asymptotic distributions of quasi maximum likelihood estimators for spatial autoregressive models this paper investigates asymptotic properties of the maximum likelihood estimator and the quasi maximum likelihood estimator for the spatial autoregressive model the rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model it is important to make the distinction with dif ferent spatial scenarios under the scenario that each unit will be influenced by only a few neighboring units the estimators may have n rate of convergence and be asymp totically normal when each unit can be influenced by many neighbors irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence

central limit theorems and uniform laws of large numbers for arrays of random fields over the last decades spatial interaction models have been increasingly used in economics however the development of a sufficiently general asymptotic theory for nonlinear spatial models has been hampered by a lack of relevant central limit theorems clts uniform laws of large numbers ullns and pointwise laws of large numbers llns these limit theorems form the essential building blocks towards developing the asymptotic theory of m estimators including maximum likelihood and generalized method of moments estimators the paper establishes a clt ulln and lln for spatial processes or random fields that should be applicable to a broad range of data processes

hac estimation in a spatial framework abstract we suggest a non parametric heteroscedasticity and autocorrelation consistent hac estimator of the variance covariance vc matrix for a vector of sample moments within a spatial context we demonstrate consistency under a set of assumptions that should be satisfied by a wide class of spatial models we allow for more than one measure of distance each of which may be measured with error monte carlo results suggest that our estimator is reasonable in finite samples we then consider a spatial model containing various complexities and demonstrate that our hac estimator can be applied in the context of that model

completely reducible operatorname sl 2 homomorphisms keywords semisimple groups group scheme homomorphisms frobenius twists optimal homomorphisms geometrically completely reducible images twisted product homomorphisms cocharacters reference ctg article 2007 002doi 10 1090 s0002 9947 07 04289 4 record created on 2008 12 16 modified on 2017 05 12

complete reducibility of subgroups of reductive algebraic groups over nonperfect fields 1 abstract let k be a nonperfect field of characteristic 2 let g be a k split simple algebraic group of type e 6 or g 2 defined over k in this paper we present the first examples of nonabelian non g completely reducible k subgroups of g which are g completely reducible over k our construction is based on that of subgroups of g acting non separably on the unipotent radical of a proper parabolic subgroup of g in our previous work we also present examples with the same property for a non connected reductive group g along the way several general results concerning complete reducibility over nonperfect fields are proved using the recently proved tits center conjecture for spherical buildings in particular we show that under mild conditions a connected k subgroup of g is pseudo reductive if it is g completely reducible over k

large sample properties of the matrix exponential spatial specification with an application to fdi this paper studies large sample properties of the matrix exponential spatial specification mess we find that the quasi maximum likelihood estimator qmle for the mess is consistent under heteroskedasticity a property not shared by the qmle of the sar model for the general model that has mess in both the dependent variable and disturbances labeled mess 1 1 the qmle can be consistent under unknown heteroskedasticity when the spatial weights matrices in the two mess processes are commutative we also consider the generalized method of moments estimator gmme in the homoskedastic case we derive a best gmme that is as efficient as the maximum likelihood estimator under normality and can be asymptotically more efficient than the qmle under non normality in the heteroskedastic case an optimal gmme can be more efficient than the qmle asymptotically the qml approach for the mess model has the computational advantage over that of a sar model the computational simplicity carries over to mess models with any finite order of spatial matrices no parameter range needs to be imposed in order for the model to be stable results of monte carlo experiments for finite sample properties of the estimators are reported finally the mess 1 1 is applied to belgium s outward fdi data and we observe that the dominant motivation of belgium s outward fdi lies in finding cheaper factor inputs

estimation and model selection of semiparametric copula based multivariate dynamic models under copula misspecification we introduce a new class of semiparametric copula based multivariate dynamic scomdy models which specify the conditional mean and the conditional variance of a multivariate time series parametrically but specify the multivariate distribution of the standardized innovation semiparametrically as a parametric copula evaluated at nonparametric marginal distributions we first study large sample properties of the estimators of scomdy model parameters under a misspecified parametric copula then propose pseudo likelihood ratio plr tests for model selection between two scomdy models with possibly misspecified copulas and finally develop plr tests for model selection between more than two scomdy models the limiting null distributions of the plr tests do not depend on the estimation of conditional mean and conditional variance parameters hence are very easy to simulate empirical applications to three and higher dimensional daily exchange rate series indicate that a scomdy model with a tail dependent copula is generally preferred

cocharacter closure and the rational hilbert mumford theorem for a field k let g be a reductive k group and v an affine k variety on which g acts using the notion of cocharacter closed g k orbits in v we prove a rational version of the celebrated hilbert mumford theorem from geometric invariant theory we initiate a study of applications stemming from this rationality tool a number of examples are discussed to illustrate the concept of cocharacter closure and to highlight how it differs from the usual zariski closure

spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix this paper considers spatial heteroskedasticity and autocorrelation consistent spatial hac estimation of covariance matrices of parameter estimators we generalize the spatial hac estimator introduced by kelejian and prucha 2007 to apply to linear and nonlinear spatial models with moment conditions we establish its consistency rate of convergence and asymptotic truncated mean squared error mse based on the asymptotic truncated mse criterion we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug in method the finite sample performances of the spatial hac estimator are evaluated via monte carlo simulation

an asymptotic analysis of likelihood based diffusion model selection using high frequency data abstract we provide a new asymptotic analysis of model selection procedure that compares likelihoods of two candidate diffusion models our asymptotic analysis relies on two dimensional asymptotic expansions with shrinking sampling interval and increasing sampling span t and clarifies the different roles of drift and diffusion functions in the selection of diffusion models in particular we show that the model with superior diffusion function specification is always preferred to the competing model regardless of their drift specifications if is sufficiently small relative to t the specifications of drift functions matter only when the models have an identical diffusion specification

estimating a spatial autoregressive model with an endogenous spatial weight matrix the spatial autoregressive sar model is a standard tool for analyzing data with spatial correlation conventional estimation methods rely on the key assumption that the spatial weight matrix is strictly exogenous which would likely be violated in some empirical applications where spatial weights are determined by economic factors this paper presents model specification and estimation of the sar model with an endogenous spatial weight matrix we provide three estimation methods two stage instrumental variable 2siv method quasi maximum likelihood estimation qmle approach and generalized method of moments gmm we establish the consistency and asymptotic normality of these estimators and investigate their finite sample properties by a monte carlo study

the ramification of centres lie algebras in positive characteristic and quantised enveloping algebras let h be a hopf algebra over the field k which is a finite module over a central affine sub hopf algebra r examples include enveloping algebras u mathfrak g of finite dimensional k lie algebras mathfrak g in positive characteristic and quantised enveloping algebras and quantised function algebras at roots of unity the ramification behaviour of the maximal ideals of z h with respect to the subalgebra r is studied and the conclusions are then applied to the cases of classical and quantised enveloping algebras in the case of u mathfrak g for mathfrak g semisimple a conjecture of humphreys 28 on the block structure of u mathfrak g is confirmed in the case of u epsilon mathfrak g for mathfrak g semisimple and epsilon an odd root of unity we obtain a quantum analogue of a result of mirkovic and rumynin 35 and we fully describe the factor algebras lying over the regular sheet 9 the blocks of u epsilon mathfrak g are determined and a necessary condition which may also be sufficient for a baby verma u epsilon mathfrak g module to be simple is obtained

complete reducibility and separability let g be a reductive linear algebraic group over an algebraically closed field of characteristic p 0 a subgroup of g is said to be separable in g if its global and infinitesimal centralizers have the same dimension we study the interaction between the notion of separability and serre s concept of g complete reducibility for subgroups of g the separability hypothesis appears in many general theorems concerning g complete reducibility we demonstrate that many of these results fail without this hypothesis on the other hand we prove that if g is a connected reductive group and p is very good for g then any subgroup of g is separable we deduce that under these hypotheses on g a subgroup h of g is g completely reducible provided the lie algebra of g is semisimple as an h module recently guralnick has proved that if h is a reductive subgroup of g and c is a conjugacy class of g then the intersection of c and g is a finite union of h conjugacy classes for generic p when certain extra hypotheses hold including separability this follows from a well known tangent space argument due to richardson but in general it rests on lusztig s deep result that a connected reductive group has only finitely many unipotent conjugacy classes we show that the analogue of guralnick s result is false if one considers conjugacy classes of n tuples of elements from h for n 1

nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models we propose a nonparametric likelihood ratio testing procedure for choosing between a parametric likelihood model and a moment condition model when both models could be misspecified our procedure is based on comparing the kullback leibler information criterion klic between the parametric model and moment condition model we construct the klic for the parametric model using the difference between the parametric log likelihood and a sieve nonparametric estimate of population entropy and obtain the klic for the moment model using the empirical likelihood statistic we also consider multiple 2 model comparison tests when all the competing models could be misspecified and some models are parametric while others are moment based we evaluate the performance of our tests in a monte carlo study and apply the tests to an example from industrial organization

a spatial autoregressive model with a nonlinear transformation of the dependent variable abstract this paper develops a nonlinear spatial autoregressive model of particular interest is a structural interaction model for share data we consider possible instrumental variable iv and maximum likelihood estimation mle for this model and analyze asymptotic properties of the iv and mle based on the notion of spatial near epoch dependence we also design a statistical test to compare the nonlinear transformation against alternatives monte carlo experiments are designed to investigate finite sample performance of the proposed estimates and the sizes and powers of the test

on spatial processes and asymptotic inference under near epoch dependence abstract the development of a general inferential theory for nonlinear models with cross sectionally or spatially dependent data has been hampered by a lack of appropriate limit theorems to facilitate a general asymptotic inference theory relevant to economic applications this paper first extends the notion of near epoch dependent ned processes used in the time series literature to random fields the class of processes that is ned on say an mixing process is shown to be closed under infinite transformations and thus accommodates models with spatial dynamics this would generally not be the case for the smaller class of mixing processes the paper then derives a central limit theorem and law of large numbers for ned random fields these limit theorems allow for fairly general forms of heterogeneity including asymptotically unbounded moments and accommodate arrays of random fields on unevenly spaced lattices the limit theorems are employed to establish consistency and asymptotic normality of gmm estimators these results provide a basis for inference in a wide range of models with spatial dependence

reductive pairs arising from representations let g be a reductive algebraic group and v a g module we consider the question of when gl v g is a reductive pair of algebraic groups where is the representation afforded by v we first make some observations about general g and v then specialise to the group sl2 k with k algebraically closed of positive characteristic p for this group we provide complete answers for the classes of simple and weyl modules the behaviour being determined by the base p expansion of the highest weight of the module we conclude by illustrating some of the results from the first section with examples for the group sl3 k

texture recognition based on diffusion in networks a texture recognition method is proposed the image is modeled into networks the texture is characterized by the diffusion over the networks it is demonstrated that directed networks are better to texture recognition the proposed method outperform the state of the art much work has been done in the field of texture analysis and classification while promising classification methods have been proposed most of them rely on classical image analysis approaches this paper presents a texture classification method based on diffusion in directed networks first an image is modeled as a directed network by mapping each pixel as a node and connecting two nodes up to a maximum distance r to reveal texture properties links between two nodes are removed based on the pixel intensity difference once such a network is obtained the activity of each node is estimated by random walks and combined into a histogram to describe the image the main contribution of this paper is the use of directed networks which tends to provide better performance than in undirected cases also we have shown that the activity induced on these networks can be effectively used as texture descriptor experimental results show that the proposed method is favorably compared to traditional texture methods on widely used texture datasets the proposed method is also found to be promising for plant species classification using samples of leaf texture

a likelihood ratio test for spatial model selection abstract this paper develops a nondegenerate likelihood ratio test for model selection between two competitive spatial econometrics models it generalizes the test of vuong 1989 to models with spatial near epoch dependent ned data we do not make any structural assumption on the true model specification and allow for the cases where both or one of the two competing models are mis specified the test is valid whether two models are nested or non nested as a prerequisite of the test we first show that quasi maximum likelihood estimators qmle of spatial econometrics models are consistent estimators of their pseudo true values and are asymptotically normal under regularity conditions in particular we study spatial autoregressive models with spatial autoregressive errors sarar and matrix exponential spatial specification mess models we derive the limiting null distribution of the test statistic a spatial heteroskedastic and autoregressive consistent estimator of asymptotic variance of the test statistic under the null which is necessary to implement the test is constructed monte carlo experiments are designed to investigate finite sample performance of qmles for sarar and mess models as well as the size and power of the proposed test

representations of reductive groups over finite local rings of length two letfqbe a finite field of characteristicp and letw2 fq be thering of witt vectors of length two overfq we prove that for any reduc tive group schemegoverzsuch thatpis very good forg fq the groupsg fq t t2 andg w2 fq have the same number of irreducible representa tions of dimensiond for eachd equivalently there exists an isomorphism ofgroup algebrasc g fq t t2 c g w2 fq

a matrix exponential spatial specification we introduce the matrix exponential as a way of modelling spatially dependent data the matrix exponential spatial specification mess simplifies the log likelihood allowing a closed form solution to the problem of maximum likelihood estimation and greatly simplifies the bayesian estimation of the model the mess can produce estimates and inferences similar to those from conventional spatial autoregressive models but has analytical computational and interpretive advantages we present maximum likelihood and bayesian approaches to the estimation of this spatial model specification along with methods of model comparisons over different explanatory variables and spatial specifications

on finite group actions on reductive groups and buildings let h be a connected reductive group over a non archimedean local field k and let f autk h be a finite group of order not divisible by p the residual characteristic of k let g h f be the identity component of the subgroup of h consisting of points fixed by f the main theorem of this paper asserts that the bruhat tits building b g of g can be identified with the set of f fixed points of b h several special cases of this theorem have been known previously when e k is a finite totally ramified galois extension h rese k g and f gal e k the condition p f is simply that e k is tamely ramified in this case our main theorem is a well known but unpublished theorem of g rousseau recently one of us g p found a simple proof of this theorem p when g is a classical group realized in the standard way as the identity component of the group of fixed points of an involution of a general linear group h bruhat and tits bt4 have given a description of b g as a subset of b h in particular it follows from their description that b g b h when p 2 recently this fact has been rediscovered by j kim and a moy km and independently a simple proof has been given by gy let h be the split form of spin 8 then one can choose an outer automorphism aut h of order 3 such that g h is of type g2 from a joint work gy by w t gan and one of us j y it is known that b g b h is true for arbitrary p

a geometric approach to complete reducibility let g be a connected reductive linear algebraic group we use geometric methods to investigate g completely reducible subgroups of g giving new criteria for g complete reducibility we show that a subgroup of g is g completely reducible if and only if it is strongly reductive in g this allows us to use ideas of r w richardson and hilbert mumford kempf from geometric invariant theory we deduce that a normal subgroup of a g completely reducible subgroup of g is again g completely reducible thereby providing an affirmative answer to a question posed by j p serre and conversely we prove that the normalizer of a g completely reducible subgroup of g is again g completely reducible some rationality questions and applications to the spherical building of g are considered many of our results extend to the case of non connected g

nonparametric spatial regression under near epoch dependence abstract this paper establishes asymptotic normality and uniform consistency with convergence rates of the local linear estimator for spatial near epoch dependent ned processes the class of the ned spatial processes covers important spatial processes including nonlinear autoregressive and infinite moving average random fields which generally do not satisfy mixing conditions apart from accommodating a larger class of dependent processes the proposed asymptotic theory allows for triangular arrays of heterogeneous random fields located on unevenly spaced lattices and sampled over regions of arbitrary configuration all these features make the results applicable in a wide range of empirical settings

efficient gabor filter design for texture segmentation gabor filters have been successfully applied to a broad range of image processing tasks the present paper considers the design of a single filter to segment a two texture image a new efficient algorithm for gabor filter design is presented along with methods for estimating filter output statistics the algorithm draws upon previous results that showed that the output of a gabor filtered texture is modeled well by a rician distribution a measure of the total output power is used to select the center frequency of the filter and is used to estimate the rician statistics of the gabor filtered image the method is further generalized to include the statistics of postfiltered outputs that are generated by a gaussian filtering operation following the gabor filter the new method typically requires an order of magnitude less computation to design a filter than a previously proposed method experimental results demonstrate the efficacy of the method

kostant section universal centralizer and a modular derived satake equivalence we prove analogues of fundamental results of kostant on the universal centralizer of a connected reductive algebraic group for algebraically closed fields of positive characteristic with mild assumptions and for integral coefficients as an application we use these results to obtain a mixed modular analogue of the derived satake equivalence of bezrukavnikov finkelberg

the zassenhaus variety of a reductive lie algebra in positive characteristic let g be the lie algebra of a connected reductive group g over an algebraically closed field k of characteristic p 0 let z be the centre of the universal enveloping algebra u u g of g its maximal spectrum is called the zassenhaus variety of g we show that under certain mild assumptions on g the field of fractions frac z of z is g equivariantly isomorphic to the function field of the dual space g with twisted g action in particular frac z is rational this confirms a conjecture of j alev furthermore we show that z is a unique factorisation domain confirming a conjecture of a braun and c hajarnavis recently a premet used the above result about frac z a result of colliot thelene kunyavskii popov and reichstein and reduction mod p arguments to show that the gelfand kirillov conjecture cannot hold for simple complex lie algebras that are not of type a c or g2

the asymptotic variance of semiparametric estimators this paper derives a general formula for the asymptotic variance of semiparametric estimators that accounts for the presence of nonparametric estimators of functions the general formula is specialized to show invariance of the asymptotic variance to the type of nonparametric estimator and to obtain correction terms for estimation of densities and mean square projections including conditional expectations regularity conditions for the validity of the formula are also given including primitive conditions for asymptotic normality when series estimators are present new examples considered include a semiparametric panel probit estimator and a series estimator of the average derivative copyright 1994 by the econometric society

sieve maximum likelihood estimation of the spatial autoregressive tobit model abstract this paper extends the ml estimation of a spatial autoregressive tobit model under normal disturbances in xu and lee 2015b journal of econometrics to distribution free estimation we examine the sieve mle of the model where the disturbances are i i d with an unknown distribution we show that the spatial autoregressive process with tobit censoring and related variables are spatial near epoch dependent ned a related contribution is that we develop some exponential inequalities for spatial ned random fields with these inequalities we establish the consistency of the estimator asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection simulations show that the sieve mle can improve the finite sample performance upon misspecified normal mles as an empirical application we examine the school district income surtax rates in iowa our results show that the spatial spillover effects are significant but they may be overestimated if disturbances are restricted to be normally distributed

optimizing gabor filter design for texture edge detection and classification an effective and efficient texture analysis method based on a new criterion for designing gabor filter sets is proposed the commonly used filter sets are usually designed for optimal signal representation we propose here an alternative criterion for designing the filter set we consider a set of filters and its response to pairs of harmonic signals two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components we propose an algorithm for deriving the set of gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range the resulting filters differ significantly from the traditional ones we test these maximal harmonic discrimination mhd filters in several texture analysis tasks clustering recognition and edge detection it turns out that the proposed filters perform much better than the traditional ones in these tasks they can achieve performance similar to that of state of the art distribution based texton methods while being simpler and more computationally efficient

combining fractal and deterministic walkers for texture analysis and classification abstract in this paper we present a novel texture analysis method based on deterministic partially self avoiding walks and fractal dimension theory after finding the attractors of the image set of pixels using deterministic partially self avoiding walks they are dilated in direction to the whole image by adding pixels according to their relevance the relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors the proposed texture analysis method is demonstrated to outperform popular and state of the art methods e g fourier descriptors occurrence matrix gabor filter and local binary patterns as well as deterministic tourist walk method and recent fractal methods using well known texture image datasets

gmm and 2sls estimation of mixed regressive spatial autoregressive models the gmm method and the classical 2sls method are considered for the estimation of mixed regressive spatial autoregressive models these methods have computational advantage over the conventional maximum likelihood method the proposed gmm estimators are shown to be consistent and asymptotically normal within certain classes of gmm estimators best ones are derived the proposed gmm estimators improve upon the 2sls estimators and are applicable even if all regressors are irrelevant a best gmm estimator may have the same limiting distribution as the ml estimator with normal disturbances

estimation and confidence regions for parameter sets in econometric models this paper develops a framework for performing estimation and inference in econometric models with partial identification focusing particularly on models characterized by moment inequalities and equalities applications of this framework include the analysis of game theoretic models revealed preference restrictions regressions with missing and corrupted data auction models structural quantile regressions and asset pricing models specifically we provide estimators and confidence regions for the set of minimizers i of an econometric criterion function q in applications the criterion function embodies testable restrictions on economic models a parameter value that describes an economic model satisfies these restrictions if q attains its minimum at this value interest therefore focuses on the set of minimizers called the identified set we use the inversion of the sample analog q n of the population criterion q to construct estimators and confidence regions for the identified set and develop consistency rates of convergence and inference results for these estimators and regions to derive these results we develop methods for analyzing the asymptotic properties of sample criterion functions under set identification

shape based invariant texture indexing this paper introduces a new texture analysis scheme which is invariant to local geometric and radiometric changes the proposed methodology relies on the topographic map of images obtained from the connected components of level sets this morphological tool providing a multi scale and contrast invariant representation of images is shown to be well suited to texture analysis we first make use of invariant moments to extract geometrical information from the topographic map this yields features that are invariant to local similarities or local affine transformations these features are invariant to any local contrast change we then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases the obtained experimental results outperform the current state of the art in locally invariant texture analysis

maximum likelihood estimation of a spatial autoregressive tobit model this paper examines a tobit model with spatial autoregressive interactions we consider the maximum likelihood estimation for this model and analyze asymptotic properties of the estimator based on the spatial near epoch dependence of the dependent variable process generated from the model structure we show that the maximum likelihood estimator is consistent and asymptotically normally distributed monte carlo experiments are performed to verify finite sample properties of the estimator

texture classification using spectral histograms based on a local spatial frequency representation we employ a spectral histogram as a feature statistic for texture classification the spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage the distance between two spectral histograms is measured using spl chi sup 2 statistic the spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification a filter selection algorithm is proposed to maximize classification performance of a given dataset our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well comparisons show that our method produces a marked improvement in classification performance finally we point out the relationships between existing texture features and the spectral histogram suggesting that the latter may provide a unified texture feature

contour and texture analysis for image segmentation this paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture natural images contain both textured and untextured regions so the cues of contour and texture differences are exploited simultaneously contours are treated in the intervening contour framework while texture is analyzed using textons each of these cues has a domain of applicability so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel having obtained a local measure of how likely two nearby pixels are to belong to the same region we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness experimental results on a wide range of images are shown

filtering for texture classification a comparative study in this paper we review most major filtering approaches to texture feature extraction and perform a comparative study filtering approaches included are laws masks 1980 ring wedge filters dyadic gabor filter banks wavelet transforms wavelet packets and wavelet frames quadrature mirror filters discrete cosine transform eigenfilters optimized gabor filters linear predictors and optimized finite impulse response filters the features are computed as the local energy of the filter responses the effect of the filtering is highlighted keeping the local energy function and the classification algorithm identical for most approaches for reference comparisons with two classical nonfiltering approaches co occurrence statistical and autoregressive model based features are given we present a ranking of the tested approaches based on extensive experiments

asymptotics for semiparametric econometric models via stochastic equicontinuity this paper provides a general framework for proving the square root of t consistency and asymptotic normality of a wide variety of semiparametric estimators the class of estimators considered consists of estimators that can be defined as the solution to a minimization problem based on a criterion function that may depend on a preliminary infinite dimensional nuisance parameter estimator the method of proof exploits results concerning the stochastic equicontinuity of stochastic processes the results are applied to the problem of semiparametric weighted least squares estimation of partially parametric regression models primitive conditions are given for square root of t consistency and asymptotic normality of this estimator copyright 1994 by the econometric society

identification and estimation of a discrete game of complete information we discuss the identification and estimation of discrete games of complete information following bresnahan and reiss 1990 1991 a discrete game is a generalization of a standard discrete choice model where utility depends on the actions of other players using recent algorithms to compute all of the nash equilibria to a game we propose simulation based estimators for static discrete games we demonstrate that the model is identified under weak functional form assumptions using exclusion restrictions and an identification at infinity approach monte carlo evidence demonstrates that the estimator can perform well in moderately sized samples as an application we study entry decisions by construction contractors to bid on highway projects in california we find that an equilibrium is more likely to be observed if it maximizes joint profits has a higher nash product uses mixed strategies and is not pareto dominated by another equilibrium

nilpotent orbits in good characteristic and the kempf rousseau theory let g be a connected reductive algebraic group over an algebraically closed field k of characteristic p 0 image and suppose that p is a good prime for the root system of g in this paper we give a fairly short conceptual proof of pommerening s theorem pommerening j algebra 49 1977 525 536 j algebra 65 1980 373 398 which states that any nilpotent element in image is richardson in a distinguished parabolic subalgebra of the lie algebra of a levi subgroup of g as a by product we obtain a short noncomputational proof of the existence theorem for good transverse slices to the nilpotent g orbits in image for earlier proofs of this theorem see kawanaka invent math 84 1986 575 616 premet trans amer math soc 347 1995 2961 2988 spaltenstein j fac sci univ tokyo sect ia math 31 1984 283 286 we extend recent results of sommers internal math res notices 11 1998 539 562 to reductive lie algebras of good characteristic thus providing a satisfactory approach to computing the component groups of the centralisers of nilpotent elements in image and unipotent elements in g earlier computations of these groups in positive characteristics relied mostly on work of mizuno j fac sci univ tokyo sect ia math 24 1977 525 563 tokyo j math 3 1980 391 459 our approach is based on the theory of optimal parabolic subgroups for g unstable vectors also known as the kempf rousseau theory which provides a good substitute for the image theory prominent in the characteristic zero case

nonnegative matrix factorization with earth mover s distance metric for image analysis nonnegative matrix factorization nmf approximates a given data matrix as a product of two low rank nonnegative matrices usually by minimizing the l2 or the kl distance between the data matrix and the matrix product this factorization was shown to be useful for several important computer vision applications we propose here two new nmf algorithms that minimize the earth mover s distance emd error between the data and the matrix product the algorithms emd nmf and bilateral emd nmf are iterative and based on linear programming methods we prove their convergence discuss their numerical difficulties and propose efficient approximations naturally the matrices obtained with emd nmf are different from those obtained with l2 nmf we discuss these differences in the context of two challenging computer vision tasks texture classification and face recognition perform actual nmf based image segmentation for the first time and demonstrate the advantages of the new methods with common benchmarks

image representation using 2d gabor wavelets this paper extends to two dimensions the frame criterion developed by daubechies for one dimensional wavelets and it computes the frame bounds for the particular case of 2d gabor wavelets completeness criteria for 2d gabor image representations are important because of their increasing role in many computer vision applications and also in modeling biological vision since recent neurophysiological evidence from the visual cortex of mammalian brains suggests that the filter response profiles of the main class of linearly responding cortical neurons called simple cells are best modeled as a family of self similar 2d gabor wavelets we therefore derive the conditions under which a set of continuous 2d gabor wavelets will provide a complete representation of any image and we also find self similar wavelet parametrization which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis approximating a tight frame generates redundancy which allows low resolution neural responses to represent high resolution images