cross correlations of diffuse noise in an ocean environment using eigenvalue based statistical inference cross correlations of diffuse noise fields can be used to extract environmental information the influence of directional sources usually ships often results in a bias of the travel time estimates obtained from the cross correlations using an array of sensors insights from random matrix theory on the behavior of the eigenvalues of the sample covariance matrix scm in an isotropic noise field are used to isolate the diffuse noise component from the directional sources a sequential hypothesis testing of the eigenvalues of the scm reveals eigenvalues dominated by loud sources that are statistical outliers for the assumed diffuse noise model travel times obtained from cross correlations using only the diffuse noise component i e by discarding or attenuating the outliers converge to the predicted travel times based on the known array sensor spacing and measured sound speed at the site and are stable temporally i e unbiased estimates data from the shallow water 2006 experiment demonstrates the effectiveness of this approach and that the signal to noise ratio builds up as the square root of time as predicted by theory

higher order properties of the wild bootstrap under misspecification abstract we examine the higher order properties of the wild bootstrap wu 1986 in a linear regression model with stochastic regressors we find that the ability of the wild bootstrap to provide a higher order refinement is contingent upon whether the errors are mean independent of the regressors or merely uncorrelated with them in the latter case the wild bootstrap may fail to match some of the terms in an edgeworth expansion of the full sample test statistic nonetheless we show that the wild bootstrap still has a lower maximal asymptotic risk as an estimator of the true distribution than a normal approximation in shrinking neighborhoods of properly specified models to assess the practical implications of this result we conduct a monte carlo study contrasting the performance of the wild bootstrap with a normal approximation and the traditional nonparametric bootstrap

a robust goodness of fit test statistic with application to ordinal regression models we propose a goodness of fit test statistic for linear regression with heterogeneous variance which is asymptotically chi square if the given model is correct the test statistic is computed as a quadratic form of observed minus predicted responses we apply the method to a linear regression for an ordinal categorical response the wheezing status of a child no wheeze wheeze with cold wheeze apart from cold as a function of maternal smoking and city of residence

learning a mahalanobis metric from equivalence constraints many learning algorithms use a metric defined over the input space as a principal tool and their performance critically depends on the quality of this metric we address the problem of learning metrics using side information in the form of equivalence constraints unlike labels we demonstrate that this type of side information can sometimes be automatically obtained without the need of human intervention we show how such side information can be used to modify the representation of the data leading to improved clustering and classification specifically we present the relevant component analysis rca algorithm which is a simple and efficient algorithm for learning a mahalanobis metric we show that rca is the solution of an interesting optimization problem founded on an information theoretic basis if dimensionality reduction is allowed within rca we show that it is optimally accomplished by a version of fisher s linear discriminant that uses constraints moreover under certain gaussian assumptions rca can be viewed as a maximum likelihood estimation of the within class covariance matrix we conclude with extensive empirical evaluations of rca showing its advantage over alternative methods

modified subspace algorithms for doa estimation with large arrays this paper proposes the use of a new generalized asymptotic paradigm in order to analyze the performance of subspace based direction of arrival doa estimation in array signal processing applications instead of assuming that the number of samples is high whereas the number of sensors antennas remains fixed the asymptotic situation analyzed herein assumes that both quantities tend to infinity at the same rate this asymptotic situation provides a more accurate description of a potential situation where these two quantities are finite and hence comparable in magnitude it is first shown that both music and ssmusic are inconsistent when the number of antennas sensors increases without bound at the same rate as the sample size this is done by analyzing and deriving closed form expressions for the two corresponding asymptotic cost functions by examining these asymptotic cost functions one can establish the minimum number of samples per antenna needed to resolve closely spaced sources in this asymptotic regime next two alternative estimators are constructed that are strongly consistent in the new asymptotic situation i e they provide consistent doa estimates not only when the number of snapshots goes to infinity but also when the number of sensors antennas increases without bound at the same rate these estimators are inspired by the theory of g estimation and are therefore referred to as g music and g ssmusic respectively simulations show that the proposed algorithms outperform their traditional counterparts in finite sample size situations although they still present certain limitations

quasi aitken estimation for heteroskedasticity of unknown form abstract two modifications to standard aitken estimation of heteroskedastic linear regressions are suggested 1 different criteria for calculating unknown parameters to yield more efficient estimates to the coefficients when the form of heteroskedasticity is misspecified and 2 a different way of estimating the covariance matrix of the feasible aitken estimator a sampling experiment provides evidence that the first adjustment could provide more efficient estimates though not clearly more so than alternatives the second adjustment is crucial for reliable inference even when the form of heteroskedasticity is correctly specified

approximate power functions for some robust tests of regression coefficients approximate power functions for some robust tests of regression coefficients thomas j rothenberg department o f economics u n i v e r s i t y o f c a l i f o r n i a berkeley research papers i n economics no 84 1 summary edgeworth expansions are developed f o r the d i s t r i b u t i o n functions o f some t e s t s t a t i s t i c s i n the normal l i n e a r regression model where the e r r o r covariance matrix i s unknown tests based on generalized l e a s t squares estimates and also on ordinary l e a s t squares estimates are considered in both cases adjustments t o the asymptotic c r i t i c a l the approxi values are found and approximate l o c a l power c a l c u l a t e d and a u t o c o r r e l a t i o n mations are applied to a number o f examples i n c l u d i n g h e t e r o s c e d a s t i c i t y

learning a mahalanobis distance metric for data clustering and classification distance metric is a key issue in many machine learning algorithms this paper considers a general problem of learning from pairwise constraints in the form of must links and cannot links as one kind of side information a must link indicates the pair of the two data points must be in a same class while a cannot link indicates that the two data points must be in two different classes given must link and cannot link information our goal is to learn a mahalanobis distance metric under this metric we hope the distances of point pairs in must links are as small as possible and those of point pairs in cannot links are as large as possible this task is formulated as a constrained optimization problem in which the global optimum can be obtained effectively and efficiently finally some applications in data clustering interactive natural image segmentation and face pose estimation are given in this paper experimental results illustrate the effectiveness of our algorithm

some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties abstract we examine several modified versions of the heteroskedasticity consistent covariance matrix estimator of hinkley 1977 and white 1980 on the basis of sampling experiments which compare the performance of quasi t statistics we find that one estimator based on the jackknife performs better in small samples than the rest we also examine the finite sample properties of using modified critical values based on edgeworth approximations as proposed by rothenberg 1984 in addition we compare the power of several tests for heteroskedasticity and find that it may be wise to employ the jackknife heteroskedasticity consistent covariance matrix even in the absence of detected heteroskedasticity

second order approximation in the partially linear regression model we examine the second order properties of various quantities of interest in the partially linear regression model we obtain a stochastic expansion with remainder op n 2 4 where u 1 2 for the standardized semiparametric least squares estimator a standard error estimator and a studentized statistic we use the second order expansions to correct the standard error estimates for second order effects and to define a method of bandwidth choice a monte carlo experiment provides favorable evidence on our method of bandwidth choice

analyzing two stage experiments in the presence of interference abstracttwo stage randomization is a powerful design for estimating treatment effects in the presence of interference that is when one individual s treatment assignment affects another individual s outcomes our motivating example is a two stage randomized trial evaluating an intervention to reduce student absenteeism in the school district of philadelphia in that experiment households with multiple students were first assigned to treatment or control then in treated households one student was randomly assigned to treatment using this example we highlight key considerations for analyzing two stage experiments in practice our first contribution is to address additional complexities that arise when household sizes vary in this case researchers must decide between assigning equal weight to households or equal weight to individuals we propose unbiased estimators for a broad class of individual and household weighted estimands with corresponding theoretical and estimated variances our second co

optimal convergence rates bahadur representation and asymptotic normality of partitioning estimators abstract this paper studies the asymptotic properties of partitioning estimators of the conditional expectation function and its derivatives mean square and uniform convergence rates are established and shown to be optimal under simple and intuitive conditions the uniform rate explicitly accounts for the effect of moment assumptions which is useful in semiparametric inference a general asymptotic integrated mean square error approximation is obtained and used to derive an optimal plug in tuning parameter selector a uniform bahadur representation is developed for linear functionals of the estimator using this representation asymptotic normality is established along with consistency of a standard error estimator the finite sample performance of the partitioning estimator is examined and compared to other nonparametric techniques in an extensive simulation study

constrained clustering with nonnegative matrix factorization nonnegative matrix factorization nmf and symmetric nmf symnmf have been shown to be effective for clustering linearly separable data and nonlinearly separable data respectively nevertheless many practical applications demand constrained algorithms in which a small number of constraints in the form of must link and cannot link are available in this paper we propose an nmf based constrained clustering framework in which the similarity between two points on a must link is enforced to approximate 1 and the similarity between two points on a cannot link is enforced to approximate 0 we then formulate the framework using nmf and symnmf to deal with clustering of linearly separable data and nonlinearly separable data respectively furthermore we present multiplicative update rules to solve them and show the correctness and convergence experimental results on various text data sets university of california irvine uci data sets and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms

a comparison of the variance estimation methods for heteroscedastic nonlinear models heteroscedasticity is commonly encountered when fitting nonlinear regression models in practice we discuss eight different variance estimation methods for nonlinear regression models with heterogeneous response variances and present a simulation study to compare the performance of the eight methods in terms of estimating the standard errors of the fitted model parameters the simulation study suggests that when the true variance is a function of the mean model the power of the mean variance function estimation method and the transform both sides method are the best choices for estimating the standard errors of the estimated model parameters in general the wild bootstrap estimator and two modified versions of the standard sandwich variance estimator are reasonably accurate with relatively small bias especially when the heterogeneity is nonsystematic across values of the covariate furthermore we note that the two modified sandwich estimators are appealing choices in practice considering the computational advantage of these two estimation methods relative to the variance function estimation method and the transform both sides approach copyright 2016 john wiley sons ltd

asymptotic normality of series estimators for nonparametric and semiparametric regression models this paper establishes the asymptotic normality of series estimators for nonparametric regression models gallant s fourier flexible form estimators trigonometric series estimators and polynomial series estimators are prime examples of the estimators covered by the results the results apply to a wide variety of estimates in the regression model under consideration including derivatives and integrals of the regression function the errors in the model may be homoskedastic or heteroskedastic the paper also considers series estimators for additive interactive regression semiparametric regression and semiparametric index regression models and shows them to be consistent and asymptotically normal copyright 1991 by the econometric society

on the asymptotic behavior of the sample estimates of eigenvalues and eigenvectors of covariance matrices this paper analyzes the asymptotic behavior of the sample estimators of the eigenvalues and eigenvectors of covariance matrices rather than considering traditional large sample size asymptotics in this paper the focus is on limited sample size situations whereby the number of available observations is comparable in magnitude to the observation dimension using tools from random matrix theory the asymptotic behavior of the traditional sample estimates is investigated under the assumption that both the sample size and the observation dimension tend to infinity while their quotient converges to a positive quantity assuming that an asymptotic eigenvalue splitting condition is fulfilled closed form asymptotic expressions of these estimators are derived proving inconsistency of the traditional sample estimators in these asymptotic conditions the derived expressions are shown to provide a valuable insight into the behavior of the sample estimators in the small sample size regime

residual bootstrap tests in linear models with many regressors abstract this paper is concerned with bootstrap hypothesis testing in linear regression models with many regressors i show that bootstrap f l r and l m tests are asymptotically valid even when the numbers of estimated parameters and tested restrictions are not asymptotically negligible fractions of the sample size one of the conditions for these results is that the regressors come from an asymptotically balanced design depending on the number of restrictions tested and on the errors distribution violation of that condition might render the bootstrap tests asymptotically invalid in that case i propose bootstrapping calhoun s 2011 g statistic or modified versions of the l r and l m statistics and show that these procedures remain asymptotically valid monte carlo simulations indicate that the bootstrap tests often outperform asymptotic ones however analyzing the approximate third cumulant of the f statistic reveals that the bootstrap test does not generally provide the usual higher order asymptotic refinements nevertheless it is found that the bootstrap third cumulant partially matches the population third cumulant which might explain the bootstrap s good finite sample performances especially when the errors come from a symmetric distribution

does modeling a structural break improve forecast accuracy mean square forecast error loss implies a bias variance trade off that suggests that structural breaks of small magnitude should be ignored in this paper we provide a test to determine whether modeling a structural break improves forecast accuracy the test is near optimal even when the date of a local to zero break is not consistently estimable the results extend to forecast combinations that weight the post break sample and the full sample forecasts by our test statistic in a large number of macroeconomic time series we find that structural breaks that are relevant for forecasting occur much less frequently than existing tests indicate

a robust effect size index effect size indices are useful tools in study design and reporting because they are unitless measures of association strength that do not depend on sample size existing effect size indices are developed for particular parametric models or population parameters here we propose a robust effect size index based on m estimators this approach yields an index that is very generalizable because it is unitless across a wide range of models we demonstrate that the new index is a function of cohen s d formula see text and standardized log odds ratio when each of the parametric models is correctly specified we show that existing effect size estimators are biased when the parametric models are incorrect e g under unknown heteroskedasticity we provide simple formulas to compute power and sample size and use simulations to assess the bias and standard error of the effect size estimator in finite samples because the new index is invariant across models it has the potential to make communication and comprehension of effect size uniform across the behavioral sciences

supervised principal component analysis visualization classification and regression on subspaces and submanifolds we propose supervised principal component analysis supervised pca a generalization of pca that is uniquely effective for regression and classification problems with high dimensional input data it works by estimating a sequence of principal components that have maximal dependence on the response variable the proposed supervised pca is solvable in closed form and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations such as dna microarray experiments furthermore we show how the algorithm can be kernelized which makes it applicable to non linear dimensionality reduction tasks experimental results on various visualization classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency

improved heteroscedasticity consistent covariance matrix estimators summary the heteroscedasticity consistent covariance matrix estimator proposed by white 1980 is commonly used in practical applications and is implemented into a number of pieces of statistical software however although consistent it can display substantial bias in small to moderately large samples as shown by monte carlo simulations elsewhere this paper defines modified white estimators which are approximately bias free numerical results show that the modified estimators display much smaller bias than white s estimator in small samples we also show that the bias correction leads to some variance inflation in hypothesis testing based on heteroscedasticity consistent covariance matrix estimators numerical results suggest that tests based on the proposed bias corrected estimators typically display smaller size distortions

non linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data the problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem until now various metric learning methods have been proposed for semi supervised clustering although some of the existing methods can use both positive must link and negative cannot link constraints they are usually limited to learning a linear transformation i e finding a global mahalanobis metric in this paper we propose a framework for learning linear and non linear transformations efficiently we use both positive and negative constraints and also the intrinsic topological structure of data we formulate our metric learning method as an appropriate optimization problem and find the global optimum of this problem the proposed non linear method can be considered as an efficient kernel learning method that yields an explicit non linear transformation and thus shows out of sample generalization ability experimental results on synthetic and real world data sets show the effectiveness of our metric learning method for semi supervised clustering tasks

a note on studentizing a test for heteroscedasticity abstract breusch and pagan 1979 have recently proposed a convenient test for heteroscedasticity in general linear models this note derives the asymptotic distribution of their test under sequences of contiguous alternatives to the null hypothesis of homoscedasticity the test is shown to possess asymptotically incorrect size nominal significance level except in the case of strictly gaussian disturbances a slight modification of the test is proposed which corrects this defect

music g music and maximum likelihood performance breakdown direction of arrival estimation performance of music and maximum likelihood estimation in the so called ldquothresholdrdquo area is analyzed by means of general statistical analysis gsa also known as random matrix theory both analytic predictions and direct monte carlo simulations demonstrate that the well known music specific ldquoperformance breakdownrdquo is associated with the loss of resolution capability in the music pseudo spectrum while the sample signal subspace is still reliably separated from the actual noise subspace significant distinctions between music g music specific and mle intrinsic causes of ldquoperformance breakdown rdquo as well as the role of ldquosubspace swaprdquo phenomena are specified analytically and supported by simulation

on mitigating the analytical limitations of finely stratified experiments although attractive from a theoretical perspective finely stratified experiments such as paired designs suffer from certain analytical limitations that are not present in block randomized experiments with multiple treated and control individuals in each block in short when using a weighted difference in means to estimate the sample average treatment effect the traditional variance estimator in a paired experiment is conservative unless the pairwise average treatment effects are constant across pairs however in more coarsely stratified experiments the corresponding variance estimator is unbiased if treatment effects are constant within blocks even if they vary across blocks using insights from classical least squares theory we present an improved variance estimator that is appropriate in finely stratified experiments the variance estimator remains conservative in expectation but is asymptotically no more conservative than the classical estimator and can be considerably less conservative the magnitude of the improvement depends on the extent to which effect heterogeneity can be explained by observed covariates aided by this estimator a new test for the null hypothesis of a constant treatment effect is proposed these findings extend to some but not all superpopulation models depending on whether the covariates are viewed as fixed across samples

statistical analysis of diffusion tensors in diffusion weighted magnetic resonance imaging data diffusion tensor imaging has been widely used to reconstruct the structure and orientation of fibers in biological tissues particularly in the white matter of the brain because it can track the effective diffusion of water along those fibers the raw diffusion weighted images from which diffusion tensors are estimated however inherently contain noise noise in the images produces uncertainty in the estimation of the tensors which are 3 3 positive definite matrices and of their derived quantities including eigenvalues eigenvectors and the fiber pathways that are reconstructed based on those tensor elements the aim of this article is to provide a comprehensive theoretical framework of statistical inference for quantifying the effects of noise on diffusion tensors on their eigenvalues and eigenvectors and on their morphological classification we propose a semiparametric model to account for noise in diffusion weighted images we then develop a one step weighted least squares estimate of the t

resurrecting weighted least squares this paper shows how asymptotically valid inference in regression models based on the weighted least squares wls estimator can be obtained even when the model for reweighting the data is misspecified like the ordinary least squares estimator the wls estimator can be accompanied by heterokedasticty consistent hc standard errors without knowledge of the functional form of conditional heteroskedasticity first we provide rigorous proofs under reasonable assumptions second we provide numerical support in favor of this approach indeed a monte carly study demonstrates attractive finite sample properties compared to the status quo both in terms of estimation and making inference

discriminative metric learning for multi view graph partitioning we propose discriminative metric learning for multi view graph partitioning we envision the multi view graph as an adaptive dynamic system the intra view connections and the inter view couplings are interplayed extensive experiments have been conducted to show the effectiveness in recent years multi view graph partitioning has attracted more and more attention but most efforts have been made to develop graph partitioning approaches directly in the original topological structure in many real world applications graph may contain noisy links and the distance metric may not be so discriminative for revealing cluster structure this paper addresses the problem of discriminative metric learning for multi view graph partitioning in particular we propose a novel method called multi view dml abbr of multi view discriminative metric learning to transform the metric space in the original graph into a more discriminative metric space in which better graph partitioning results will be obtained we envision the multi view graph as an adaptive dynamic system where both the intra view connections and the inter view couplings are interplayed to gradually update the relation metric among nodes on the one hand the inter view coupling will be influenced by the intra view connection between two nodes on the other hand the inter view coupling will also affect the intra view connection such interplay eventually makes the whole graph reach a steady state which has a stronger cluster structure than the original graph extensive experiments are conducted on both synthetic and real world graphs to confirm that the proposed method is able to learn more discriminative metric

a note on the efficiency of sandwich covariance matrix estimation the sandwich estimator also known as robust covariance matrix estimator heteroscedasticity consistent covariance matrix estimate or empirical covariance matrix estimator has achieved increasing use in the econometric literature as well as with the growing popularity of generalized estimating equations its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when the fitted parametric model fails to hold or is not even specified surprisingly though there has been little discussion of properties of the sandwich method other than consistency we investigate the sandwich estimator in quasi likelihood models asymptotically and in the linear case analytically we show that under certain circumstances when the quasi likelihood model is correct the sandwich estimate is often far more variable than the usual parametric variance estimate the increased variance is a fixed feature of the method and the price that one pays to obtain consistency even when the p

rapid and brief communication extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints relevant component analysis rca is a recently proposed metric learning method for semi supervised learning applications it is a simple and efficient method that has been applied successfully to give impressive results however rca can make use of supervisory information in the form of positive equivalence constraints only in this paper we propose an extension to rca that allows both positive and negative equivalence constraints to be incorporated experimental results show that the extended rca algorithm is effective

spatial filtering in ambient noise interferometry theoretically the empirical green s function between a pair of receivers can be extracted from the cross correlation of the received diffuse noise the diffuse noise condition rarely is met in the ocean and directional sources may bias the green s function here matrix based spatial filters are used for removing unwanted contributions in the cross correlations two methods are used for solving the matrix filter design problem first a matrix least square problem is solved with a low rank approximation of the pseudo inverse here derived for linear and planar arrays second a convex optimization approach is used to solve the design problem reformulated with ad hoc constraints the spatial filter is applied to real data cross correlations of elements from a linear array to attenuate the contribution of a discrete interferer in the case of a planar array and simulated data a spatial filter enables a passive upgoing downgoing wavefield separation along with an efficient rejection of horizontally propagating noise the impact of array size and frequency band on the filtered cross correlations is discussed

heteroscedasticity robust inference in linear regression models with many covariates we consider inference in linear regression models that is robust to heteroscedasticity and the presence of many control variables when the number of control variables increases at the same rate as

the finite sample distributions of heteroskedasticity robust wald statistics the imhof procedure is used to calculate the exact finite sample distributions of alternative heteroskedasticity robust wald type tests of scalar linear hypothesis in the normal linear model the tests are distinguished by the variance estimator that they use these include the white eicker and jackknife estimators the calculations demonstrate the importance of regression design in determining the quality of conventional first order asymptotic approximations to the null distributions of the tests

some improvements in confidence intervals for standardized regression coefficients yuan and chan psychometrika 76 670 690 2011 doi 10 1007 s11336 011 9224 6 derived consistent confidence intervals for standardized regression coefficients under fixed and random score assumptions jones and waller psychometrika 80 365 378 2015 doi 10 1007 s11336 013 9380 y extended these developments to circumstances where data are non normal by examining confidence intervals based on browne s br j math stat psychol 37 62 83 1984 doi 10 1111 j 2044 8317 1984 tb00789 x asymptotic distribution free adf theory seven different heteroscedastic consistent hc estimators were investigated in the current study as potentially better solutions for constructing confidence intervals on standardized regression coefficients under non normality normal theory adf and hc estimators were evaluated in a monte carlo simulation findings confirmed the superiority of the hc3 mackinnon and white j econ 35 305 325 1985 doi 10 1016 0304 4076 85 90158 7 and hc5 cribari neto and da silva adv stat anal 95 129 146 2011 doi 10 1007 s10182 010 0141 2 interval estimators over jones and waller s adf estimator under all conditions investigated as well as over the normal theory method the hc5 estimator was more robust in a restricted set of conditions over the hc3 estimator some possible extensions of hc estimators to other effect size measures are considered for future developments

eigenvalue distributions of large euclidean random matrices for waves in random media we study probability distributions of eigenvalues of hermitian and non hermitian euclidean random matrices that are typically encountered in the problems of wave propagation in random media

semi supervised clustering with metric learning an adaptive kernel method most existing representative works in semi supervised clustering do not sufficiently solve the violation problem of pairwise constraints on the other hand traditional kernel methods for semi supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided but also lack a measure that achieves better effectiveness of clustering in this paper we propose an adaptive semi supervised clustering kernel method based on metric learning sckmm to mitigate the above problems specifically we first construct an objective function from pairwise constraints to automatically estimate the parameter of the gaussian kernel then we use pairwise constraint based k means approach to solve the violation issue of constraints and to cluster the data furthermore we introduce metric learning into nonlinear semi supervised clustering to improve separability of the data for clustering finally we perform clustering and metric learning simultaneously experimental results on a number of real world data sets validate the effectiveness of the proposed method

eigenvalues of the sample covariance matrix for a towed array it is well known that observations of the spatial sample covariance matrix scm also called the cross spectral matrix reveal that the ordered noise eigenvalues of the scm decay steadily but common models predict equal noise eigenvalues random matrix theory rmt is used to derive and discuss properties of the eigenvalue spectrum of the data scm for linear arrays with an application to ocean acoustic data noise on the array is considered either incoherent or propagating acoustic noise that is coherent across the array using conventional three dimensional or two dimensional isotropic noise models with full or snapshot deficient observations realizations of the scm eigenvalues are explained using rmt deep water towed array data are analyzed and it is shown that the eigenvalues of the scm compare well with theory it is demonstrated how rmt can be applied to study eigenvalue spectrum estimation as dependent on array properties element spacing to wavelength ratio and data sampling snapshots apart from explaining the observed noise eigenvalue spectrum the improved model of the eigenvalue spectrum has important applications in array signal processing

supervised distance metric learning through maximization of the jeffrey divergence over the past decades distance metric learning has attracted a lot of interest in machine learning and related fields in this work we propose an optimization framework for distance metric learning via linear transformations by maximizing the jeffrey divergence between two multivariate gaussian distributions derived from local pairwise constraints in our method the distance metric is trained on positive and negative difference spaces which are built from the neighborhood of each training instance so that the local discriminative information is preserved we show how to solve this problem with a closed form solution rather than using tedious optimization procedures the solution is easy to implement and tractable for large scale problems experimental results are presented for both a linear and a kernelized version of the proposed method for k nearest neighbors classification we obtain classification accuracies superior to the state of the art distance metric learning methods in several cases while being competitive in others highlightswe propose a novel distance metric learning method dmlmj for classification dmlmj is simple to implement and it can be solved analytically we extend dmlmj into a kernelized version to tackle non linear problems experiments on several data sets show the effectiveness of the proposed method

heteroskedasticity robust standard errors for fixed effects panel data regression the conventional heteroskedasticity robust hr variance matrix estimator for cross sectional regression with or without a degrees of freedom adjustment applied to the fixed effects estimator for panel data with serially uncorrelated errors is inconsistent if the number of time periods t is fixed and greater than 2 as the number of entities n increases we provide a bias adjusted hr estimator that is consistent under any sequences n t in which n and or t increase to this estimator can be extended to handle serial correlation of fixed order

a kernel approach for semisupervised metric learning while distance function learning for supervised learning tasks has a long history extending it to learning tasks with weaker supervisory information has only been studied recently in particular some methods have been proposed for semisupervised metric learning based on pairwise similarity or dissimilarity information in this paper we propose a kernel approach for semisupervised metric learning and present in detail two special cases of this kernel approach the metric learning problem is thus formulated as an optimization problem for kernel learning an attractive property of the optimization problem is that it is convex and hence has no local optima while a closed form solution exists for the first special case the second case is solved using an iterative majorization procedure to estimate the optimal solution asymptotically experimental results based on both synthetic and real world data show that this new kernel approach is promising for nonlinear metric learning

motion compensation for adaptive horizontal line array processing large aperture horizontal line arrays have small resolution cells and can be used to separate a target signal from an interference signal by array beamforming high resolution adaptive array processing can be used to place a null at the interference signal so that the array gain can be much higher than that of conventional beamforming but these nice features are significantly degraded by the source motion which reduces the time period under which the environment can be considered stationary from the array processing point of view for adaptive array processing a large number of data samples are generally required to minimize the variance of the cross spectral density or the covariance matrix between the array elements for a moving source and interference the penalty of integrating over a large number of samples is the spread of signal and interference energy to more than one or two eigenvalues the signal and interference are no longer clearly identified by the eigenvectors and consequently the ability to suppress the interference suffers we show in this paper that the effect of source motion can be compensated for the signal beam covariance matrix thus allowing integration over a large number of data samples without loss in the signal beam power we employ an equivalent of a rotating coordinate frame to track the signal bearing change and use the waveguide invariant theory to compensate the signal range change by frequency shifting

cross spectral matrix estimation effects on adaptive beamforming adaptive beamforming algorithms that use cross spectral matrix csm data to calculate optimum weights are sensitive to the number of unsmoothed csm samples used in the csm estimate assuming that the samples used in the csm estimate are independent identically gaussian distributed with zero mean it is shown that the output power of a minimum variance distortionless response mvdr beamformer using the estimated csm is biased low relative to the output power of a mvdr beamformer using the true csm using data from a horizontal line array deployed in a shallow water high noise environment the mvdr beamformer output power has been measured with various numbers of samples used in the csm estimate the number of csm samples was varied by changing the time bandwidth product the measured median beam noise levels are compared with the theory conclusions are drawn about the number of samples that should be used to obtain a reliable csm estimate and about how stationary the data are within the time bandwidth integration used in the csm estimates

inference in linear regression models with many covariates and heteroskedasticity the linear regression model is widely used in empirical work in economics statistics and many other disciplines researchers often include many covariates in their linear model specification in a

an optimized wild bootstrap method for evaluation of measurement uncertainties of dti derived parameters in human brain evaluation of measurement uncertainties or errors in diffusion tensor derived parameters is essential to quantify the sensitivity and specificity of these quantities as potential surrogate biomarkers for pathophysiological processes with diffusion tensor imaging dti computational methods such as the monte carlo simulation have provided insights into characterization of the measurement uncertainty in dti however due to the complexity of real brain data as well as different sources of variations during the image acquisition a robust estimator for dti measurement uncertainty in human brain is required recent studies have shown that wild bootstrap a novel nonparametric statistical method can potentially provide effective estimations of dti measurement uncertainties in human brain dti data in this study we further optimized the dti application of the wild bootstrap method for typical clinical applications we evaluated the validity of wild bootstrap utilizing numerical simulations with different combinations of dti protocol parameters and wild bootstrap experimental designs and quantitatively compared estimates of uncertainties from wild bootstrapping with those from monte carlo simulations our results demonstrate that a wild bootstrap implementation using at least 1000 wild bootstrap iterations with a type ii or type iii heteroskedasticity consistent covariance matrix estimator provides robust evaluations of most dti protocols

the moving blocks bootstrap and robust inference for linear least squares and quantile regressions abstract this paper presents the moving blocks bootstrap mbb as a new inference procedure for linear regression estimation which is robust to heteroskedasticity and autocorrelation of unknown forms the mbb covariance estimator is shown to provide heteroskedasticity and autocorrelation consistent hac standard errors for least squares ls and quantile regression qr coefficient estimators the mbb covariance estimator is shown to be asymptotically equivalent to the bartlett kernel estimator suggested by newey and west 1987 and the asymptotically optimal choice of the blocksize is discussed a monte carlo study is included showing that the mbb fares well in comparison to standard hac inference procedures considering strong mixing data generating processes the paper extends existing asymptotic results for the qr estimator the analogy to the ls case is stressed the paper analyzes both the cases of stochastic and of nonstochastic regressors and suggests two new grenander like conditions for the latter case the use of the mbb approach is illustrated for a practical example using a standard econometric package

the wild bootstrap tamed at last the wild bootstrap is studied in the context of regression models with heteroskedastic disturbances we show that in one very specific case perfect bootstrap inference is possible and a substantial reduction in the error in the rejection probability of a bootstrap test is available much more generally however the version of the wild bootstrap with this desirable property is without the skewness correction afforded by the currently most popular version of the wild bootstrap simulation experiments show that this does not prevent the preferred version from having the smallest error in rejection probability in small and medium sized samples

fixed effects estimation of structural parameters and marginal effects in panel probit models fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem in this paper i characterize the leading term of a large t expansion of the bias of the mle and estimators of average marginal effects in parametric fixed effects panel binary choice models for probit index coefficients the former term is proportional to the true value of the coefficients being estimated this result allows me to derive a lower bound for the bias of the mle i then show that the resulting fixed effects estimates of ratios of coefficients and average marginal effects exhibit no bias in the absence of heterogeneity and negligible bias for a wide variety of distributions of regressors and individual effects in the presence of heterogeneity i subsequently propose new bias corrected estimators of index coefficients and marginal effects with improved finite sample properties for linear and nonlinear models with predetermined regressors

multivariate regression analyses for categorical data summary it is common to observe a vector of discrete and or continuous responses in scientific problems where the objective is to characterize the dependence of each response on explanatory variables and to account for the association between the outcomes the response vector can comprise repeated observations on one variable as in longitudinal studies or genetic studies of families or can include observations for different variables this paper discusses a class of models for the marginal expectations of each response and for pairwise associations the marginal models are contrasted with log linear models two generalized estimating equation approaches are compared for parameter estimation the first focuses on the regression parameters the second simultaneously estimates the regression and association parameters the robustness and efficiency of each is discussed the methods are illustrated with analyses of two data sets from public health research

sequential bayesian sparse signal reconstruction using array data in this paper the sequential reconstruction of source waveforms under a sparsity constraint is considered from a bayesian perspective let the wave field which is observed by a sensor array be caused by a spatially sparse set of sources a spatially weighted laplace like prior is assumed for the source field and the corresponding weighted least absolute shrinkage and selection operator lasso cost function is derived after the weighted lasso solution has been calculated as the maximum a posteriori estimate at time step k the posterior distribution of the source amplitudes is analytically approximated the weighting of the laplace like prior for time step k 1 is then fitted to the approximated posterior distribution this results in a sequential update for the lasso weights thus a sequence of weighted lasso problems is solved for estimating the temporal evolution of a sparse source field the method is evaluated numerically using a uniform linear array in simulations and applied to data which were acquired from a towed horizontal array during the long range acoustic communications experiment

ordinal margin metric learning and its extension for cross distribution image data propose a novel ordinal margin metric learning ormml by separating the data with large margins and making them distributed in order extend ormml to cope with cross distribution application scenarios named cd ormml extensively demonstrate the superiority of the proposed metric learning methods to related state of the art methods in machine learning and computer vision fields a wide range of applications such as human age estimation and head pose recognition are related to ordinal data in which there exists an order relationship to perform such ordinal estimations in a desired metric space in this paper we first propose a novel ordinal margin metric learning ormml method by separating the data classes with a sequence of margins which makes the classes distribute orderly in the learned metric space then to cope with more realistic scenarios where the data are sampled with each class across multiple distributions we present a cross distribution variant of ormml coined as cd ormml by maximizing the correlation between distributions within each class when conducting metric learning finally extensive experiments on synthetic and publicly available image datasets demonstrate the superiority of the proposed methods in performance to the state of the art methods