nonparametric bayes modelling of count processes data on count processes arise in a variety of applications including longitudinal spatial and imaging studies measuring count responses the literature on statistical models for dependent count data is dominated by models built from hierarchical poisson components the poisson assumption is not warranted in many applied contexts and hierarchical poisson models make restrictive assumptions about overdispersion in marginal distributions in this article we propose a class of nonparametric bayes count process models constructed through rounding real valued underlying processes the proposed class of models accommodates situations in which separate count valued functional data are observed for each subject under study theoretical results on large support and posterior consistency are established and computational algorithms are developed based on markov chain monte carlo simulation the methods are evaluated via simulation and illustrated by application to longitudinal tumour counts and to asthma inhaler usage copyright 2013 oxford university press

bayesian isotonic regression and trend analysis in many applications the mean of a response variable can be assumed to be a nondecreasing function of a continuous predictor controlling for covariates in such cases interest often focuses on estimating the regression function while also assessing evidence of an association this article proposes a new framework for bayesian isotonic regression and order restricted inference approximating the regression function with a high dimensional piecewise linear model the nondecreasing constraint is incorporated through a prior distribution for the slopes consisting of a product mixture of point masses accounting for flat regions and truncated normal densities to borrow information across the intervals and smooth the curve the prior is formulated as a latent autoregressive normal process this structure facilitates efficient posterior computation since the full conditional distributions of the parameters have simple conjugate forms point and interval estimates of the regression function and posterior probabilities of an association for different regions of the predictor can be estimated from a single mcmc run generalizations to categorical outcomes and multiple predictors are described and the approach is applied to an epidemiology application

gibbs sampling for bayesian non conjugate and hierarchical models by using auxiliary variables summary we demonstrate the use of auxiliary or latent variables for sampling non standard densities which arise in the context of the bayesian analysis of non conjugate and hierarchical models by using a gibbs sampler their strategic use can result in a gibbs sampler having easily sampled full conditionals we propose such a procedure to simplify or speed up the markov chain monte carlo algorithm the strength of this approach lies in its generality and its ease of implementation the aim of the paper therefore is to provide an alternative sampling algorithm to rejection based methods and other sampling approaches such as the metropolis hastings algorithm

distributions generated by perturbation of symmetry with emphasis on a multivariate skew t distribution summary a fairly general procedure is studied to perturb a multivariate density satisfying a weak form of multivariate symmetry and to generate a whole set of non symmetric densities the approach is sufficiently general to encompass some recent proposals in the literature variously related to the skew normal distribution the special case of skew elliptical densities is examined in detail establishing connections with existing similar work the final part of the paper specializes further to a form of multivariate skew t density likelihood inference for this distribution is examined and it is illustrated with numerical examples

finite state vector quantization by exploiting interband and intraband correlations for subband image coding subband coding sbc with vector quantization vq has been shown to be an effective method for coding images at low bit rates we split the image spectrum into seven nonuniform subbands threshold vector quantization tvq and finite state vector quantization fsvq methods are employed in coding the subband images by exploiting interband and intraband correlations our new sbc fsvq schemes have the advantages of the subband vq scheme while reducing the bit rate and improving the image quality experimental results are given and comparisons are made using our new scheme and some other coding techniques in the experiments it is found that sbc fsvq schemes achieve the best peak signal to noise ratio psnr performance when compared to other methods at the same bit rate

embedded image coding using zerotrees of wavelet coefficients the embedded zerotree wavelet algorithm ezw is a simple yet remarkably effective image compression algorithm having the property that the bits in the bit stream are generated in order of importance yielding a fully embedded code the embedded code represents a sequence of binary decisions that distinguish an image from the null image using an embedded coding algorithm an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly also given a bit stream the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream in addition to producing a fully embedded bit stream the ezw consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images yet this performance is achieved with a technique that requires absolutely no training no pre stored tables or codebooks and requires no prior knowledge of the image source the ezw algorithm is based on four key concepts 1 a discrete wavelet transform or hierarchical subband decomposition 2 prediction of the absence of significant information across scales by exploiting the self similarity inherent in images 3 entropy coded successive approximation quantization and 4 universal lossless data compression which is achieved via adaptive arithmetic coding

matching on the estimated propensity score propensity score matching estimators rosenbaum and rubin 1983 are widely used in evaluation research to estimate average treatment effects in this article we derive the large sample distribution of propensity score matching estimators our derivations take into account that the propensity score is itself estimated in a first step prior to matching we prove that first step estimation of the propensity score affects the large sample distribution of propensity score matching estimators and derive adjustments to the large sample variances of propensity score matching estimators of the average treatment effect ate and the average treatment effect on the treated atet the adjustment for the ate estimator is negative or zero in some special cases implying that matching on the estimated propensity score is more efficient than matching on the true propensity score in large samples however for the atet estimator the sign of the adjustment term depends on the data generating process and ignoring the estimation error in the propensity score may lead to confidence intervals that are either too large or too small

on bayesian consistency we consider a sequence of posterior distributions based on a data dependent prior which we shall refer to as a pseudoposterior distribution and establish simple conditions under which the sequence is hellinger consistent it is shown how investigations into these pseudo posteriors assist with the understanding of some true posterior distributions including polya trees the infinite dimensional exponential family and mixture models

estimating smooth monotone functions many situations call for a smooth strictly monotone function f of arbitrary flexibility the family of functions defined by the differential equation d 2 f w df where w is an unconstrained coefficient function comprises the strictly monotone twice differentiable functions the solution to this equation is f c 0 c 1 d 1 exp d 1 w where c 0 and c 1 are arbitrary constants and d 1 is the partial integration operator a basis for expanding w is suggested that permits explicit integration in the expression of f in fitting data it is also useful to regularize f by penalizing the integral of w 2 since this is a measure of the relative curvature in f applications are discussed to monotone nonparametric regression to the transformation of the dependent variable in non linear regression and to density estimation

gaussian processes with monotonicity information a method for using monotonicity information in multivariate gaussian process regression and classification is proposed monotonicity information is introduced with virtual derivative observations and the resulting posterior is approximated with expectation propagation behaviour of the method is illustrated with artificial regression examples and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates

bayesian local extremum splines we consider shape restricted nonparametric regression on a closed set formula see text where it is reasonable to assume the function has no more than h local extrema interior to formula see text following a bayesian approach we develop a nonparametric prior over a novel class of local extremum splines this approach is shown to be consistent when modeling any continuously differentiable function within the class considered and is used to develop methods for testing hypotheses on the shape of the curve sampling algorithms are developed and the method is applied in simulation studies and data examples where the shape of the curve is of interest

multivariate student t regression models pitfalls and inference we consider likelihood based inference from multivariate regression models with independent student t errors some very intruiging pitfalls of both bayesian and classical methods on the basis of point observations are uncovered bayesian inference may be precluded as a consequence of the coarse nature of the data global maximization of the likelihood function is a vacuous exercise since the likelihood function is unbounded as we tend to the boundary of the parameter space a bayesian analysis on the basis of set observations is proposed and illustrated by several examples

calibration using constrained smoothing with applications to mass spectrometry data summary linear regressions are commonly used to calibrate the signal measurements in proteomic analysis by mass spectrometry however with or without a monotone e g log transformation data from such functional proteomic experiments are not necessarily linear or even monotone functions of protein or peptide concentration except over a very restricted range a computationally efficient spline procedure improves upon linear regression however mass spectrometry data are not necessarily homoscedastic more often the variation of measured concentrations increases disproportionately near the boundaries of the instruments measurement capability dynamic range that is the upper and lower limits of quantitation these calibration difficulties exist with other applications of mass spectrometry as well as with other broad scale calibrations therefore the method proposed here uses a functional data approach to define the calibration curve and also the limits of quantitation under the two assumptions i that the variance is a bounded convex function of concentration and ii that the calibration curve itself is monotone at least between the limits of quantitation but not necessarily outside these limits within this paradigm the limit of detection where the signal is definitely present but not measurable with any accuracy is also defined an iterative approach draws on existing smoothing methods to account simultaneously for both restrictions and is shown to achieve the global optimal convergence rate under weak conditions this approach can also be implemented when convexity is replaced by other bounded restrictions examples from addona et al 2009 nature biotechnology 27 663 641 both motivate and illustrate the effectiveness of this functional data methodology when compared with the simpler linear regressions and spline techniques

large time asymptotics in contaminant transport in porous media in this paper we derive large time solutions of the partial differential equations modelling contaminant transport in porous media for initial data with bounded support while the main emphasis is on two space dimensions for the sake of completeness we give a brief summary of the corresponding results for one space dimension the philosophy behind the paper is to compare the results of a formal asymptotic analysis of the governing equations as t to infty with numerical solutions of the complete initial value problem the analytic results are obtained using the method of dominant balance which identifies the dominant terms in the model equations determining the behaviour of the solution in the large time limit these are found in terms of time scaled space similarity variables and the procedure results in a reduction of the number of independent variables in the original partial differential equation this generates what we call a reduced equation the solution of which depends crucially on the value

nonparametric function estimation subject to monotonicity convexity and other shape constraints this paper uses free knot and fixed knot regression splines in a bayesian context to develop methods for the nonparametric estimation of functions subject to shape constraints in models with log concave likelihood functions the shape constraints we consider include monotonicity convexity and functions with a single minimum a computationally efficient mcmc sampling algorithm is developed that converges faster than previous methods for non gaussian models simulation results indicate the monotonically constrained function estimates have good small sample properties relative to i unconstrained function estimates and ii function estimates obtained from other constrained estimation methods when such methods exist also asymptotic results show the methodology provides consistent estimates for a large class of smooth functions two detailed illustrations exemplify the ideas

markov chains for exploring posterior distributions several markov chain methods are available for sampling from a posterior distribution two important examples are the gibbs sampler and the metropolis algorithm in addition several strategies are available for constructing hybrid algorithms this paper outlines some of the basic methods and strategies and discusses some related theoretical and practical issues on the theoretical side results from the theory of general state space markov chains can be used to obtain convergence rates laws of large numbers and central limit theorems for estimates obtained from markov chain methods these theoretical results can be used to guide the construction of more efficient algorithms for the practical use of markov chain methods standard simulation methodology provides several variance reduction techniques and also give guidance on the choice of sample size and allocation

a bayesian approach to additive semiparametric regression abstract we present a bayesian approach to estimating an additive semiparametric regression model which is robust to outliers the unknown curves are estimated by posterior means and are shown to be smoothing splines by using markov chain monte carlo an o mn algorithm is produced where n is the sample size and m is the total number of markov chain iterations previous exact approaches required o n3 operations making the estimation of large data sets impractical efficient methods for estimating the posterior means using mixture and backfitting estimates are developed the properties of the curve estimates are studied empirically using both simulated and real examples

bayesian isotonic density regression density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions and are well supported in many applications a rich variety of bayesian methods have been proposed for density regression but it is not clear whether such priors have full support so that any true data generating model can be accurately approximated this article develops a new class of density regression models that incorporate stochastic ordering constraints which are natural when a response tends to increase or decrease monotonely with a predictor theory is developed showing large support methods are developed for hypothesis testing with posterior computation relying on a simple gibbs sampler frequentist properties are illustrated in a simulation study and an epidemiology application is considered

affinely invariant matching methods with ellipsoidal distributions matched sampling is a common technique used for controlling bias in observational studies we present a general theoretical framework for studying the performance of such matching methods specifically results are obtained concerning the performance of affinely invariant matching methods with ellipsoidal distributions which extend previous results on equal percent bias reducing methods additional extensions cover conditionally affinely invariant matching methods for covariates with conditionally ellipsoidal distributions these results decompose the effects of matching into one subspace containing the best linear discriminant and the subspace of variables uncorrelated with the discriminant this characterization of the effects of matching provides a theoretical foundation for understanding the performance of specific methods such as matched sampling using estimated propensity scores calculations for such methods are given in subsequent articles

multivariate convex regression with adaptive partitioning we propose a new nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function convexity constraints are common in economics statistics operations research financial engineering and optimization but there is currently no multivariate method that is stable and computationally feasible for more than a few thousand observations we introduce convex adaptive partitioning cap which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions cap is a computationally efficient consistent method for convex regression we demonstrate empirical performance by comparing the performance of cap to other shape constrained and unconstrained regression methods for predicting weekly wages and value function approximation for pricing american basket options

large time behavior for convection diffusion equations in rn abstract we describe the large time behavior of solutions of the convection diffusion equation u t u a u q 1 u in 0 r n with a r n and q 1 1 n n 1 when q 1 1 n we prove that the large time behavior of solutions with initial data in l 1 r n is given by a uniparametric family of self similar solutions the relevant parameter is the mass of the solution that is conserved for all t our result extends to dimensions n 1 well known results on the large time behavior of solutions for viscous burgers equations in one space dimension the proof is based on la salle s invariance principle applied to the equation written in its self similarity variables when q 1 1 n the convection term is too weak and the large time behavior is given by the heat kernel in this case the result is easily proved applying standard estimates of the heat kernel on the integral equation related to the problem

a bayesian approach to non parametric monotone function estimation the paper proposes two bayesian approaches to non parametric monotone function estimation the first approach uses a hierarchical bayes framework and a characterization of smooth monotone functions given by ramsay that allows unconstrained estimation the second approach uses a bayesian regression spline model of smith and kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate the small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods asymptotic results are also given that show that bayesian methods provide consistent function estimators for a large class of smooth functions an example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple regression model where two functions are constrained to be monotone

stratification and weighting via the propensity score in estimation of causal treatment effects a comparative study estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics the propensity score the probability of treatment exposure conditional on covariates is the basis for two approaches to adjusting for confounding methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores we review popular versions of these approaches and related methods offering improved precision describe theoretical properties and highlight their implications for practice and present extensive comparisons of performance that provide guidance for practical use

on the bernstein von mises theorem with infinite dimensional parameters if there are many independent identically distributed observations governed by a smooth finite dimensional statistical model the bayes estimate and the maximum likelihood estimate will be close furthermore the posterior distribution of the parameter vector around the posterior mean will be close to the distribution of the maximum likelihood estimate around truth thus bayesian confidence sets have good frequentist coverage properties and conversely however even for the simplest infinite dimensional models such results do not hold the object here is to give some examples

bayesian estimation of the average treatment effect on the treated using inverse weighting we develop a bayesian approach to estimate the average treatment effect on the treated in the presence of confounding the approach builds on developments proposed by saarela et al in the context of marginal structural models using importance sampling weights to adjust for confounding and estimate a causal effect the bayesian bootstrap is adopted to approximate posterior distributions of interest and avoid the issue of feedback that arises in bayesian causal estimation relying on a joint likelihood we present results from simulation studies to estimate the average treatment effect on the treated evaluating the impact of sample size and the strength of confounding on estimation we illustrate our approach using the classic right heart catheterization data set and find a negative causal effect of the exposure on 30 day survival in accordance with previous analyses of these data we also apply our approach to the data set of the national center for health statistics birth data and obtain a negative effect of maternal smoking during pregnancy on birth weight

sampling based approaches to calculating marginal densities abstract stochastic substitution the gibbs sampler and the sampling importance resampling algorithm can be viewed as three alternative sampling or monte carlo based approaches to the calculation of numerical estimates of marginal probability distributions the three approaches will be reviewed compared and contrasted in relation to various joint probability structures frequently encountered in applications in particular the relevance of the approaches to calculating bayesian posterior densities for a variety of structured models will be discussed and illustrated

estimation of causal effects of binary treatments in unconfounded studies estimation of causal effects in non randomized studies comprises two distinct phases design without outcome data and analysis of the outcome data according to a specified protocol recently gutman and rubin 2013 proposed a new analysis phase method for estimating treatment effects when the outcome is binary and there is only one covariate which viewed causal effect estimation explicitly as a missing data problem here we extend this method to situations with continuous outcomes and multiple covariates and compare it with other commonly used methods such as matching subclassification weighting and covariance adjustment we show using an extensive simulation that of all methods considered and in many of the experimental conditions examined our new multiple imputation using two subclassification splines method appears to be the most efficient and has coverage levels that are closest to nominal in addition it can estimate finite population average causal effects as well as non linear causal estimands this type of analysis also allows the identification of subgroups of units for which the effect appears to be especially beneficial or harmful copyright 2015 john wiley sons ltd

extensions of the penalized spline of propensity prediction method of imputation little and an 2004 statistica sinica 14 949 968 proposed a penalized spline of propensity prediction pspp method of imputation of missing values that yields robust model based inference under the missing at random assumption the propensity score for a missing variable is estimated and a regression model is fitted that includes the spline of the estimated logit propensity score as a covariate the predicted unconditional mean of the missing variable has a double robustness dr property under misspecification of the imputation model we show that a simplified version of pspp which does not center other regressors prior to including them in the prediction model also has the dr property we also propose two extensions of pspp namely stratified pspp and bivariate pspp that extend the dr property to inferences about conditional means these extended pspp methods are compared with the pspp method and simple alternatives in a simulation study and applied to an online weight loss study conducted by kaiser permanente

combining propensity score matching with additional adjustments for prognostic covariates abstract propensity score matching refers to a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many covariates this matching is the observational study analog of randomization in ideal experiments but is far less complete as it can only balance the distribution of observed covariates whereas randomization balances the distribution of all covariates both observed and unobserved an important feature of propensity score matching is that it can be easily combined with model based regression adjustments or with matching on a subset of special prognostic covariates or combinations of prognostic covariates that have been identified as being especially predictive of the outcome variables we extend earlier results by developing approximations for the distributions of covariates in matched samples created with linear propensity score methods for the practically important situation where matching uses both the estimat

nonparametric regression using bayesian variable selection abstract this paper estimates an additive model semiparametrically while automatically selecting the significant independent variables and the appropriate power transformation of the dependent variable the nonlinear variables are modeled as regression splines with significant knots selected from a large number of candidate knots the estimation is made robust by modeling the errors as a mixture of normals a bayesian approach is used to select the significant knots the power transformation and to identify outliers using the gibbs sampler to carry out the computation empirical evidence is given that the sampler works well on both simulated and real examples and that in the univariate case it compares favorably with a kernel weighted local linear smoother the variable selection algorithm in the paper is substantially faster than previous bayesian variable selection algorithms

small sample degrees of freedom with multiple imputation an appealing feature of multiple imputation is the simplicity of the rules for combining the multiple complete data inferences into a final inference the repeated imputation inference rubin 1987 this inference is based on a t distribution and is derived from a bayesian paradigm under the assumption that the complete data degrees of freedom com are infinite but the number of imputations m is finite when com is small and there is only a modest proportion of missing data the calculated repeated imputation degrees of freedom m for the t reference distribution can be much larger than com which is clearly inappropriate following the bayesian paradigm we derive an adjusted degrees of freedom m with the following three properties for fixed m and estimated fraction of missing information m monotonically increases in com m is always less than or equal to com and m equals m when com is infinite a small simulation study demonstrates the superior frequentist performance when using m rather than m

isotonic regression and isotonic projection abstract the note describes the cones in the euclidean space admitting isotonic metric projection with respect to the coordinate wise ordering as a consequence it is shown that the metric projection onto the isotonic regression cone the cone defined by the general isotonic regression problem admits a projection which is isotonic with respect to the coordinate wise ordering

efficient estimation of average treatment effects using the estimated propensity score we are interested in estimating the average effect of a binary treatment on a scalar outcome if assignment to the treatment is exogenous or unconfounded that is independent of the potential outcomes given covariates biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the covariates rosenbaum and rubin 1983 show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates although adjusting for differences in the propensity score removes all the bias this can come at the expense of efficiency as shown by hahn 1998 heckman ichimura and todd 1998 and robins mark and newey 1992 we show that weighting by the inverse of a nonparametric estimate of the propensity score rather than the true propensity score leads to an efficient estimate of the average treatment effect we provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score

estimating and using propensity scores with partially missing data abstract investigators in observational studies have no control over treatment assignment as a result large differences can exist between the treatment and control groups on observed covariates which can lead to badly biased estimates of treatment effects propensity score methods are an increasingly popular method for balancing the distribution of the covariates in the two groups to reduce this bias for example using matching or subclassification sometimes in combination with model based adjustment to estimate propensity scores which are the conditional probabilities of being treated given a vector of observed covariates we must model the distribution of the treatment indicator given these observed covariates much work has been done in the case where covariates are fully observed we address the problem of calculating propensity scores when covariates can have missing values in such cases which commonly arise in practice the pattern of missing covariates can be prognostically important and

asymptotic profiles for covection diffusion equations with variable diffusion abstract we investigate the large time behavior of solutions of the convection diffusion equation u t div a x u d u q 1 u d r n in 0 r n with integrable initial data u0 x we take a x 1 b x 0 with b smooth and decaying to zero fast enough as x when q 1 1 n it is known that the solutions behave in a first approximation like the solutions of the head equation taking the same initial data as t we show here the influence of the nonlinear term and the variable diffusion in the large time behavior by obtaining the second term in the asymptotic development of solutions as t

asymptotics in the critical case for whitham type equations abstract consider the cauchy problem for nonlinear dissipative evolution equations u t n u u l u 0 x r t 0 u 0 x u 0 x x r where l is the linear pseudodifferential operator l u f x l u and the nonlinearity is a quadratic pseudodifferential operator n u u f x r a t y u t y u t y d y u f x u is direct fourier transformation let the initial data u 0 h 0 h 0 1 2 are sufficiently small and have a non zero total mass m u 0 x d x 0 here h n m l 2 x m i x n x l 2 is the weighted sobolev space then we prove that the main term of the large time asymptotics of solutions in the critical case is given by the self similar solution defined uniquely by the total mass m of the initial data

the central role of the propensity score in observational studies for causal effects abstract the results of observational studies are often disputed because of nonrandom treatment assignment for example patients at greater risk may be overrepresented in some treatment group this paper discusses the central role of propensity scores and balancing scores in the analysis of observational studies the propensity score is the estimated conditional probability of assignment to a particular treatment given a vector of observed covariates both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates applications include matched sampling on the univariate propensity score which is equal percent bias reducing under more general conditions than required for discriminant matching multivariate adjustment by subclassification on balancing scores where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations and visual representation of multivariate adjustment by a two dimensional plot author

multivariate matching methods that are monotonic imbalance bounding we introduce a new monotonic imbalance bounding mib class of matching methods for causal inference with a surprisingly large number of attractive statistical properties mib generalizes and extends in several new directions the only existing class equal percent bias reducing epbr which is designed to satisfy weaker properties and only in expectation we also offer strategies to obtain specific members of the mib class and analyze in more detail a member of this class called coarsened exact matching whose properties we analyze from this new perspective we offer a variety of analytical results and numerical simulations that demonstrate how members of the mib class can dramatically improve inferences relative to epbr based matching methods

on bayesian modeling of fat tails and skewness abstract we consider a bayesian analysis of linear regression models that can account for skewed error distributions with fat tails the latter two features are often observed characteristics of empirical datasets and we formally incorporate them in the inferential process a general procedure for introducing skewness into symmetric distributions is first proposed even though this allows for a great deal of flexibility in distributional shape tail behavior is not affected applying this skewness procedure to a student t distribution we generate a skewed student distribution which displays both flexible tails and possible skewness each entirely controlled by a separate scalar parameter the linear regression model with a skewed student error term is the main focus of the article we first characterize existence of the posterior distribution and its moments using standard improper priors and allowing for inference on skewness and tail parameters for posterior inference with this model we suggest

large sample properties of matching estimators for average treatment effects matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases the absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data in this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results we focus on matching with replacement with a fixed number of matches first we show that matching estimators are not n 1 2 consistent in general and describe conditions under which matching estimators do attain n 1 2 consistency second we show that even in settings where matching estimators are n 1 2 consistent simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound third we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions software for implementing these methods is available in matlab stata and r

generalized monotonic regression using random change points we introduce a procedure for generalized monotonic curve fitting that is based on a bayesian analysis of the isotonic regression model conventional isotonic regression fits monotonically increasing step functions to data in our approach we treat the number and location of the steps as random for each step level we adopt the conjugate prior to the sampling distribution of the data as if the curve was unconstrained we then propose to use markov chain monte carlo simulation to draw samples from the unconstrained model space and retain only those samples for which the monotonic constraint holds the proportion of the samples collected for which the constraint holds can be used to provide a value for the weight of evidence in terms of bayes factors for monotonicity given the data using the samples probability statements can be made about other quantities of interest such as the number of change points in the data and posterior distributions on the location of the change points can be provided the method is illustrated throughout by a reanalysis of the leukaemia data studied by schell and singh

subband coding of images subband coding has become quite popular for the source encoding of speech this paper presents a simple yet efficient extension of this concept to the source coding of images we specify the constraints for a set of two dimensional quadrature mirror filters qmf s for a particular frequency domain partition and show that these constraints are satisfied by a separable combination of one dimensional qmf s bits are then optimally allocated among the subbands to minimize the mean squared error for dpcm coding of the subbands also an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask optimum quantization is employed with quantizers matched to the laplacian distribution subband coded images are presented along with their signal to noise ratios snr s the snr performance of the subband coder is compared to that of the adaptive discrete cosine transform dct vector quantization and differential vector quantization for bit rates of 0 67 1 0 and 2 0 bits per pixel for 256 256 monochrome images the adaptive subband coder has the best snr performance

characterizing the effect of matching using linear propensity score methods with normal distributions summary matched sampling is a standard technique for controlling bias in observational studies due to specific covariates since rosenbaum rubin 1983 multivariate matching methods based on estimated propensity scores have been used with increasing frequency in medical educational and sociological applications we obtain analytic expressions for the effect of matching using linear propensity score methods with normal distributions these expressions cover cases where the propensity score is either known or estimated using either discriminant analysis or logistic regression as is typically done in current practice the results show that matching using estimated propensity scores not only reduces bias along the population propensity score but also controls variation of components orthogonal to it matching on estimated rather than population propensity scores can therefore lead to relatively large variance reduction as much as a factor of two in common matching settings where close matches are possible approximations are given for the magnitude of this variance reduction which can be computed using estimates obtained from the matching pools related expressions for bias reduction are also presented which suggest that in difficult matching situations the use of population scores leads to greater bias reduction than the use of estimated scores

robust estimation of causal effects of binary treatments in unconfounded studies with dichotomous outcomes the estimation of causal effects has been the subject of extensive research in unconfounded studies with a dichotomous outcome y cangul chretien gutman and rubin 2009 demonstrated that logistic regression for a scalar continuous covariate x is generally statistically invalid for testing null treatment effects when the distributions of x in the treated and control populations differ and the logistic model for y given x is misspecified in addition they showed that an approximately valid statistical test can be generally obtained by discretizing x followed by regression adjustment within each interval defined by the discretized x this paper extends the work of cangul et al 2009 in three major directions first we consider additional estimation procedures including a new one that is based on two independent splines and multiple imputation second we consider additional distributional factors and third we examine the performance of the procedures when the treatment effect is non null of all the methods considered and in most of the experimental conditions that were examined our proposed new methodology appears to work best in terms of point and interval estimation copyright 2012 john wiley sons ltd

dealing with limited overlap in estimation of average treatment effects summary estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups this lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification in such cases researchers have often used ad hoc methods for trimming the sample we develop a systematic approach to addressing lack of overlap we characterize optimal subsamples for which the average treatment effect can be estimated most precisely under some conditions the optimal selection rules depend solely on the propensity score for a wide range of distributions a good approximation to the optimal rule is provided by the simple rule of thumb to discard all units with estimated propensity scores outside the range 0 1 0 9

reducing bias in observational studies using subclassification on the propensity score abstract the propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates previous theoretical arguments have shown that subclassification on the propensity score will balance all observed covariates subclassification on an estimated propensity score is illustrated using observational data on treatments for coronary artery disease five subclasses defined by the estimated propensity score are constructed that balance 74 covariates and thereby provide estimates of treatment effects using direct adjustment these subclasses are applied within sub populations and model based adjustments are then used to provide estimates of treatment effects within these sub populations two appendixes address theoretical issues related to the application the effectiveness of subclassification on the propensity score in removing bias and balancing properties of propensity scores with incomplete data

model misspecification and robustness in causal inference comparing matching with doubly robust estimation in this paper we compare the robustness properties of a matching estimator with a doubly robust estimator we describe the robustness properties of matching and subclassification estimators by show

side match and overlap match vector quantizers for images a class of vector quantizers with memory that are known as finite state vector quantizers fsvqs in the image coding framework is investigated two fsvq designs namely side match vector quantizers smvqs and overlap match vector quantizers omvqs are introduced these designs take advantage of the 2 d spatial contiguity of pixel vectors as well as the high spatial correlation of pixels in typical gray level images smvq and omvq try to minimize the granular noise that causes visible pixel block boundaries in ordinary vq for 512 by 512 gray level images smvq and omvq can achieve communication quality reproduction at an average of 1 2 b pixel per image frame and acceptable quality reproduction because block boundaries are less visible the perceived improvement in quality over ordinary vq is even greater owing to the structure of smvq and omvq simple variable length noiseless codes can achieve as much as 60 bit rate reduction over fixed length noiseless codes

bayesian spatial monotonic multiple regression we consider monotonic multiple regression for contiguous regions the regression functions vary regionally and may exhibit spatial structure we develop bayesian nonparametric methodology that permits estimation of both continuous and discontinuous functional shapes using marked point process and reversible jump markov chain monte carlo techniques spatial dependence is incorporated by a flexible prior distribution which is tuned using cross validation and bayesian optimization we derive the mean and variance of the prior induced by the marked point process approach asymptotic results show consistency of the estimated functions posterior realizations enable variable selection the detection of discontinuities and prediction in simulations and in an application to a norwegian insurance data set our methodology shows better performance than existing approaches

a two step bayesian approach for propensity score analysis simulations and case study a two step bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous bayesian propensity score approaches the corresponding variance estimators are also provided the two step bayesian propensity score is provided for three methods of implementation propensity score stratification weighting and optimal full matching three simulation studies and one case study are presented to elaborate the proposed two step bayesian propensity score approach results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist based treatment effect a slight advantage is shown for the bayesian approach in small samples results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results however greater precision around the correct treatment effect parameter yields quite good results with slight improvement seen with greater precision in the propensity score equation a comparison of coverage rates for the conventional frequentist approach and proposed bayesian approach is also provided the case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non informative

monotonic calibrated interpolated look up tables real world machine learning applications may have requirements beyond accuracy such as fast evaluation times and interpretability in particular guaranteed monotonicity of the learned function with respect to some of the inputs can be critical for user confidence we propose meeting these goals for low dimensional machine learning problems by learning flexible monotonic functions using calibrated interpolated look up tables we extend the structural risk minimization framework of lattice regression to monotonic functions by adding linear inequality constraints in addition we propose jointly learning interpretable calibrations of each feature to normalize continuous features and handle categorical or missing data at the cost of making the objective non convex we address large scale learning through parallelization mini batching and random sampling of additive regularizer terms case studies on real world problems with up to sixteen features and up to hundreds of millions of training samples demonstrate the proposed monotonic functions can achieve state of the art accuracy in practice while providing greater transparency to users

an analysis of bayesian inference for nonparametric regression abstract the observation model y sub i beta i n epsilon sub i 1 or n is considered where the epsilon s are i i d mean zero and variance sigma sq and beta is an unknown smooth function a gaussian prior distribution is specified by assuming beta is the solution of a high order stochastic differential equation the estimation error delta beta beta average is analyzed where beta average is the posterior expectation of beta asymptotic posterior and sampling distributional approximations are given for abs val del square when abs val square is one of a family of norms natural to the problem it is shown that the frequentist coverage probability of a variety of 1 alpha posterior probability regions tends to be larger than 1 alpha but will be infinitely often less than any epsilon 0 as n approaches infinity with prior probability 1 a related continuous time signal estimation problem is also studied keywords bayesian inference nonparametric regression confidence regions signal extraction smoothing splices

full matching in an observational study of coaching for the sat among matching techniques for observational studies full matching is in principle the best in the sense that its alignment of comparable treated and control subjects is as good as that of any alternate method and potentially much better this article evaluates the practical performance of full matching for the first time modifying it in order to minimize variance as well as bias and then using it to compare coached and uncoached takers of the sat in this new version with restrictions on the ratio of treated subjects to controls within matched sets full matching makes use of many more observations than does pair matching but achieves far closer matches than does matching with k 2 controls prior to matching the coached and uncoached groups are separated on the propensity score by 1 1 sds full matching reduces this separation to 1 or 2 of an sd in older literature comparing matching and regression cochran expressed doubts that any method of adjustment could substantially reduce observed bias

divergence measures based on the shannon entropy a novel class of information theoretic divergence measures based on the shannon entropy is introduced unlike the well known kullback divergences the new measures do not require the condition of absolute continuity to be satisfied by the probability distributions involved more importantly their close relationship with the variational distance and the probability of misclassification error are established in terms of bounds these bounds are crucial in many applications of divergence measures the measures are also well characterized by the properties of nonnegativity finiteness semiboundedness and boundedness

bayesian monotone regression using gaussian process projection shape constrained regression analysis has applications in dose response modelling environmental risk assessment disease screening and many other areas incorporating the shape constraints can improve estimation efficiency and avoid implausible results we propose a novel method focusing on monotone curve and surface estimation which uses gaussian process projections our inference is based on projecting posterior samples from the gaussian process we develop theory on continuity of the projection and rates of contraction our approach leads to simple computation with good performance in finite samples the proposed projection method can also be applied to other constrained function estimation problems including those in multivariate settings

doubly robust estimation in missing data and causal inference models the goal of this article is to construct doubly robust dr estimators in ignorable missing data and causal inference models in a missing data model an estimator is dr if it remains consistent when either but not necessarily both a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified because with observational data one can never be sure that either a missingness model or a complete data model is correct perhaps the best that can be hoped for is to find a dr estimator dr estimators in contrast to standard likelihood based or nonaugmented inverse probability weighted estimators give the analyst two chances instead of only one to make a valid inference in a causal inference model an estimator is dr if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct inference based on dr estimators should improve upon previous approaches indeed we present the results of simulation studies which demonstrate that the finite sample performance of dr estimators is as impressive as theory would predict the proposed method is applied to a cardiovascular clinical trial

the monotone smoothing of scatterplots we consider the problem of summarizing a scatterplot with a smooth monotone curve a solution that combines local averaging and isotonic regression is proposed and we demonstrate its use with two examples in the second example the procedure is applied in a regression setting to some data from box and cox 1964 and it is shown how this new procedure generalizes box and cox s well known family of transformations in the same example the bootstrap is applied to obtain a measure of the variability of the procedure

ellipsoidally symmetric extensions of the general location model for mixed categorical and continuous data summary the general location model olkin tate 1961 krzanowski 1980 1982 little schluchter 1985 has categorical variables marginally distributed as a multinomial and continuous variables conditionally normally distributed with different means across cells defined by the categorical variables but a common covariance matrix across cells two extensions of the general location model are obtained the first replaces the common covariance matrix with different but proportional covariance matrices where the proportionality constants are to be estimated the second replaces the multivariate normal distributions of the first extension with multivariate t distributions where the degrees of freedom can also vary across cells and are to be estimated the t distribution is just one example of more general ellipsoidally symmetric distributions that can be used in place of the normal these extensions can provide more accurate fits to real data and can be viewed as tools for robust inference moreover the models can be very useful for multiple imputation of ignorable missing values maximum likelihood estimation using the aecm algorithm meng van dyk 1997 is presented as is a monotone data gibbs sampling scheme for drawing parameters and missing values from their posterior distributions to illustrate the techniques a numerical example is presented

nonparametric bayesian testing for monotonicity this paper adopts a nonparametric bayesian approach to testing whether a function is monotone two new families of tests are constructed the first uses constrained smoothing splines with a hierarchical stochastic process prior that explicitly controls the prior probability of monotonicity the second uses regression splines together with two proposals for the prior over the regression coefficients via simulation the finite sample performance of the tests is shown to improve upon existing frequentist and bayesian methods the asymptotic properties of the bayes factor for comparing monotone versus nonmonotone regression functions in a gaussian model are also studied our results significantly extend those currently available which chiefly focus on determining the dimension of a parametric linear model

affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions in observational studies designed to estimate the effects of interventions or exposures such as cigarette smoking it is desirable to try to control background differences between the treated group e g current smokers and the control group e g never smokers on covariates x e g age education matched sampling attempts to effect this control by selecting subsets of the treated and control groups with similar distributions of such covariates this paper examines the consequences of matching using affinely invariant methods when the covariate distributions are discriminant mixtures of proportional ellipsoidally symmetric dmpes distributions a class herein defined which generalizes the ellipsoidal symmetry class of rubin and thomas ann statist 20 1992 1079 1093 the resulting generalized results help indicate why earlier results hold quite well even when the simple assumption of ellipsoidal symmetry is not met e g biometrics 52 1996 249 264 extensions to conditionally affinely invariant matching with conditionally dmpes distributions are also discussed