bayesian solutions to graduate admissions and related selection problems a graduate program has to decide how many offers of admission to make to achieve a targeted number of acceptances for its entering class given uncertainty about how many will accept such offers we first structure and solve selection problems under the assumption that probabilities associated with uncertain outcomes have assumed values using symmetric and asymmetric loss functions and show sensitivity of solutions to errors in these assumed values then we show how these problems can be solved when these probabilities are heterogeneous and have to be estimated from sample data

a new approach for aggregating edge points into line segments abstract a technique is presented for aggregating edge points into polylines which can effectively represent object contours the technique is based on a new formulation of hough transform ht for detection of line segments the space requirement of the ht is brought down by considering a different parameterization of straight lines in this method the process of edge linking and boundary approximation are combined into a single algorithm consequently the scheme is computationally more efficient than the classical boundary approximation techniques which require a separate edge linking algorithm for preparing their input the linked list of edge points experimental results presented here highlight the effectiveness of this method for approximating object boundaries of polygonal as well as curved shapes present in the images of complex multi object scenes even in the presence of noise further the parallel algorithm proposed herein for implementation of this technique makes it an ideal choice for real time applications

a comparative study of different sets of variables for solving compressible and incompressible flows a globally conservative galerkin least squares formulation which attains correct shock structure is developed for any choice of variables only the choice of entropy variables satisfies exactly the discrete clausius duhem inequality without any dissipative mechanisms whereas for the rest of the variables artificial diffusion is required to guarantee entropy production the limit of the formulation is well defined for entropy variables and the primitive variables p u t leading to conservative incompressible formulations the approach is stable for any continuous interpolations both for compressible and incompressible flows a comparative study of different variables is performed indicating that entropy variables and the primitive variables p u t possess the most attributes for practical problem solving

robust forecast combinations abstract forecast outliers commonly occur in economic financial and other areas of forecasting applications in the literature of forecast combinations there have been only a few studies exploring how to deal with outliers in this work we propose two robust combining methods based on the after algorithm yang 2004a our approach utilizes robust loss functions in order to reduce the influence of outliers oracle inequalities for certain versions of these methods are obtained which show that the combined forecasts automatically perform as well as the best individual among the pool of original forecasts systematic simulations and data examples show that the robust methods outperform the after algorithm when outliers are likely to occur and perform on par with after when there are no outliers comparison of the robust afters with some commonly used combining methods also shows their potential advantages

parameter estimates of general failure rate model a bayesian approach abstract the failure rate function plays an important role in studying the lifetime distributions in reliability theory and life testing models a study of the general failure rate model r t a b t 1 under squared error loss function taking a and b independent exponential random variables has been performed in the literature in this article we consider a and b not necessarily independent the estimates of the parameters a and b under squared error loss linex loss and entropy loss functions are obtained and further examined using simulated and real data sets

optimal forecast combinations under general loss functions and forecast error distributions existing results on the properties and performance of forecast combinations have been derived in the context of mean squared error loss under this loss function empirical studies have generally found that equally weighted combined forecasts lead to better performance than estimates of optimal forecast combination weights which in turn outperform the best individual predictions we show that this and other results can be overturned when asymmetries are introduced in the loss function and the forecast error distribution is skewed we characterize the optimal combination weights for the most commonly used alternatives to mean squared error loss and demonstrate how the degree of asymmetry in the loss function and skews in the underlying forecast error distribution can significantly change the optimal combination weights we also propose estimation methods and investigate their small sample properties in simulations and in an inflation forecasting exercise

bayesian estimation for nonstandard loss functions using a parametric family of estimators bayesian estimation with other loss functions than the standard hit or miss loss or the quadratic loss often yields optimal bayesian estimators obes that can only be formulated as optimization problems and which have to be solved for each new observation the contribution of this paper is to introduce a new parametric family of estimators to circumvent this problem by restricting the estimator to lie in this family we split the estimation problem into two parts in a first step we have to find the best estimator with respect to the bayes risk for a given nonstandard loss function which has to be done only once the second step then calculates the estimate for an observation using importance sampling the computational complexity of this second step is therefore comparable to that of an mmse estimator if the mmse estimator also uses monte carlo integration we study the proposed parametric family using two examples and show that the estimator family gives for both a good approximation of the obe

bayes risk reduction of estimators using artificial observation noise this paper studies noise enhanced ne estimators which are constructed from an original estimator by artificially adding noise to the observation and computing the expected estimator output by this expectation operation we take into account the neighbourhood of an observation and the resulting ne estimator often has a smaller bayes risk than the original one we derive some general properties of this estimator and also present a method to obtain a suitable approximation of the optimal ne estimator which can be computed numerically by solving a constrained optimization problem finally we study two examples to show the bayes risk improvement that we can obtain from the ne estimator and we compare it to the stochastic resonance estimator

optimal critical values of pre tests when estimating the regression error variance analytical findings under a general loss structure this paper re visits the problem of estimating the regression error variance in a linear multiple regression model after preliminary hypothesis tests for either linear restrictions on the coefficients or homogeneity of variances there is an extensive literature that discusses these problems particularly in terms of the sampling properties of the pre test estimators using various loss functions as the basis for risk analysis in this paper a unified framework for analysing the risk properties of these estimators is developed under a general class of loss structures that incorporates virtually all first order differentiable losses particular consideration is given to the choice of critical values for the pre tests analytical results indicate that an level substantially higher than those normally used may be appropriate for optimal risk properties under a wide range of loss functions the paper also generalizes some known analytical results in the pre test literature and proves other results only previously shown numerically

comparison of a regular and an irregular decomposition of regions and volumes a comparison is made between k trees i e quadtrees and octrees which are regular decompositions of volumes and new irregular decompositions called r trees this comparison is made within the context of the representation problems that might be associated with a robotic system the results show that the irregular decomposition is independent of object position and can provide a more efficient encoding for certain shapes however detecting intersections between r trees requires an algorithm of greater complexity than that of interference detection for octrees due to the irregularity of the r tree decomposition

making and evaluating point forecasts typically point forecasting methods are compared and assessed by means of an error measure or scoring function with the absolute error and the squared error being key examples the individual scores are averaged over forecast cases to result in a summary measure of the predictive performance such as the mean absolute error or the mean squared error i demonstrate that this common practice can lead to grossly misguided inferences unless the scoring function and the forecasting task are carefully matched effective point forecasting requires that the scoring function be specified ex ante or that the forecaster receives a directive in the form of a statistical functional such as the mean or a quantile of the predictive distribution if the scoring function is specified ex ante the forecaster can issue the optimal point forecast namely the bayes rule if the forecaster receives a directive in the form of a functional it is critical that the scoring function be consistent for it in the sense that t

signal to noise ratios performance criteria and transformations for the analysis of designed experiments taguchi uses performance criteria that he calls signal to noise sn ratios three such criteria are here denoted by sn t sn l and sn s the criterion sn t was to be used in preference to the standard deviation for the problem of achieving for some quality characteristic y the smallest mean squared error about an operating target value leon shoemaker and kacker 1987 showed how sn t was appropriate to solve this problem only when y was proportional to y on that assumption the same result could be obtained more simply by conducting the analysis in terms of log y rather than y a more general transformation approach is here introduced for other commonly met kinds of dependence between y and y including no dependence and a lambda plot is presented that uses the data to suggest an appropriate transformation the criteria sn l and sn s were for problems in which the objective was to make the response as large or as small as possible it is arg

noninformative priors and frequentist risks of bayesian estimators of vector autoregressive models in this study we examine posterior properties and frequentist risks of bayesian estimators based on several noninformative priors in vector autoregressive var models we prove existence of the posterior distributions and posterior moments under a general class of priors using a variety of priors in this class we conduct numerical simulations of posteriors we find that in most examples bayesian estimators with a shrinkage prior on the var coefficients and the reference prior of yang and berger ann statist 22 1994 1195 on the var covariance matrix dominate mle bayesian estimators with the diffuse prior and bayesian estimators with the prior used in rats we also examine the informative minnesota prior and find that its performance depends on the nature of the data sample and on the tightness of the minnesota prior a tightly set minnesota prior is better when the data generating processes are similar to random walks but the shrinkage prior or constant prior can be better otherwise

properties of optimal forecasts under asymmetric loss and nonlinearity abstract evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows using analytical results we show that standard properties of optimal forecasts can be invalid under asymmetric loss and nonlinear data generating processes and thus may be very misleading as a benchmark for an optimal forecast we establish instead that a suitable transformation of the forecast error known as the generalized forecast error possesses an equivalent set of properties the paper also provides empirical examples to illustrate the significance in practice of asymmetric loss and nonlinearities and discusses the effect of parameter estimation error on optimal forecasts

pay phones parking meters vending machines and optimal bayesian decisions on collection times payphones parking meters and vending machines illustrate the modern business practice of substituting machinery for manpower they do not eliminate manual labor completely because full coin boxes still must be replaced and vending machines still must be stocked deciding when to replace a coin box is important with unequal losses resulting from underestimation and overestimation this article derives optimal methodology for this problem by incorporating collection history and specifying common prior distributions over average daily fill rate and standard deviation at each box the approach is implemented and analyzed on collection records from 11 308 pay phones over a large geographical region when the loss from overestimation is 19 times that from underestimation our methods outperform the one in current use at least 69 9 of the time translating into average potential collection cost reductions exceeding 21

multicomponent flow calculations by a consistent primitive algorithm the dynamics of inviscid multicomponent fluids may be modelled by the euler equations augmented by one or more additional species equation s attempts to compute solutions for extended euler models in conservation form show strong oscillations and other computational inaccuracies near material interfaces these are due to erroneous pressure fluctuations generated by the conservative wave model this problem does not occur in single component computations and arises only in the presence of several species a nonconservative primitive euler formulation is proposed which results in complete elimination of the oscillations the numerical algorithm uses small viscous perturbations to remove leading order conservation errors and is conservative to the order of numerical approximation numerical experiments show clean monotonic solution profiles with acceptably small conservation error for shocks of weak to moderate strengths

reliability analysis of a class of exponential distribution under record values in this paper the estimation of the parameters as well as survival and hazard functions for a class of an exponential family are presented by using bayesian and non bayesian approaches under record values maximum likelihood and interval estimation are derived for the model parameters and bayes estimators of reliability performances are obtained under symmetric and asymmetric loss functions when the parameters have discrete and continuous priors respectively finally two numerical examples are presented to illustrate the proposed results an algorithm is introduced to generate record data then the numerical example is given by using monte carlo simulation and different estimates results are compared

screening as an application of decision theory we develop a decision theoretical approach to setting the threshold for a screening procedure that declares each examined subject as a positive or a negative it is fundamentally different from maximising the youden index the method incorporates the consequences of the two kinds of bad decisions false positives and false negatives by means of a set of plausible loss functions elicited from a subject matter expert or committee we present details for several classes of loss functions and within group distributions of the outcomes we outline extensions related to mixture distributions and compositions of loss functions we illustrate the method on simulated examples and apply it to real datasets

bayesian and non bayesian approaches to statistical inference and decision making bayesian and non bayesian approaches to statistical inference and decision making are discussed and compared a pragmatic criterion success in practice as well as logical consistency are emphasized in comparing alternative approaches attention is given to learning models probability concepts prior information axiom systems and selected technical aspects of bayesian and non bayesian approaches

an informed strategy for matching models to images of fabricated objects abstract a new method for the matching of 2 dimensional scenes of single and multiple objects with a previously formed model database is presented and discussed the system is based on a two tier organisation of the model database consisting of object centred and binary relation centred structures this is formed to reduce the complexity of the matching process yet retain a robust and accurate identification procedure in the event of an incomplete and inaccurate segmented description of the scene which is a probable consequence of non idealised viewing conditions examples of system performance in identifying single and multiple component images are presented

performance measures independent of adjustment an explanation and extension of taguchi s signal to noise ratios parameter design is a method popularized by japanese quality expert g taguchi for designing products and manufacturing processes that are robust to uncontrollable variations in parameter design taguchi s stated objective is to find the settings of product or process design parameters that minimize average quadratic loss that is the average squared deviation of the response from its target value yet in practice to choose the settings of design parameters he maximizes a set of measures called signal to noise ratios in general he gives no connection between these two optimization problems in this article we show that for certain underlying models for the product or process response maximization of the signal to noise ratio leads to minimization of average quadratic loss the signal to noise ratios take advantage of the existence of special design parameters called adjustment parameters when these parameters exist use of the signal to noise ratio allows the parameter design optimization procedu

properties of ordinary least squares estimators in regression models with nonspherical disturbances abstract a general discussion is presented of the properties of the ols estimator in regression models where the disturbances do not have a scalar identity covariance matrix several new and interesting characterizations are provided together with a synthesis of existing results a distinguishing feature of the paper is the unified treatment of the issues of efficiency and inference the theoretical material is illustrated by examples covering a wide range of models of interest to applied econometricians

a high order godunov method for multiple condensed phases presented in this work is a nonlinear adaptive nonreflecting boundary condition nrbc that when compared with classical local nrbcs further reduces the wave reflection error at far fields in the numerical simulation of wave dominated problems a nonlinear procedure can considerably improve the accuracy of nrbcs even if the problem itself is linear as in the case of the 2d wave equation in fact the first and second order nrbcs of engquist and majda can be modified by adding proper weights to the coefficients of the difference equations the weights are adaptively determined in such a way that the angle of complete absorption is locally coincident with the outgoing wave direction the theoretical reflection coefficients and the numerical experiments show that a considerable reduction of the numerical error due to wave reflections is achieved by applying the present adaptive algorithm

design of progressively censored group sampling plans for weibull distributions an optimization problem optimization algorithms provides efficient solutions to many statistical problems essentially the design of sampling plans for lot acceptance purposes is an optimization problem with several constraints usually related to the quality levels required by the producer and the consumer an optimal acceptance sampling plan is developed in this paper for the weibull distribution with unknown scale parameter the proposed plan combines grouping of items sudden death testing in each group and progressive group removals and its decision criterion is based on the uniformly most powerful life test a mixed integer programming problem is first solved for determining the minimum number of failures required and the corresponding acceptance constant the optimal number of groups is then obtained by minimizing a balanced estimation of the expected test cost excellent approximately optimal solutions are also provided in closed forms the sampling plan is considerably flexible and allows to save experimental time and cost in general our methodology achieves solutions that are quite robust to small variations in the weibull shape parameter a numerical example about a manufacturing process of gyroscopes is included for illustration

recognition of occluded objects with heuristic search abstract this paper presents a new heuristic search based approach for recognition of partially obscured planar shapes based on a general scheme for representing the planar shapes in terms of their contour segments a state space formulation is obtained for the recognition problem the search in the state space is guided by an admissible heuristic function which is not dependent upon the features actually used for representing the shapes some schemes for toning up the efficiency of the method are also discussed a study of the method was carried out by experimenting with some typical objects and results of experimentation are presented

composite binary losses we study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the composition of a proper loss with a link function we characterise when margin losses can be proper composite losses explicitly show how to determine a symmetric loss in full from half of one of its partial losses introduce an intrinsic parametrisation of composite binary losses and give a complete characterisation of the relationship between proper losses and classification calibrated losses we also consider the question of the best surrogate binary loss we introduce a precise notion of best and show there exist situations where two convex surrogate losses are incommensurable we provide a complete explicit characterisation of the convexity of composite binary losses in terms of the link function and the weight function associated with the proper loss which make up the composite loss this characterisation suggests new ways of surrogate tuning as well as providing an explicit characterisation of when bregman divergences on the unit interval are convex in their second argument finally in an appendix we present some new algorithm independent results on the relationship between properness convexity and robustness to misclassification noise for binary losses and show that all convex proper losses are non robust to misclassification noise

convergence rate of empirical bayes estimation for two dimensional truncation parameters under linex loss this paper deals with the empirical bayes eb estimation for two dimensional truncation parameters under linex loss by using kernel estimation of density function we construct the eb estimation of parameters under some suitable conditions we prove that the constructed eb estimations is asymptotically optimal and we obtain its convergence rate

attributed string matching with merging for shape recognition a new structural approach to shape recognition using attributed string matching with merging is proposed after illustrating the disadvantages of conventional symbolic string matching using changes deletions and insertions attributed strings are suggested for matching each attributed string is an ordered sequence of shape boundary primitives each representing a basic boundary structural unit line segment with two types of numerical attributes length and direction a new type of primitive edit operation called merge is then introduced which can be used to combine and then match any number of consecutive boundary primitives in one shape with those in another the resulting attributed string matching with merging approach is shown useful for recognizing distorted shapes experimental results prove the feasibility of the proposed approach for general shape recognition some possible extensions of the approach are also included

bayes estimation and expected termination time for the competing risks model from gompertz distribution under progressively hybrid censoring with binomial removals this paper considers the bayes estimation for competing risks model under type i progressively hybrid censoring with binomial removals from two parameter gompertz distribution bayes procedure is used to derive the estimations of the unknown parameters reliability and hazard rate functions based on symmetric and asymmetric loss functions the effect of varying removal probability p on the expected termination time point under type i phc with binomial removals over the expected termination time point for the complete sample reet is investigated numerical experiments using monte carlo simulation are given to compare the performance of the proposed estimates finally one data set was used for illustrative purposes in conclusion

local estimation of posterior class probabilities to minimize classification errors decision theory shows that the optimal decision is a function of the posterior class probabilities more specifically in binary classification the optimal decision is based on the comparison of the posterior probabilities with some threshold therefore the most accurate estimates of the posterior probabilities are required near these decision thresholds this paper discusses the design of objective functions that provide more accurate estimates of the probability values taking into account the characteristics of each decision problem we propose learning algorithms based on the stochastic gradient minimization of these loss functions we show that the performance of the classifier is improved when these algorithms behave like sample selectors samples near the decision boundary are the most relevant during learning

preconditioned methods for solving the incompressible low speed compressible equations abstract acceleration methods are presented for solving the steady state incompressible equations these systems are preconditioned by introducing artificial time derivatives which allow for a faster convergence to the steady state we also consider the compressible equations in conservation form with slow flow two arbitrary functions and are introduced in the general preconditioning an analysis of this system is presented and an optimal value for is determined given a constant it is further sown that the resultant incompressible equations form a symmetric hyperbolic system and so are well posed several generalizations to the compressible equations are presented which extend previous results

recognition of occluded objects a cluster structure algorithm clustering techniques have been used to perform image segmentation to detect lines and curves in images and to solve several other problems in pattern recognition and image analysis in this paper we apply clustering methods to a new problem domain and present a new method based on a cluster structure approach for the recognition of 2 d partially occluded objects basically the technique consists of three steps clustering of border segment transformations finding continuous sequences of segments in appropriately chosen clusters and clustering of sequence average transformation values as compared to some of the earlier methods which identify an object based on only one sequence of matched segments the new approach allows the identification of all parts of the model which match in the occluded scene we also discuss the application of the clustering techniques to 3 d scene analysis in both cases the cluster structure algorithm entails the application of clustering concepts in a hierarchical manner resulting in a decrease in the computational effort as the recognition algorithm progresses the implementation of the techniques discussed for the 2 d case has been completed and the algorithm has been evaluated with respect to a large number of examples where several objects partially occlude one another the method is able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and or the polygonal approximation of the boundary of the object a summary of the results is presented

how to prevent pressure oscillations in multicomponent flow calculations a new numerical scheme for computing multicomponent flows is presented it is able to handle strong shocks is pressure oscillation free through the contact discontinuities and guarantees the posi tivity of mass fractions some numerical experiments on shock tubes indicate the convergence to the correct weak solution comparisons with classical finite volume schemes are also proposed

inference for confidence sets of the generalized inverted exponential distribution under k record values abstract based on the k record values confidence sets are explored for the parameters of the generalized inverted exponential distribution series of exact balanced confidence intervals and exact confidence regions are constructed using pivotal quantities in order to obtain minimum size confidence sets constrained optimization problems are also discussed and the associated nonlinear programming procedures are established by minimizing lagrangian functions shortest length confidence intervals and smallest area confidence regions for the unknown parameters can be obtained by simultaneously solving nonlinear system finally two real data examples and a simulation study are provided for illustrative purposes

generalized flux vector splitting and roe average for an equilibrium real gas abstract the flux vector splittings of steger warming and van leer and roe s approximate riemann solver are generalized to arbitrary equilibrium gas laws comparisons with other formulations are made

roc curves for regression receiver operating characteristic roc analysis is one of the most popular tools for the visual assessment and understanding of classifier performance in this paper we present a new representation of regression models in the so called regression roc rroc space the basic idea is to represent over estimation against under estimation the curves are just drawn by adjusting a shift a constant that is added or subtracted to the predictions and plays a similar role as a threshold in classification from here we develop the notions of optimal operating condition convexity dominance and explore several evaluation metrics that can be shown graphically such as the area over the rroc curve aoc in particular we show a novel and significant result the aoc is equivalent to the error variance we illustrate the application of rroc curves to resource estimation namely the estimation of software project effort

a graphical analysis of cost sensitive regression problems several efforts have been done to bring roc analysis beyond binary classification especially in regression however the mapping and possibilities of these proposals do not correspond to what we expect from the analysis of operating conditions dominance hybrid methods etc in this paper we present a new representation of regression models in the so called regression roc rroc space the basic idea is to represent over estimation on the x axis and under estimation on the y axis the curves are just drawn by adjusting a shift a constant that is added or subtracted to the predictions and plays a similar role as a threshold in classification from here we develop the notions of optimal operating condition convexity dominance and explore several evaluation metrics that can be shown graphically such as the area over the rroc curve aoc in particular we show a novel and significant result the aoc is equal to the error variance multiplied by a factor which does not depend on the model the derivation of rroc curves with non constant shifts and soft regression models and the relation with cost plots is also discussed

an adaptive empirical bayes estimator of the multivariate normal mean under quadratic loss abstract for shrinkage estimators to achieve significant risk improvement over their traditional competitors one must identify the region or subspace where the unknown location vector lies or is thought likely to lie a priori when vague or conflicting priors suggest that a broad class of estimators may be effective under a squared error loss measure new minimax or near minimax empirical bayes estimators are proposed that make use of stein s unbiased estimator of the risk to identify the optimum risk effective shrinkage estimator

bayes estimators for the extreme value reliability function bayes estimates under both modified symmetric and asymmetric loss functions are obtained for the reliability function of the extreme value distribution ev1 using lindley s approximation procedure these estimates are compared to each others and to maximum likelihood estimates mle using simulation study a noninformative prior jeffreys invariant prior is used in the comparisons the bayes estimator under asymmetric loss function compared to the posterior mean it incorporates additional information about possible consequences of overestimation and underestimation of the true value of the reliability function the mle is superior to either of the bayes estimates except for small values of time t the bayes estimates consistently perform well while the bayes approach is computationally intensive the calculations can be easily computerized

computation of flows with arbitrary equations of state the extension of time marching computations to fluids with arbitrary equations of state is demonstrated by means of stability analyses simplified problems and practical applications most of the examples use the properties of supercritical hydrogen for which the density varies by more than an order of magnitude for small changes in pressure and temperature but representative computations for incompressible fluids and perfect gases are also given to demonstrate the generality of the procedure because representative flow velocities in typical supercritical fluids applications are much lower than the speed of sound convergence enhancement through eigenvalue control is often necessary this is accomplished through a generalization of earlier preconditioning methods that enables efficient computation of arbitrary equation of state fluids perfect gases and incompressible fluids by a single procedure the present approach thus provides a single method that is uniformly applicable to all equations of state

a comparison of approaches to high level image interpretation abstract a critical comparison of methods of high level interpretation of segmented images is presented and illustrated by examples drawn from two dimensional image analysis the methods discussed include boundary correlation generalized hough transformation relational distance measures graph matching heuristic search and relaxation labelling each technique is considered in terms of its reliability in interpreting noisy and cluttered scenes and in terms of the dynamic complexity of the algorithm as a function of the input data although 2 d analysis of linearly segmented scenes is used as a mechanism for comparison many of the arguments are applicable to three dimensional image interpretation based on surface modelling for example

the finite sample properties of simultaneous equations estimates and estimators bayesian and non bayesian approaches abstract after discussing the need for good finite sample estimation procedures for simultaneous equations models and showing the inadequacies of asymptotically justified estimators it is shown how the bayesian method of moments bmom provides an exact finite sample analysis of unrestricted reduced form systems then optimal finite sample estimates of structural coefficients are derived using three standard loss functions and they are compared to traditional bayesian optimal estimates monte carlo experimental evidence from four studies on the relative performance of bayesian and non bayesian estimators is reviewed with the finding that the performance of bayesian estimators is better

bayesian estimation and prediction using asymmetric loss functions abstract estimators and predictors that are optimal relative to varian s asymmetric linex loss function are derived for a number of well known models their risk functions and bayes risks are derived and compared with those of usual estimators and predictors it is shown that some usual estimators for example a scalar sample mean or a scalar least squares regression coefficient estimator are inadmissible relative to asymmetric linex loss by providing alternative estimators that dominate them uniformly in terms of risk

accurate computation of contact discontinuities in flows with general equations of state abstract the evolution of a contact discontinuity in a real fluid flow is particularly difficult to compute with a conservative scheme indeed godunov type schemes involve an averaging step which may create an intermediate state with an incorrect pressure spurious acoustic waves are then generated these waves affect transient simulations as well as implicit time marching procedures for steady state computations however a formulation involving the pressure as a dependent variable can be used to restore a fast convergence for steady state computations

hybrid multifluid algorithms extensions of many successful single component schemes to compute multicomponent gas dynamics suffer from oscillations and other computational inaccuracies near material interfaces that are caused by the failure of the schemes to maintain pressure equilibrium between the fluid components a new algorithm based on the compressible euler equations for multicomponent fluids augmented by the pressure evolution equation is presented the extended set of equations offers two alternative ways to update the pressure field i using the equation of state or ii using the pressure evolution equation in a numerical implementation these two procedures generally yield different answers the former is a standard conservative update but may produce oscillations near material interfaces the latter is nonconservative but becomes exact near interfaces and automatically maintains pressure equilibrium a hybrid scheme which selects from the two pressure update procedures is presented the scheme perfectly conserves total mass and momentum and conserves total energy everywhere except at a finite very small number of grid cells computed solutions exhibit oscillation free interfaces and have em negligible relative conservation errors in total energy even for very strong shocks the proposed hybrid approach and switching strategies are independent of the numerical implementation and may provide a simple framework within which to extend one s favourite scheme to solve multifluid dynamics

cost driven parameter design parameter design methodology has focused primarily on the quadratic loss function which can often be solved by using a two step procedure involving the minimization of a dispersion measure and then adjusting the mean to target in some practical situations however the loss can be nonquadratic or highly skewed by building on a theory of leon and wu we develop a modeling and data analysis strategy for a general loss function in which the quality characteristic follows a location scale model the only difference from the two step procedure just mentioned is the adjustment step in which the mean is moved to that side of the target with lower cost the technique is illustrated on an experiment involving epitaxial layer growth in integrated circuit fabrication

building cost functions minimizing to some summary statistics a learning machine or a model is usually trained by minimizing a given criterion the expectation of the cost function measuring the discrepancy between the model output and the desired output as is already well known the choice of the cost function has a profound impact on the probabilistic interpretation of the output of the model after training in this work we use the calculus of variations in order to tackle this problem in particular we derive necessary and sufficient conditions on the cost function ensuring that the output of the trained model approximates 1 the conditional expectation of the desired output given the explanatory variables 2 the conditional median and more generally the q quantile 3 the conditional geometric mean and 4 the conditional variance the same method could be applied to the estimation of other summary statistics as well we also argue that the least absolute deviations criterion could in some cases act as an alternative to the ordinary least squares criterion for nonlinear regression in the same vein the concept of regression quantile is briefly discussed

efficiency of a liu type estimator in semiparametric regression models in this paper we consider the semiparametric regression model y x b f e recently hu 11 proposed ridge regression estimator in a semiparametric regression model we introduce a liu type combined ridge stein estimator lte in a semiparametric regression model firstly liu type estimators of both b and f are attained without a restrained design matrix secondly the lte estimator of b is compared with the two step estimator in terms of the mean square error we describe the almost unbiased liu type estimator in semiparametric regression models the almost unbiased liu type estimator is compared with the liu type estimator in terms of the mean squared error matrix a numerical example is provided to show the performance of the estimators

sampling performance of some joint one sided preliminary test estimators under squared error loss in many areas of science when drawing conclusions concerning a set of phenomena individual or linear combinations of parameters are assumed to be nonnegative nonpositive or to lie between upper and lower bounds likewise many functions are assumed to be monotonic convex or quasiconvex consequently questions naturally arise as to the correctness of the assumptions and within the context of statistical inference how to evaluate the evidence against these assumptions and to use the data at hand to seek the appropriate statistical model and the corresponding method of estimation within the context of the general linear statistical model multivariate analogues of one sided inequality hypotheses tests have been explored by bartholomew 1959 kudo 1963 osterhoff 1969 barlow et al 1972 yancey judge and bock 1981 yancey bohrer and judge 1982 gourieroux holly and montfort ghm 1982 judge and yancey 1986 wolak 1987 and shapiro 1988 in general the likelihood ratio framework has been used in developing a test statistic and determining acceptance and rejection regions in order to facilitate the interpretation of these test results in econometric practice in this paper we evaluate under a squared error loss sel measure the risk characteristics of the preliminary test pt estimators that evolve when based on the data at hand an estimation decision is taken as a result of the outcomes of two one sided multivariate hypothesis tests

bayesian inference using record values from rayleigh model with application in this article based on a set of upper record values from a rayleigh distribution bayesian and non bayesian approaches have been used to obtain the estimators of the parameter and some lifetime parameters such as the reliability and hazard functions bayes estimators have been developed under symmetric squared error and asymmetric linex and general entropy ge loss functions these estimators are derived using the informative and non informative prior distributions for we compare the performance of the presented bayes estimators with known non bayesian estimators such as the maximum likelihood ml and the best linear unbiased blu estimators we show that bayes estimators under the asymmetric loss functions are superior to both the ml and blu estimators the highest posterior density hpd intervals for the rayleigh parameter and its reliability and hazard functions are presented also bayesian prediction intervals of the future record values are obtained and discussed finally practical examples using real record values are given to illustrate the application of the results

best median unbiased estimation in linear regression with bounded asymmetric loss functions abstract this article considers optimal median unbiased estimation in a linear regression model with the distribution of the errors lying in a subclass of the elliptically symmetric distributions the generalized least squares gls estimator is shown to be best for any monotone loss function that is any loss function that is nondecreasing as the magnitude of underestimation or overestimation increases this includes bounded asymmetric loss functions for the same loss functions a restricted gls estimator is shown to be best when the estimand is known to lie in an interval for the case of normal errors a best median unbiased estimator of the error variance 2 is given for restricted and unrestricted parameter spaces this estimator differs from the sample variance s 2 in comparison with best mean unbiased estimators of regression and variance parameters the best median unbiased estimators considered here take advantage of restrictions on the parameter space and are optimal with respect to a much wi

a consistent test for nonlinear out of sample predictive accuracy abstract in this paper we draw on both the consistent specification testing and the predictive ability testing literatures and propose an integrated conditional moment type predictive accuracy test that is similar in spirit to that developed by bierens j econometr 20 1982 105 econometrica 58 1990 1443 and bierens and ploberger econometrica 65 1997 1129 the test is consistent against generic nonlinear alternatives and is designed for comparing nested models one important feature of our approach is that the same loss function is used for in sample estimation and out of sample prediction in this way we rule out the possibility that the null model can outperform the nesting generic alternative model it turns out that the limiting distribution of the icm type test statistic that we propose is a functional of a gaussian process with a covariance kernel that reflects both the time series structure of the data as well as the contribution of parameter estimation error as a consequence critical values that are data dependent and cannot be directly tabulated one approach in this case is to obtain critical value upper bounds using the approach of bierens and ploberger econometrica 65 1997 1129 here we establish the validity of a conditional p value method for constructing critical values the method is similar in spirit to that proposed by hansen econometrica 64 1996 413 and inoue econometric theory 17 2001 156 although we additionally account for parameter estimation error in a series of monte carlo experiments the finite sample properties of three variants of the predictive accuracy test are examined our findings suggest that all three variants of the test have good finite sample properties when quadratic loss is specified even for samples as small as 600 observations however non quadratic loss functions such as linex loss require larger sample sizes of 1000 observations or more in order to ensure reasonable finite sample performance

inadmissibility of the stein rule estimator under the balanced loss function in this paper the risk of the stein rule sr estimator is compared with that of the improved stein rule estimator using the stein variance srsv estimator under the balanced loss function it is shown that the srsv estimator dominates the sr estimator even if the loss function is extended to the blf also our numerical results show that the srsv estimator dominates the ols estimator when the weight of precision of estimation is larger than about half and vice versa

testing forecast optimality under unknown loss empirical tests of forecast optimality have traditionally been conducted under the assumption of mean squared error loss or some other known loss function in this article we establish new testable properties that hold when the forecaster s loss function is unknown but testable restrictions can be imposed on the data generating process trading off conditions on the data generating process against conditions on the loss function we propose flexible estimation of the forecaster s loss function in situations where the loss depends not only on the forecast error but also on other state variables such as the level of the target variable we apply our results to the problem of evaluating the federal reserve s forecasts of output growth forecast optimality is rejected if the fed s loss depends only on the forecast error however the empirical findings are consistent with forecast optimality provided that overpredictions of output growth are costlier to the fed than underpredictions particularly during per