chemotaxis with logistic source very weak global solutions and their boundedness properties abstract we consider the chemotaxis system u t u u v g u x t 0 0 v v u x t 0 in a smooth bounded domain r n where 0 and g generalizes the logistic function g u a u b u with 1 a 0 and b 0 a concept of very weak solutions is introduced and global existence of such solutions for any nonnegative initial data u 0 l 1 is proved under the assumption that 2 1 n moreover boundedness properties of the constructed solutions are studied inter alia it is shown that if b is sufficiently large and u 0 l has small norm in l for some n 2 then the solution is globally bounded finally in the case that additionally n 2 holds a bounded set in l can be found which eventually attracts very weak solutions emanating from arbitrary l 1 initial data the paper closes with numerical experiments that illustrate some of the theoretically established results

a bernstein von mises theorem for smooth functionals in semiparametric models a bernstein von mises theorem is derived for general semiparametric functionals the result is applied to a variety of semiparametric problems in i i d and non i i d situations in particular new tools are developed to handle semiparametric bias in particular for nonlinear functionals and in cases where regularity is possibly low examples include the squared l 2 norm in gaussian white noise nonlinear functionals in density estimation as well as functionals in autoregressive models for density estimation a systematic study of bvm results for two important classes of priors is provided namely random histograms and gaussian process priors

rates of convergence for bayes and maximum likelihood estimation for mixture of normal densities we study the rates of convergence of the maximum likelihood estimator mle and posterior distribution in density estimation problems where the densities are location or location scale mixtures of normal distributions with the scale parameter lying between two positive numbers the true density is also assumed to lie in this class with the true mixing distribution either compactly supported or having sub gaussian tails we obtain bounds for hellinger bracketing entropies for this class and from these bounds we deduce the convergence rates of sieve mles in hellinger distance the rate turns out to be log n n where k 1 is a constant that depends on the type of mixtures and the choice of the sieve next we consider a dirichlet mixture of normals as a prior on the unknown density we estimate the prior probability of a certain kullback leibler type neighborhood and then invoke a general theorem that computes the posterior convergence rate in terms the growth rate of the hellinger entropy and the concentration rate of the prior the posterior distribution is also seen to converge at the rate log n n in where k now depends on the tail behavior of the base measure of the dirichlet process

asymptotically autonomous semiflows chain recurrence and lyapunov functions from the work of c conley it is known that the omega limit set of a precompact orbit of an autonomous semiflow is a chain recurrent set here we improve a result of l markus by showing that the omega limit set of a solution of an asymptotically autonomous semiflow is a chain recurrent set relative to the limiting autonomous semiflow in the special case that there is a lyapunov function for the limiting semiflow sufficient conditions are given for an omega limit set of the asymptotically autonomous semiflow to be contained in a level set of the lyapunov function

convergence of latent mixing measures in finite and infinite mixture models this paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models using transportation distances i e wasserstein metrics the relationship between wasserstein distances on the space of mixing measures and f divergence functionals such as hellinger and kullback leibler distances on the space of mixture distributions is investigated in detail using various identifiability conditions convergence in wasserstein metrics for discrete measures implies convergence of individual atoms that provide support for the measures thereby providing a natural interpretation of convergence of clusters in clustering applications where mixture models are typically employed convergence rates of posterior distributions for latent mixing measures are established for both finite mixtures of multivariate distributions and infinite mixtures based on the dirichlet process

existence stability and bifurcation of random complete and periodic solutions of stochastic parabolic equations abstract in this paper we study the existence stability and bifurcation of random complete and periodic solutions for stochastic parabolic equations with multiplicative noise we first prove the existence and uniqueness of tempered random attractors for the stochastic equations and characterize the structures of the attractors by random complete solutions we then examine the existence and stability of random complete quasi solutions and establish the relations of these solutions and the structures of tempered attractors when the stochastic equations are incorporated with periodic forcing we obtain the existence and stability of random periodic solutions for the stochastic chafee infante equation we further establish the multiplicity and stochastic bifurcation of complete and periodic solutions

the semiparametric bernstein von mises theorem in a smooth semiparametric estimation problem the marginal posterior for the parameter of interest is expected to be asymptotically normal and satisfy frequentist criteria of optimality if the model is endowed with a suitable prior it is shown that under certain straightforward and interpretable conditions the assertion of le cam s acclaimed but strictly parametric bernstein von mises theorem univ california publ statist 1 1953 277 329 holds in the semiparametric situation as well as a consequence bayesian point estimators achieve efficiency for example in the sense of hajek s convolution theorem z wahrsch verw gebiete 14 1970 323 330 the model is required to satisfy differentiability and metric entropy conditions while the nuisance prior must assign nonzero mass to certain kullback leibler neighborhoods ghosal ghosh and van der vaart ann statist 28 2000 500 531 in addition the marginal posterior is required to converge at parametric rate which appears to be the most stringent condition in examples the results are applied to estimation of the linear coefficient in partial linear regression with a gaussian prior on a smoothness class for the nuisance

dirichlet process mixtures of generalized linear models we propose dirichlet process mixtures of generalized linear models dp glm a new class of methods for nonparametric regression given a data set of input response pairs the dp glm produces a global model of the joint distribution through a mixture of local generalized linear models dp glms allow both continuous and categorical inputs and can model the same class of responses that can be modeled with a generalized linear model we study the properties of the dp glm and show why it provides better predictions and density estimates than existing dirichlet process mixture regression models we give conditions for weak consistency of the joint distribution and pointwise consistency of the regression estimate

mixture models with a prior on the number of components abstracta natural bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric dirichlet weights and put a prior on the number of components that is to use a mixture of finite mixtures mfm the most commonly used method of inference for mfms is reversible jump markov chain monte carlo but it can be nontrivial to design good reversible jump moves especially in high dimensional spaces meanwhile there are samplers for dirichlet process mixture dpm models that are relatively simple and are easily adapted to new applications it turns out that in fact many of the essential properties of dpms are also exhibited by mfms an exchangeable partition distribution restaurant process random measure representation and stick breaking representation and crucially the mfm analogues are simple enough that they can be used much like the corresponding dpm properties consequently many of the powerful methods developed for inference in dpms

on bayesian supremum norm contraction rates building on ideas from castillo and nickl 6 a method is pro vided to study nonparametric bayesian posterior convergence rates when strong measures of distances such as the sup norm are considered in particular we show that likelihood methods can achieve optimal minimax sup norm rates in density estimation on the unit interval the introduced methodology is used to prove that commonly used families of prior distri butions on densities namely log density priors and dyadic random density histograms can indeed achieve optimal sup norm rates of convergence new results are also derived in the gaussian white noise model as a further il lustration of the presented techniques ams 2000 subject classications primary 62g20 secondary 62g05 62g07

globall estimates for a class of reaction diffusion systems abstract we show that a priori l 1 estimates dissipativity imply l estimates dissipativity for a class of reaction diffusion systems this generalizes the results obtained in biological models such as unmixed bioreactors plug flow models and lotka volterra systems

pullback permanence in a non autonomous competitive lotka volterra model abstract the goal of this work is to study in some detail the asymptotic behaviour of a non autonomous lotka volterra model both in the conventional sense as t and in the pullback sense starting a fixed initial condition further and further back in time the non autonomous terms in our model are chosen such that one species will eventually die out ruling out any conventional type of permanence in contrast we introduce the notion of pullback permanence and show that this property is enjoyed by our model this is not just a mathematical artifice but rather shows that if we come across an ecology that has been evolving for a very long time we still expect that both species are represented and their numbers are bounded below even if the final fate of one of them is less happy the main tools in the paper are the theory of attractors for non autonomous differential equations the sub supersolution method and the spectral theory for linear elliptic equations

posterior consistency of dirichlet mixtures in density estimation a dirichlet mixture of normal densities is a useful choice for a prior distribution on densities in the problem of bayesian density estimation in the recent years efficient markov chain monte carlo method for the computation of the posterior distribution has been developed the method has been applied to data arising from different fields of interest the important issue of consistency was however left open in this paper we settle this issue in affirmative

rates of contraction of posterior distributions based on gaussian process priors we derive rates of contraction of posterior distributions on nonparametric or semiparametric models based on gaussian processes the rate of contraction is shown to depend on the position of the true parameter relative to the reproducing kernel hilbert space of the gaussian process and the small ball probabilities of the gaussian process we determine these quantities for a range of examples of gaussian priors and in several statistical settings for instance we consider the rate of contraction of the posterior distribution based on sampling from a smooth density model when the prior models the log density as a fractionally integrated brownian motion we also consider regression with gaussian errors and smooth classification under a logistic or probit link function combined with various priors

application of an adams type inequality to a two chemical substances chemotaxis system abstract this paper deals with positive solutions of the fully parabolic system u t u u v in 0 1 v t v v w in 0 2 w t w w u in 0 under homogeneous neumann boundary conditions or mixed boundary conditions no flux and dirichlet conditions in a smooth bounded domain r n n 4 with positive parameters 1 2 0 and nonnegative smooth initial data u 0 v 0 w 0 in the lower dimensional case n 3 it is proved that for all reasonable initial data solutions of the system exist globally in time and remain bounded in the case n 4 it is shown that in the radially symmetric setting solutions to the neumann boundary value problem of the system exist globally in time and remain bounded if u 0 l 1 8 2 as to the mixed boundary value problem we will establish global existence and boundedness of solutions if u 0 l 1 8 2 without radial symmetry the key ingredients are a lyapunov functional and an adams type inequality a lyapunov functional of the above problems will be constructed and the constant 8 2 is deduced from the critical constant in the adams type inequality this result is regarded as a generalization of the well known 8 problem in the keller segel system to higher dimensions

bayesian regression tree ensembles that adapt to smoothness and sparsity ensembles of decision trees are a useful tool for obtaining flexible estimates of regression functions examples of these methods include gradient boosted decision trees random forests and bayesian classification and regression trees two potential shortcomings of tree ensembles are their lack of smoothness and their vulnerability to the curse of dimensionality we show that these issues can be overcome by instead considering sparsity inducing soft decision trees in which the decisions are treated as probabilistic we implement this in the context of the bayesian additive regression trees framework and illustrate its promising performance through testing on benchmark data sets we provide strong theoretical support for our methodology by showing that the posterior distribution concentrates at the minimax rate up to a logarithmic factor for sparse functions and functions with additive structures in the high dimensional regime where the dimensionality of the covariate space is allowed to grow nearly exponentially in the sample size our method also adapts to the unknown smoothness and sparsity levels and can be implemented by making minimal modifications to existing bayesian additive regression tree algorithms

rates of contraction for posterior distributions in lr metrics 1 r the frequentist behavior of nonparametric bayes estimates more specifically rates of contraction of the posterior distributions to shrinking lr norm neighborhoods 1 r of the unknown parameter are studied a theorem for nonparametric density estimation is proved under general approximation theoretic assumptions on the prior the result is applied to a variety of common examples including gaussian process wavelet series normal mixture and histogram priors the rates of contraction are minimax optimal for 1 r 2 but deteriorate as r increases beyond 2 in the case of gaussian nonparametric regression a gaussian prior is devised for which the posterior contracts at the optimal rate in all lr norms 1 r

misspecification in infinite dimensional bayesian statistics we consider the asymptotic behavior of posterior distributions if the model is misspecified given a prior distribution and a random sample from a distribution p 0 which may not be in the support of the prior we show that the posterior concentrates its mass near the points in the support of the prior that minimize the kullback leibler divergence with respect to p 0 an entropy condition and a prior mass condition determine the rate of convergence the method is applied to several examples with special interest for infinite dimensional models these include gaussian mixtures nonparametric regression and parametric models

on the lifting of elliptic cusp forms to siegel cusp forms of degree 2n i 1 cf 8 in this paper we will prove that their conjecture is true moreover we obtain a simple fourier coefficient formula for f z ibukiyama 23 also made a similar conjecture which was formulated in terms of the koecher maass series which we will not consider here let us describe our results throughout this paper we fix positive integers k and n such that k n mod 2 and put e 1 k 1 n for any positive rational number n we denote the absolute value of the discriminant of q vn q by dn the primitive dirichlet character corresponding to q v n q is denoted by xn let fn be the positive rational number such that n nf2nlet b e m2n q be a positive definite half integral symmetric matrix put db det 2b db d db fb f db and xb x db note that edb 0 1 mod 4 and that fb is an integer for each prime p let p qp cx be the unique additive character such that bp x exp 27ir ltx for x e z p 1 recall that the siegel series for b is defined by

random recurrent neural networks with delays abstract an infinite lattice model of a recurrent neural network with random connection strengths between neurons is developed and analyzed to incorporate the presence of various type of delays in the neural networks both discrete and distributed time varying delays are considered in the model for the existence of random pullback attractors and periodic attractors the nonlinear terms of the resulting system are not expected to be lipschitz continuous but only satisfy a weaker continuity assumption along with growth conditions under which the uniqueness of the underlying cauchy problem may not hold then after extending the concept and theory of monotone multi valued semiflows to the random context the structure of random pullback attractors with or without periodicity is investigated in particular the existence and stability properties of extremal random complete trajectories are studied

a semiparametric bernstein von mises theorem for gaussian process priors this paper is a contribution to the bayesian theory of semiparametric estimation we are interested in the so called bernstein von mises theorem in a semiparametric framework where the unknown quantity is f with the parameter of interest and f an infinite dimensional nuisance parameter two theorems are established one in the case with no loss of information and one in the information loss case with gaussian process priors the general theory is applied to three specific models the estimation of the center of symmetry of a symmetric function in gaussian white noise a time discrete functional data analysis model and cox s proportional hazards model in all cases the range of application of the theorems is investigated by using a family of gaussian priors parametrized by a continuous parameter

robust priors in nonlinear panel data models many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects fixed effects random effects and bayesian approaches all fall in this category we provide a characterization of the class of weights or priors that produce estimators that are first order unbiased we show that such bias reducing weights must depend on the data unless an orthogonal reparameterization or an essentially equivalent condition is available two intuitively appealing weighting schemes are discussed we argue that asymptotically valid confidence intervals can be read from the posterior distribution of the common parameters when n and t grow at the same rate finally we show that random effects estimators are not bias reducing in general and discuss important exceptions three examples and some monte carlo experiments illustrate the results

global asymptotic stability of constant equilibria in a fully parabolic chemotaxis system with strong logistic dampening abstract this paper deals with nonnegative solutions of the neumann initial boundary value problem for the parabolic chemotaxis system u t u u v u u 2 x t 0 v t v v u x t 0 in bounded convex domains r n n 1 with smooth boundary it is shown that if the ratio is sufficiently large then the unique nontrivial spatially homogeneous equilibrium given by u v 1 is globally asymptotically stable in the sense that for any choice of suitably regular nonnegative initial data u 0 v 0 such that u 0 0 the above problem possesses a uniquely determined global classical solution u v with u v t 0 u 0 v 0 which satisfies u t 1 l 0 and v t 1 l 0 as t

bayesian nonparametric calibration and combination of predictive distributions abstractwe introduce a bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights building on the work of ranjan and gneiting we use infinite beta mixtures for the calibration the proposed bayesian nonparametric approach takes advantage of the flexibility of dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions the inference procedure is based on combination gibbs and slice sampling we provide some conditions under which the proposed probabilistic calibration converges in terms of weak posterior consistency to the true underlying density for both cases of iid and markovian observations this calibration property improves upon the earlier calibration approaches we study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of dail

asymptotic behavior of the non autonomous 3d navier stokes problem with coercive force abstract we construct pullback attractors to the weak solutions of the three dimensional dirichlet problem for the incompressible navier stokes equations in the case when the external force may become unbounded as time goes to plus or minus infinity

probability inequalities for likelihood ratios and convergence rates of sieve mles let y 1 y n be independent identically distributed with density p 0 and let f be a space of densities we show that the supremum of the likelihood ratios i 1 n p y i p 0 y i where the supremum is over p f with p l 2 p 0 1 2 2 e is exponentially small with probability exponentially close to 1 the exponent is proportional to ne 2 the only condition required for this to hold is that e exceeds a value determined by the bracketing hellinger entropy of f a similar inequality also holds if we replace f by f n and p 0 by q n where q n is an approximation to p 0 in a suitable sense these results are applied to establish rates of convergence of sieve mles furthermore weak conditions are given under which the optimal rate e n defined by h e n f ne n 2 where h f is the hellinger entropy of f is nearly achievable by sieve estimators

rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density in this paper we investigate the asymptotic properties of nonparametric bayesian mixtures of betas for estimating a smooth density on 0 1 we consider a parametrization of beta distributions in terms of mean and scale parameters and construct a mixture of these betas in the mean parameter while putting a prior on this scaling parameter we prove that such bayesian nonparametric models have good frequentist asymptotic properties we determine the posterior rate of concentration around the true density and prove that it is the minimax rate of concentration when the true density belongs to a holder class with regularity for all positive leading to a minimax adaptive estimating procedure of the density we also believe that the approximating results obtained on these mixtures of beta densities can be of interest in a frequentist framework

attractors for non autonomous parabolic problems with singular initial data in this paper we study the asymptotic behavior of solutions of non autonomous parabolic problems with singular initial data we first establish the well posedness of the equation when the initial data belongs to lr 1 r and w1 r 1 r n respectively when the initial data belongs to lr we establish the existence of uniform attractors in lr for the family of processes with external forces being translation bounded but not translation compact in llocp r lr when we consider the existence of uniform attractors in h01 the solution of equation lacks the higher regularity so we introduce a new type of solution and prove the existence result for the long time behavior of solutions of the equation in w1 r we only obtain the uniform attracting property in the weak topology

on the long time behavior of non autonomous lotka volterra models with diffusion via the sub supertrajectory method abstract in this paper we study in detail the geometrical structure of global pullback and forwards attractors associated to non autonomous lotka volterra systems in all the three cases of competition symbiosis or prey predator in particular under some conditions on the parameters we prove the existence of a unique nondegenerate global solution for these models which attracts any other complete bounded trajectory thus we generalize the existence of a unique strictly positive stable stationary solution from the autonomous case and we extend to lotka volterra systems the result for scalar logistic equations to this end we present the sub supertrajectory tool as a generalization of the now classical sub supersolution method in particular we also conclude pullback and forwards permanence for the above models

bayesian modeling of joint and conditional distributions in this paper we study a bayesian approach to exible modeling of conditional distributions the approach uses a exible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution we use a nite mixture of multivariate normals fmmn to estimate the joint distribution the conditional distributions can then be assessed analytically or through simulations the discrete variables are handled through the use of latent variables the estimation procedure employs an mcmc algorithm we provide a characterization of the kullback leibler closure of fmmn and show that the joint and conditional predictive densities implied by fmmn model are consistent estimators for a large class of data generating processes with continuous and discrete observables the method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a bayesian alternative to semi and non parametric models such as quantile and kernel regression in experiments the method compares favorably with classical nonparametric and alternative bayesian methods

on choosing mixture components via non local priors choosing the number of mixture components remains an elusive challenge model selection criteria can be either overly liberal or conservative and return poorly separated components of limited practical use we formalize non local priors nlps for mixtures and show how they lead to well separated components with non negligible weight interpretable as distinct subpopulations we also propose an estimator for posterior model probabilities under local priors and nlps showing that bayes factors are ratios of posterior to prior empty cluster probabilities the estimator is widely applicable and helps to set thresholds to drop unoccupied components in overfitted mixtures we suggest default prior parameters based on multimodality for normal t mixtures and minimal informativeness for categorical outcomes we characterize theoretically the nlp induced sparsity derive tractable expressions and algorithms we fully develop normal binomial and product binomial mixtures but the theory computation and principles hold more generally we observed a serious lack of sensitivity of the bayesian information criterion insufficient parsimony of the akaike information criterion and a local prior and a mixed behaviour of the singular bayesian information criterion we also considered overfitted mixtures their performance was competitive but depended on tuning parameters under our default prior elicitation nlps offered a good compromise between sparsity and power to detect meaningfully separated components

pullback attractors and extremal complete trajectories for non autonomous reaction diffusion problems abstract we analyse the dynamics of the non autonomous nonlinear reaction diffusion equation u t u f t x u subject to appropriate boundary conditions proving the existence of two bounding complete trajectories one maximal and one minimal our main assumption is that the nonlinear term satisfies a bound of the form f t x u u c t x u 2 d t x u where the linear evolution operator associated with c t x is exponentially stable as an important step in our argument we give a detailed analysis of the exponential stability properties of the evolution operator for the non autonomous linear problem u t u c t x u between different l p spaces

solvability and long time behavior of nonlinear reaction diffusion equations with robin boundary condition abstract in this paper we investigate the long time behavior and solvability of the reaction diffusion equation which has a polynomial growth nonlinearity of arbitrary order with robin boundary condition we begin this paper with the existence and uniqueness of the solution to the problem for the long time behavior we firstly prove the existence of an absorbing set in two different spaces secondly for the autonomous case the existence of a global attractor is obtained in w 2 1 l 2

adaptive bayesian multivariate density estimation with dirichlet mixtures summary we show that rate adaptive multivariate density estimation can be performed using bayesian methods based on dirichlet mixtures of normal kernels with a prior distribution on the kernel s covariance matrix parameter we derive sufficient conditions on the prior specification that guarantee convergence to a true density at a rate that is minimax optimal for the smoothness class to which the true density belongs no prior knowledge of smoothness is assumed the sufficient conditions are shown to hold for the dirichlet location mixture of normals prior with a gaussian base measure and an inverse wishart prior on the covariance matrix parameter locally h older

bayesian density estimation and inference using mixtures abstract we describe and illustrate bayesian inference in models for density estimation using mixtures of dirichlet processes these models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from mixtures of normal distributions efficient simulation methods are used to approximate various prior posterior and predictive distributions this allows for direct inference on a variety of practical issues including problems of local versus global smoothing uncertainty about density estimates assessment of modality and the inference on the numbers of components also convergence results are established for a general class of normal mixture models

posterior convergence rates of dirichlet mixtures at smooth densities we study the rates of convergence of the posterior distribution for bayesian density estimation with dirichlet mixtures of normal distributions as the prior the true density is assumed to be twice continuously differentiable the bandwidth is given a sequence of priors which is obtained by scaling a single prior by an appropriate order in order to handle this problem we derive a new general rate theorem by considering a countable covering of the parameter space whose prior probabilities satisfy a summability condition together with certain individual bounds on the hellinger metric entropy we apply this new general theorem on posterior convergence rates by computing bounds for hellinger bracketing entropy numbers for the involved class of densities the error in the approximation of a smooth density by normal mixtures and the concentration rate of the prior the best obtainable rate of convergence of the posterior turns out to be equivalent to the well known frequentist rate for integrated mean squared error n 2 5 up to a logarithmic factor

extremal equilibria for monotone semigroups in ordered spaces with application to evolutionary equations abstract we consider monotone semigroups in ordered spaces and give general results concerning the existence of extremal equilibria and global attractors we then show some applications of the abstract scheme to various evolutionary problems from odes and retarded functional differential equations to parabolic and hyperbolic pdes in particular we exhibit the dynamical properties of semigroups defined by semilinear parabolic equations in r n with nonlinearities depending on the gradient of the solution we consider as well systems of reaction diffusion equations in r n and provide some results concerning extremal equilibria of the semigroups corresponding to damped wave problems in bounded domains or in r n we further discuss some nonlocal and quasilinear problems as well as the fourth order cahn hilliard equation

new approaches to bayesian consistency we use martingales to study bayesian consistency we derive sufficient conditions for both hellinger and kullback leibler consistency which do not rely on the use of a sieve alternative sufficient conditions for hellinger consistency are also found and demonstrated on examples

attractors in strongly monotone flows where f is a c vector field in r and all the off diagonal terms of its jacobian matrix are nonnegative the well known kamke theorem implies that the flow 4 i ir0 generated by 1 has the property it preserves the partial ordering 0 another system which has this property is the semilinear parabolic equation on a compact n dimensional submanifold solutions of its dirichlet or neumann problem determine a monotone flow in appropriate function space see 11 or 51 in general a flow which preserves a partial ordering on the state space is called monotone m w hirsch has studied this flow in a series of papers 14 io he 2 31 has studied the limiting behavior and convergence of solutions of system 1 he 1 4 lo has investigated this flow in the considerably abstract state space in chapter iii of cl he has used two sections to discuss the properties of attractors for monotone flows and obtained some very important results he has shown that a monotone flow cannot have an attracting cycle see l corollary 2 41 and that every attractor must contain an equilibrium see cl theorem 3 11 in particular if the flow is strongly monotone he has shown that every attractor k contains an order stable equilibrium see 1 theorem 4 11 meanwhile if we suppose z is attracted to k but is not quasiconvergent then k contains two order stable equilibria p q such that p o z 4 q see cl theorem 4 31 the purpose of this paper is further to discuss the properties of attractors for strongly monotone flows we shall study when an o limit set can be an 210 0022 247x 91 3 00

pullback of the lifting of elliptic cusp forms and miyawaki s conjecture we construct a lifting from siegel cusp forms of degree r to siegel cusp forms of degree r 2n for r n 1 our result is a partial solution of a conjecture made by miyawaki 27 page 307 in 1992 in particular we can calculate the standard l function of a cusp form of degree 3 and weight 12 which is in accordance with miyawaki s conjecture we give a conjecture on the petersson inner product of the lifting in terms of certain l values

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

on the bayes optimality of f measure maximizers the f measure which has originally been introduced in information retrieval is nowadays routinely used as a performance metric for problems such as binary classification multi label classification and structured output prediction optimizing this measure is a statistically and computationally challenging problem since no closed form solution exists adopting a decision theoretic perspective this article provides a formal and experimental analysis of different approaches for maximizing the f measure we start with a bayes risk analysis of related loss functions such as hamming loss and subset zero one loss showing that optimizing such losses as a surrogate of the f measure leads to a high worst case regret subsequently we perform a similar type of analysis for f measure maximizing algorithms showing that such algorithms are approximate while relying on additional assumptions regarding the statistical distribution of the binary response variables furthermore we present a new algorithm which is not only computationally efficient but also bayes optimal regardless of the underlying distribution to this end the algorithm requires only a quadratic with respect to the number of binary responses number of parameters of the joint distribution we illustrate the practical performance of all analyzed methods by means of experiments with multi label classification problems

asymptotic behaviour of nonautonomous difference inclusions abstract nonautonomous difference inclusions are formulated as difference inclusion cocycles which generalize set valued semigroups corresponding to autonomous difference inclusions and the existence of a difference inclusion cocycle attractor is established under the assumption of upper semi continuity of the cocycle mappings if the difference inclusion cocycle has an absorbing set several illustrative examples are provided

rates of convergence for the gaussian mixture sieve gaussian mixtures provide a convenient method of densityestimation that lies somewhere between parametric models and kernel densityestimators when the number of components of the mixture is allowed to increase as sample size increases the model is called a mixture sieve we establish a bound on the rate of convergence in hellinger distance for densityestimation using the gaussian mixture sieve assuming that the true density is itself a mixture of gaussians the underlying mixing measure of the true densityis not necessarilyassumed to have finite support computing the rate involves some delicate calculations since the size of the sieve as measured bybracketing entropy and the saturation rate cannot be found using standard methods when the mixing measure has compact support using kn n 2 3 log n 1 3 components in the mixture yields a rate of order log n 1 6 n1 6 for every 0 the rates depend heavilyon the tail behavior of the true density the sensitivity to the tail behavior is diminished byusing a robust sieve which includes a long tailed component in the

convergence rates of posterior distributions for non i i d observations we consider the asymptotic behavior of posterior distributions and bayes estimators based on observations which are required to be neither independent nor identically distributed we give general results on the rate of convergence of the posterior measure relative to distances derived from a testing criterion we then specialize our results to independent nonidentically distributed observations markov processes stationary gaussian time series and the white noise model we apply our general results to several examples of infinite dimensional statistical models including nonparametric regression with normal errors binary regression poisson regression an interval censoring model whittle estimation of the spectral density of a time series and a nonlinear autoregressive model

a mass formula for unimodular lattices with no roots we derive a mass formula for n dimensional unimodular lattices having any prescribed root system we use katsurada s formula for the fourier coefficients of siegel eisenstein series to compute these masses for all root systems of even unimodular 32 dimensional lattices and odd unimodular lattices of dimension n 30 in particular we find the mass of even unimodular 32 dimensional lattices with no roots and the mass of odd unimodular lattices with no roots in dimension n 30 verifying bacher and venkov s enumerations in dimensions 27 and 28 we also compute better lower bounds on the number of inequivalent unimodular lattices in dimensions 26 to 30 than those afforded by the minkowski siegel mass constants