting emulsion for 0 25 and 1 which leads to an increase in the average sedimentation velocity with time a high efficiency of the method is demonstrated with two orders of magnitude gains over the standard o n2n2 boundary integral technique for n 102 drops in a periodic cell with n 103 triangular boundary elements per drop so that typical long time dynamical simulations can be performed in a few days or weeks on a standard workstation as compared to the several years which would be required for the same simulations using standard boundary integral techniques the effects of drop triangulation and truncation of multipole expansions on dynamical simulations are assessed

a central limit theorem under metric entropy with l_2 bracketing let mathbf s rho be a metric space mathbf v mathscr v mu be a probability space and f mathbf s times mathbf v rightarrow mathbb r be a real valued function on mathbf s times mathbf v which has mean zero and is lipschitz in l 2 mu with respect to rho let v be a random variable defined on mathbf v mathscr v mu and let v i i geq 1 be a sequence of independent copies of v the limiting behavior of the process s n s n 1 2 sum n i 1 f s v i is studied under an integrability condition on the metric entropy with bracketing in l 2 mu this metric entropy condition is analogous to one which implies the continuity of the limiting gaussian process a tightness result is derived which in conjunction with the results of andersen and dobric 1987 shows that a central limit theorem holds for the s n process this result generalizes those of dudley 1978 dudley 1981 and jain and marcus 1975

penalized quasi likelihood estimation in partial linear models consider a partial linear model where the expectation of a random variable y depends on covariates x z through f theta 0 x m 0 z with theta 0 an unknown parameter and m 0 an unknown function we apply the theory of empirical processes to derive the asymptotic properties of the penalized quasi likelihood estimator

optimal aggregation of classifiers in statistical learning classification can be considered as nonparametric estimation of sets where the risk is defined by means of a specific distance between sets associated with misclassification error it is shown that the rates of convergence of classifiers depend on two parameters the complexity of the class of candidate sets and the margin parameter the dependence is explicitly given indicating that optimal fast rates approaching o n 1 can be attained where n is the sample size and that the proposed classifiers have the property of robustness to the margin the main result of the paper concerns optimal aggregation of classifiers we suggest a classifier that automatically adapts both to the complexity and to the margin and attains the optimal fast rates up to a logarithmic factor

smooth discrimination analysis ties f and g can be based on the estimation of the set g x fx gx we consider applications where it is appropriate to assume that the region g has a smooth boundary or belongs to another nonparametric class of sets in particular this assumption makes sense if discrimination is used as a data analytic tool decision rules based on minimization of empirical risk over the whole class of sets and over sieves are considered their rates of convergence are obtained we show that these rules achieve optimal rates for estimation of g and optimal rates of convergence for bayes risks an interesting conclusion is that the optimal rates for bayes risks can be very fast in particular faster than the parametric root n rate these fast rates cannot be guaranteed for plug in rules

sharp rates of convergence of maximum likelihood estimators in nonparametric models sharp rates of convergence of maximum likelihood estimators are established in models which are defined by probability densities having bounded derivatives this result is achieved by making use of local properties of the empirical distribution function

exponential inequalities for martingales with application to maximum likelihood estimation for counting processes we obtain an exponential probability inequality for martingales and a uniform probability inequality for the process gdn where n is a counting process and where g varies within a class of predictable functions g for the latter we use techniques from empirical process theory the uniform inequality is shown to hold under certain entropy conditions on g as an application we consider rates of convergence for nonparametric maximum likelihood estimators for counting processes a similar result for discrete time observations is also presented

an adaptive mesh algorithm for evolving surfaces simulation of drop breakup and coalescence abstract an algorithm is presented for the adaptive restructuring of meshes on evolving surfaces the resolution of the relevant local length scale is maintained everywhere with prescribed accuracy through the minimization of an appropriate mesh energy function by a sequence of local restructuring operations the resulting discretization depends on the instantaneous configuration of the surface but is insensitive to the deformation history application of the adaptive discretization algorithm is illustrated with three dimensional boundary integral simulations of deformable drops in stokes flow the results show that the algorithm can accurately resolve detailed features of deformed fluid interfaces including slender filaments associated with drop breakup and dimpled regions associated with drop coalescence our algorithm should be useful in a variety of fields including computational fluid dynamics image processing geographical information systems and biomedical engineering problems

on profile likelihood abstract we show that semiparametric profile likelihoods where the nuisance parameter has been profiled out behave like ordinary likelihoods in that they have a quadratic expansion in this expansion the score function and the fisher information are replaced by the efficient score function and efficient fisher information the expansion may be used among others to prove the asymptotic normality of the maximum likelihood estimator to derive the asymptotic chi squared distribution of the log likelihood ratio statistic and to prove the consistency of the observed information as an estimator of the inverse of the asymptotic variance

approximation dans les espaces metriques et theorie de l estimation we investigate the relations between the speed of estimation and the metric structure of the parameter space especially in the case when its metric dimension is infinite given some distance d on generally hellinger distance in the case of n i i d variables we consider the minimax risk for n observations r n q mathop inf sup limits t n theta in theta text ie theta d q theta t n t n being any estimate of we shall look for functions r such that for positive constants c 1 q and c 2 q c 1 r q n r n q c2 r q n r n is the speed of estimation and we shall show under fairly general conditions including i i d variables and regular cases of markov chains and stationnary gaussian processes that r n is determined up to multiplicative constants by the metric structure of we shall also give a construction for some sort of universal estimates the risk of which is bounded by c 2 r q n in all cases where the preceding theory applies

the grenader estimator a nonasymptotic approach on examine certaines proprietes non asymtotiques de l estimateur de grenander d une densite decroissante f cet estimation est defini comme la pente du plus petit majorant concave de la fonction de repartition empirique on montre que son risque mesure avec perte l 1 est borne par une certaine fonctionnelle qui depend de f et du nombre d observations n le comportement asymptotique de la fonctionnelle quand n tend vers l infini est en accord avec les performances asymptotiques connues de l estimation de grenander

asymptotic normality of semiparametric and nonparametric posterior distributions in this article it is shown that the marginal semiparametric and nonparametric posterior distributions for a parameter of interest behave like an ordinary parametric posterior distribution this in practice provides support of the utility of marginal semiparametric and nonparametric posterior distributions in particular the marginal semiparametric and nonparametric posterior distributions are asymptotically normal and centered at the corresponding maximum likelihood estimates mles or posterior means with covariance matrix the inverse of the fisher information additionally the semiparametric and nonparametric mles for the parameter of interest and the marginal posterior means are asymptotically normal and centered at the true parameter with the same covariance matrix the results are a semiparametric version and a nonparametric version of the parametric bayesian central limit theorem that establish a connection between the semiparametric and the nonparametric bayesian inference and their frequentis

a parallel fast multipole accelerated integral equation scheme for 3d stokes equations in this paper we discuss a numerical scheme for the stokes equations in three dimensions it uses an integral equation formulation and is accelerated by the new version of fast multipole method first introduced by greengard and rokhlin in 1997 acta numerica 1997 6 229 269 the code is parallelized to solve problems of extremely large size the resulting numerical solver can be applied to stokes flows in complex geometry and also serves as a building block for solving the navier stokes equations of low to moderate reynold s numbers copyright 2006 john wiley sons ltd

propagation of singularities in one dimensional thermoelasticity the propagation of singularities for the system of homogeneous thermoelasticity in one space dimension is studied linear and a class of semilinear cauchy problems are considered

periodic conduction problems the fast multipole method and convergence of integral equations and lattice sums this paper presents a version of the fast multipole method fmm for integral equations describing conduction through three dimensional periodic heterogeneous media the proposed method is based on the standard rather than periodic fundamental solution and therefore it is very close to the original fmm in deriving the method particular attention is paid to convergence of arising integral equations and lattice sums it is shown that convergence can be achieved without introducing artificial compensatory sources or boundary conditions

independent component analysis via nonparametric maximum likelihood estimation independent component analysis ica models are very popular semiparametric models in which we observe independent copies of a random vector x as where a is a non singular matrix and s has independent components we propose a new way of estimating the unmixing matrix w a 1 and the marginal distributions of the components of s using nonparametric maximum likelihood specifically we study the projection of the empirical distribution onto the subset of ica distributions having log concave marginals we show that from the point of view of estimating the unmixing matrix it makes no difference whether or not the log concavity is correctly specified the approach is further justified by both theoretical results and a simulation study

a spectral boundary integral method for flowing blood cells a spectral boundary integral method for simulating large numbers of blood cells flowing in complex geometries is developed and demonstrated the blood cells are modeled as finite deformation elastic membranes containing a higher viscosity fluid than the surrounding plasma but the solver itself is independent of the particular constitutive model employed for the cell membranes the surface integrals developed for solving the viscous flow and thereby the motion of the massless membrane are evaluated using an o nlogn particle mesh ewald pme approach the cell shapes which can become highly distorted under physiologic conditions are discretized with spherical harmonics the resolution of these global basis functions is of course excellent but more importantly they facilitate an approximate de aliasing procedure that stabilizes the simulations without adding any numerical dissipation or further restricting the permissible numerical time step complex geometry no slip boundaries are included using a constraint method that is coupled into an implicit system that is solved as part of the time advancement routine the implementation is verified against solutions for axisymmetric flows reported in the literature and its accuracy is demonstrated by comparison against exact solutions for relaxing surface deformations it is also used to simulate flow of blood cells at 30 volume fraction in tubes between 4 9 and email protected in diameter for these it is shown to reproduce the well known non monotonic dependence of the effective viscosity on the tube diameter

statistical estimation and optimal recovery new formulas are given for the minimax linear risk in estimating a linear functional of an unknown object from indirect data contaminated with random gaussian noise the formulas cover a variety of loss functions and do not require the symmetry of the convex a priori class it is shown that affine minimax rules are within a few percent of minimax even among nonlinear rules for a variety of loss functions it is also shown that difficulty of estimation is measured by the modulus of continuity of the functional to be estimated the method of proof exposes a correspondence between minimax affine estimates in the statistical estimation problem and optimal algorithms in the theory of optimal recovery

semiparametric likelihood ratio inference likelihood ratio tests and related confidence intervals for a real parameter in the presence of an infinite dimensional nuisance parameter are considered in all cases the estimator of the real parameter has an asymptotic normal distribution however the estimator of the nuisance parameter may not be asymptotically gaussian or may converge to the true parameter value at a slower rate than the square root of the sample size nevertheless the likelihood ratio statistic is shown to possess an asymptotic chi squared distribution the examples considered are tests concerning survival probabilities based on doubly censored data a test for presence of heterogeneity in the gamma frailty model a test for significance of the regression coefficient in cox s regression model for current status data and a test for a ratio of hazards rates in an exponential mixture model in both of the last examples the rate of convergence of the estimator of the nuisance parameter is less than the square root of the sample size

law of the iterated logarithm for set indexed partial sum processes with finite variance is relatively compact in the uniform topology and the set of limit points is the set of functions which are absolutely continuous and whose derivatives have l 2 norm less than or equal to 1 using skorokhod embedding strassen showed that random walks also obeyed a functional lil and that the usual lil as well as many other interesting results could be obtained as a consequence the extension of strassen s results to brownian motion indexed by points in and to partial sum processes indexed by points in ir was done by pyke 7 and wichura 9 respectively for a brownian motion indexed by a large collection d of sets the functional law was proved by bass and pyke 2 and then a type of skorokhod embedding was used to extend this for partial sum processes however if h 5 is the log entropy of d defined in sect 2 it was necessary that h 6 0 moreover the sets in d had to have uniformly smooth boundaries morrow and philipp 6j were able to wea

nonparametric estimation via empirical risk minimization a general notion of universal consistency of nonparametric estimators is introduced that applies to regression estimation conditional median estimation curve fitting pattern recognition and learning concepts general methods for proving consistency of estimators based on minimizing the empirical error are shown in particular distribution free almost sure consistency of neural network estimates and generalized linear estimators is established

discussion local rademacher complexities and oracle inequalities in risk minimization in this magnificent paper professor koltchinskii offers general and powerful performance bounds for empirical risk minimization a fundamental principle of statistical learning theory since the elegant pioneering work of vapnik and chervonenkis in the early 1970s various such bounds have been known that relate the performance of empirical risk minimizers to combinatorial and geometrical features of the class over which the minimization is performed this area of research has been a rich source of motivation and a major field of applications of empirical process theory the appearance of advanced concentration inequalities in the 1990s primarily thanks to talagrand s influential work provoked major advances in both empirical process theory and statistical learning theory and led to a much deeper understanding of some of the basic phenomena in the discussed paper professor koltchinskii develops a powerful new methodology iterative localization which with the help of concentration inequalities is able to explain most of the recent results and go significantly beyond them in many cases the main motivation behind professor koltchinskii s paper is based on classical problems of statistical learning theory such as binary classification and regression in which given a sample xi y i i 1 n of independent and identically distributed pairs of random variables where the xi take their values in some feature space x and the yi are say real valued the goal is to find a function f x r whose risk defined in terms of the expected value of an appropriately chosen loss function is as small as possible in the remaining part of this discussion we point out how the performance bounds of professor koltchinskii s paper can be used to study a seemingly different model motivated by nonparametric ranking problems which has received increasing attention both in the statistical and machine learning literature indeed in several applications such as the search engine problem or credit risk screening the goal is to learn how to rank or to score observations rather than just classify them in this case performance measures involve pairs of observations as can be seen for instance with the auc area under an roc curve criterion in this

parametric estimation finite sample theory the paper aims at reconsidering the famous le cam lan theory the main features of the approach which make it different from the classical one are as follows 1 the study is nonasymptotic that is the sample size is fixed and does not tend to infinity 2 the parametric assumption is possibly misspecified and the underlying data distribution can lie beyond the given parametric family these two features enable to bridge the gap between parametric and nonparametric theory and to build a unified framework for statistical estimation the main results include large deviation bounds for the quasi maximum likelihood and the local quadratic bracketing of the log likelihood process the latter yields a number of important corollaries for statistical inference concentration confidence and risk bounds expansion of the maximum likelihood estimate etc all these corollaries are stated in a nonclassical way admitting a model misspecification and finite samples however the classical asymptotic results including the efficiency bounds can be easily derived as corollaries of the obtained nonasymptotic statements at the same time the new bracketing device works well in the situations with large or growing parameter dimension in which the classical parametric theory fails the general results are illustrated for the i i d setup as well as for generalized linear and median estimation the results apply for any dimension of the parameter space and provide a quantitative lower bound on the sample size yielding the root n accuracy

second kind integral equation formulation of stokes flows past a particle of arbitary shape the problem of determining the slow viscous flow of an unbounded fluid past a single solid particle is formulated exactly as a system of linear fredholm integral equations of the second kind for a

optimal rates of convergence for convex set estimation from support functions nitely many noisy measurements of its support function the solution is based on appropriate regularizations of the least squares estimator both xed and random designs are considered 1 introduction we study the nonparametric estimation problem of estimating a compact convex set in euclidean space from noisy support function measurements the support function hk of a compact convex subset k of r d d 2 is dened for u in the unit sphere s d 1 fx2 r d x 2 x 2 1g by

minimax risk over l p balls for l p error consider estimating the mean vector from datan n 2 i withl q norm loss q 1 when is known to lie in ann dimensionall p ball p 0 for largen the ratio of minimaxlinear risk to minimax risk can bearbitrarily large ifp q obvious exceptions aside the limiting ratio equals 1 only ifp q 2 our arguments are mostly indirect involving a reduction to a univariate bayes minimax problem whenp q simple non linear co ordinatewise threshold rules are asymptotically minimax at small signal to noise ratios and within a bounded factor of asymptotic minimaxity in general we also give asymptotic evaluations of the minimax linear risk our results are basic to a theory of estimation in besov spaces using wavelet bases to appear elsewhere

covering numbers for bounded variation functions abstract in this paper we provide upper and lower estimates for the minimal number of functions needed to represent a bounded variation function with an accuracy of epsilon with respect to l 1 distance

approximation by log concave distributions with applications to regression we study the approximation of arbitrary distributions p on d dimensional space by distributions with log concave density approximation means minimizing a kullback leibler type functional we show that such an approximation exists if and only if p has finite first moments and is not supported by some hyperplane furthermore we show that this approximation depends continuously on p with respect to mallows distance d 1 this result implies consistency of the maximum likelihood estimator of a log concave density under fairly general conditions it also allows us to prove existence and consistency of estimators in regression models with a response y x e where x and e are independent belongs to a certain class of regression functions while e is a random error with log concave density and mean zero

information theoretic upper and lower bounds for statistical estimation in this paper we establish upper and lower bounds for some statistical estimation problems through concise information theoretic arguments our upper bound analysis is based on a simple yet general inequality which we call the information exponential inequality we show that this inequality naturally leads to a general randomized estimation method for which performance upper bounds can be obtained the lower bounds applicable for all statistical estimators are obtained by original applications of some well known information theoretic inequalities and approximately match the obtained upper bounds for various important problems moreover our framework can be regarded as a natural generalization of the standard minimax framework in that we allow the performance of the estimator to vary for different possible underlying distributions according to a predefined prior

algorithm for random close packing of spheres with periodic boundary conditions the isotropic algorithm is constructed for random close packing of equisized spheres with triply periodic boundary conditions all previously published packing methods with periodic boundaries were kinetics determined i e they contained a densification rate as an arbitrary parameter in contrast the present algorithm is kinetic independent and demonstrates an unambiguous convergence to the experimental results to suppress crystallization the main principles of our algorithm are 1 to form a contact network at an early stage and 2 retain contacts throughout the densification as far as possible the particles are allowed to swell by the numerical solution of the differential equations of densification the rhs of these equations is calculated efficiently from a linear system by a combination of conjugate gradient iterations and exact sparse matrix technology when an excessive contact occurs and one of the existing bonds should be broken to continue the densification an efficient criterion based on multidimensional simplex geometry is used for searching the separating bond the algorithm has a well defined termination point resulting in a perfect contact network with the average coordination number six for particle number n 1 and a system of normal reactions between the spheres maintaining the structure these forces are the counterpart of the algorithm and can be used to calculate small elastic particle deformations in a granular medium extensive calculations are presented for 50 n 5 400 and demonstrate very good agreement with the experimental packing density about 0 637 and structure

global rates of convergence in log concave density estimation the estimation of a log concave density on mathbb r d represents a central problem in the area of nonparametric inference under shape constraints in this paper we study the performance of log concave density estimators with respect to global loss functions and adopt a minimax approach we first show that no statistical procedure based on a sample of size n can estimate a log concave density with respect to the squared hellinger loss function with supremum risk smaller than order n 4 5 when d 1 and order n 2 d 1 when d geq2 in particular this reveals a sense in which when d geq3 log concave density estimation is fundamentally more challenging than the estimation of a density with two bounded derivatives a problem to which it has been compared second we show that for d leq3 the hellinger varepsilon bracketing entropy of a class of log concave densities with small mean and covariance matrix close to the identity grows like max varepsilon d 2 varepsilon d 1 up to a logarithmic factor when d 2 this enables us to prove that when d leq3 the log concave maximum likelihood estimator achieves the minimax optimal rate up to logarithmic factors when d 2 3 with respect to squared hellinger loss

the central limit theorem and the law of iterated logarithm for empirical processes under local conditions a clt and a lil are proved under weak l2 gaussian bracketing conditions weaker than the usual ones these results have wide applicability and in particular provide an improvement of the jain marcus central limit theorem for c s valued random variables

global rates of convergence of the mles of log concave and s concave densities we establish global rates of convergence for the maximum likelihood estimators mles of log concave and s concave densities on the main finding is that the rate of convergence of the mle in the hellinger metric is no worse than n 2 5 when 1 s where s 0 corresponds to the log concave case we also show that the mle does not exist for the classes of s concave densities with s 1

existence and comparison results for operator and differential equations in abstract spaces in this paper we apply the chain methods developed in heikkila and lakshmikantham 1994 to obtain new fixed point theorems and new existence and comparison results for operator equations in partially ordered sets these results are then applied to discontinuous implicit functional differential equations in ordered banach spaces

a kernel independent adaptive fast multipole algorithm in two and three dimensions we present a new fast multipole method for particle simulations the main feature of our algorithm is that it does not require the implementation of multipole expansions of the underlying kernel and it is based only on kernel evaluations instead of using analytic expansions to represent the potential generated by sources inside a box of the hierarchical fmm tree we use a continuous distribution of an equivalent density on a surface enclosing the box to find this equivalent density we match its potential to the potential of the original sources at a surface in the far field by solving local dirichlet type boundary value problems the far field evaluations are sparsified with singular value decomposition in 2d or fast fourier transforms in 3d we have tested the new method on the single and double layer operators for the laplacian the modified laplacian the stokes the modified stokes the navier and the modified navier operators in two and three dimensions our numerical results indicate that our method compares very well with the best known implementations of the analytic fmm method for both the laplacian and modified laplacian kernels its advantage is the relative simplicity of the implementation and its immediate extension to more general kernels

propagation and reflection of regularity for semilinear hyperbolic 2x2 systems in one space dimension on etudie le transport de la regularite le long des courbes caracteristiques et la reflexion de la regularite aux limites pour des solutions regulieres par morceaux a des problemes aux valeurs limites et initiales pour des systemes strictement hyperboliques semilineaire 2 2 a deux variables

on methods of sieves and penalization we develop a general theory which provides a unified treatment for the asymptotic normality and efficiency of the maximum likelihood estimates mle s in parametric semiparametric and nonparametric models we find that the asymptotic behavior of substitution estimates for estimating smooth functionals are essentially governed by two indices the degree of smoothness of the functional and the local size of the underlying parameter space we show that when the local size of the parameter space is not very large the substitution standard nonsieve substitution sieve and substitution penalized mle s are asymptotically efficient in the fisher sense under certain stochastic equicontinuity conditions of the loglikelihood moreover when the convergence rate of the estimate is slow the degree of smoothness of the functional needs to compensate for the slowness of the rate in order to achieve efficiency when the size of the parameter space is very large the standard and penalized maximum likelihood procedures may be inefficient whereas the method of sieves may be able to overcome this difficulty this phenomenon is particularly manifested when the functional of interest is very smooth especially in the semiparametric case

how to use expert advice we analyze algorithms that predict a binary value by combining the predictions of several prediction strategies called experts our analysis is for worst case situations i e we make no assumptions about the way the sequence of bits to be predicted is generated we measure the performance of the algorithm by the difference between the expected number of mistakes it makes on the bit sequence and the expected number of mistakes made by the best expert on this sequence where the expectation is taken with respect to the randomization in the predictins we show that the minimum achievable difference is on the order of the square root of the number of mistakes of the best expert and we give efficient algorithms that achieve this our upper and lower bounds have matching leading constants in most cases we then show how this leads to certain kinds of pattern recognition learning algorithms with performance bounds that improve on the best results currently know in this context we also compare our analysis to the case in which log loss is used instead of the expected number of mistakes

strictly proper scoring rules prediction and estimation scoring rules assess the quality of probabilistic forecasts by assigning a numerical score based on the predictive distribution and on the event or value that materializes a scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distributionf if he or she issues the probabilistic forecast f rather than g f it is strictly proper if the maximum is unique in prediction problems proper scoring rules encourage the forecaster to make careful assessments and to be honest in estimation problems strictly proper scoring rules provide attractive loss and utility functions that can be tailored to the problem at hand this article reviews and develops the theory of proper scoring rules on general probability spaces and proposes and discusses examples thereof proper scoring rules derive from convex functions and relate to information measures entropy functions and bregman divergences in the case of categorical variables we prove a rigorous version of the

a front tracking method for viscous incompressible multi fluid flows abstract a method to simulate unsteady multi fluid flows in which a sharp interface or a front separates incompressible fluids of different density and viscosity is described the flow field is discretized by a conservative finite difference approximation on a stationary grid and the interface is explicitly represented by a separate unstructured grid that moves through the stationary grid since the interface deforms continuously it is necessary to restructure its grid as the calculations proceed in addition to keeping the density and viscosity stratification sharp the tracked interface provides a natural way to include surface tension effects both two and three dimensional full numerical simulations of bubble motion are presented

covering numbers for convex functions in this paper we study the covering numbers of the space of convex and uniformly bounded functions in multidimension we find optimal upper and lower bounds for the epsilon covering number of cal c a b d b in the l p metric 1 leq p in terms of the relevant constants where d geq 1 a b 0 and cal c a b d b denotes the set of all convex functions on a b d that are uniformly bounded by b we summarize previously known results on covering numbers for convex functions and also provide alternate proofs of some known results our results have direct implications in the study of rates of convergence of empirical minimization procedures as well as optimal convergence rates in the numerous convexity constrained function estimation problems

random sieve likelihood and general regression models abstract consider a semiparametric regression model y f x where f is a known function is an unknown vector consists of a random error and possibly of some unobserved variables and the distribution f of x is unspecified this article introduces in a general setting new methodology for estimating and f the proposed method constructs a profile likelihood defined on random level sets a random sieve the proposed method is related to empirical likelihood but is more generally applicable four examples are discussed including a quadratic model high dimensional semiparametric regression a nonparametric random effects model and linear regression with right censored data simulation results and asymptotic analysis support the utility and effectiveness of the proposed method

convex models mle and misspecification we analyze the asymptotic behavior of maximum likelihood estimators mle in convex dominated models when the true distribution generating the independent data does not necessarily belong to the model inspired by the hellinger distance and its properties we introduce a family of divergences contrast functions which allow a unified treatment of well and misspecified convex models convergence and rates of convergence of the mle with respect to our divergences are obtained from inequalities satisfied by these divergences and results from empirical process theory uniform laws of large numbers and maximal inequalities as a particular case we recover existing results for hellinger convergence of mle in well specified convex models four examples are considered mixtures of discrete distributions monotone densities decreasing failure rate distributions and a finite dimensional parametric model

convergence rates of posterior distributions we consider the asymptotic behavior of posterior distributions and bayes estimators for infinite dimensional statistical models we give general results on the rate of convergence of the posterior measure these are applied to several examples including priors on finite sieves log spline models dirichlet processes and interval censoring

microlocal analysis in nonlinear thermoelasticity abstract this paper is devoted to the study of the propagation of singularities for semilinear hyperbolic parabolic coupled systems in thermoelasticity first a linear transformation involving pseudodifferential operators for unknowns is introduced to decouple the hyperbolic and the parabolic operators by using the decoupled system we obtain that the semilinear system of thermoelasticity has finite speeds for the propagation of singularities furthermore it is proved that the microlocal singularities of solutions to this semilinear hyperbolic parabolic coupled system are propagated along null bicharacteristics of the hyperbolic operator by using the theory of nonsmooth pseudodifferential operators finally the cauchy problem for the semilinear system of thermoelasticity is investigated when the initial data have singularities

a front tracking method for the computations of multiphase flow direct numerical simulations of multiphase flows using a front tracking method are presented the method is based on writing one set of governing equations for the whole computational domain and treating the different phases as one fluid with variable material properties interfacial terms are accounted for by adding the appropriate sources as functions at the boundary separating the phases the unsteady navier stokes equations are solved by a conventional finite volume method on a fixed structured grid and the interface or front is tracked explicitly by connected marker points interfacial source terms such as surface tension are computed on the front and transferred to the fixed grid advection of fluid properties such as density is done by following the motion of the front the method has been implemented for fully three dimensional flows as well as for two dimensional and axisymmetric ones first the method is described for the flow of two or more isothermal phases the representation of the moving interface and its dynamic restructuring as well as the transfer of information between the moving front and the fixed grid are discussed applications and extensions of the method to homogeneous bubbly flows atomization flows with variable surface tension solidification and boiling are then presented

a multipole accelerated algorithm for close interaction of slightly deformable drops an efficient multipole accelerated boundary integral algorithm is developed to study close contact three dimensional interaction of two drops at zero reynolds numbers and very small but non zero capillary numbers ca when the drops are nearly spherical the numerical difficulties compared to the case of larger deformations include severe stability limitations on the time step and a singular perturbation for ca 1 requiring very high surface resolution both in the small gap and in the outer region the mesh triangle vertices are grouped into a large number of non overlapping patches with economical rotation based multipole reexpansions to handle patch to patch interactions and limit the use of expensive direct summations a novel concept of a dynamical projective mesh is developed to maintain fixed topology gap adaptive surface triangulations for o 10 5 boundary elements per drop in close contact the algorithm has at least an order of magnitude advantage over the standard boundary integral method making such dynamical calculations feasible in gravity induced and shear induced motion exact results are obtained for the dynamics of the surface clearance h m i n which attains values less than 0 001 of the drop radii and for the separation angle b s e p determining the configuration when two drops in apparent contact start to separate the shear flow problem is studied in the wide range of drop to medium viscosity ratios 0 25 0