a computationally efficient quadrature procedure for the one factor multinomial probit model a problem of estimation that has long confronted many economists is the difficulty of estimating the parameters of equations with limited dependent variables on cross section time series i e panel data while there are widely available packaged computer programs for estimating either a cross section probit and tobit models or b simple permanent transitory random effects panel models with continuous dependent variables there are no available computationally feasible methods of combining these two models this is because the likelihood function that arises in such a combined model contains multivariate normal integrals whose evaluation is quite difficult if not impossible with conventional approximation methods there is a widespread feeling among those working in the area that one possible method of evaluation the use of quadrature techniques is in principle possible but is in practice computationally too burdensome to consider e g albright et al 2 p 13 hausman and wise 6 p 12 in this note we point out that this is true only of standard quadrature techniques such as trapezoidal integration or its improved variants gaussian quadrature on the other hand is extremely efficient and is well within the bounds of computational feasibility on modern computers in what follows we state the nature of the integrals that need to be evaluated provide a brief exposition of gaussian quadrature and provide a numerical illustration of its use in

simulation based inference a survey with special reference to panel data models abstract in this article we study the recent developments of inference methods based on simulations in particular we discuss the simulated generalized method of moments the simulated maximum likelihood method and the simulated pseudo maximum likelihood methods the asymptotic properties of the estimators are described when the number of observations n goes to infinity and we distinguish the case where the number h of simulations per observation is fixed from the case where this number also goes to infinity in the former case the possible asymptotic bias is evaluated and in the latter case we carefully examine the consequences of the assumptions on the relative divergence rates of n and h we also show how these methods apply to various contexts in particular to the case of panel data models

opportunities and challenges of combined effect measures based on prioritized outcomes many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature in case of binary or time to event variables composite endpoints which combine several event types within a single event or time to first event analysis are often used to assess the overall treatment effect a main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another recently some authors proposed more general approaches based on a priority ranking of outcomes which moreover allow to combine outcome variables of different scale levels these new combined effect measures assign a higher impact to more important endpoints which is meant to simplify the interpretation of results whereas statistical tests and models for binary and time to event variables are well understood the latter methods have not been investigated in detail so far in this paper we will investigate the statistical properties of prioritized combined outcome measures we will perform a systematical comparison to standard composite measures such as the all cause hazard ratio in case of time to event variables or the absolute rate difference in case of binary variables to derive recommendations for different clinical trial scenarios we will discuss extensions and modifications of the new effect measures which simplify the clinical interpretation moreover we propose a new method on how to combine the classical composite approach with a priority ranking of outcomes using a multiple testing strategy based on the closed test procedure

the method of simulated scores for the estimation of ldv models the method of simulated scores mss is presented for estimating limited dependent variables models ldv with flexible correlation structure in the unobservables we propose simulators that are continuous in the unknown parameter vectors and hence standard optimization methods can be used to compute the mss estimators that employ these simulators the first continuous method relies on a recursive conditioning of the multivariate normal density through a cholesky triangularization of its variance covariance matrix the second method combines results about the conditionals of the multivariate normal distribution with gibbs resampling techniques we establish consistency and asymptotic normality of the mss estimators and derive suitable rates at which the number of simulations must rise if biased simulators are used

consistent inference in fixed effects stochastic frontier models abstract the classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency greene 2005a b proposed the so called true fixed effects specification that distinguishes these two latent components however due to the incidental parameters problem his maximum likelihood estimator may lead to biased variance estimates we propose two alternative estimators that achieve consistency for n with fixed t furthermore we extend the chen et al 2014 results providing a feasible estimator when the inefficiency is heteroskedastic and follows a first order autoregressive process we investigate the behavior of the proposed estimators through monte carlo simulations showing good finite sample properties especially in small samples an application to hospitals technical efficiency illustrates the usefulness of the new approach

consistent estimation of the fixed effects stochastic frontier model abstract in this paper we consider a fixed effects stochastic frontier model that is we have panel data fixed individual firm effects and the usual stochastic frontier analysis sfa composed error maximum likelihood estimation mle of this model has been considered by greene 2005a greene 2005b it is subject to the incidental parameters problem that is to possible inconsistency due to the number of parameters growing with the number of firms in the linear regression model with normal errors it is known that the mle of the regression coefficients is consistent and the inconsistency due to the incidental parameters problem applies only to the error variance greene s simulations suggest that the same is true in the fixed effects sfa model in this paper we take a somewhat different approach we consider mle based only on the joint density of the deviations from means in the linear regression model with normal errors this estimator is the same as the full mle for the regression coefficients but it yields a consistent estimator of the error variance for the sfa model the mle based on the deviations from means is not the same as the full mle and it has the advantage of not being subject to the incidental parameters problem the derivation of the joint density of the deviations from means is made possible by results in the statistical literature on the closed skew normal family of distributions these results may be of independent interest to researchers in this area simulations indicate that our within mle estimator performs quite well in finite samples we also present an empirical example

mixtures of multiple testing procedures for gatekeeping applications in clinical trials this paper proposes a general framework for constructing gatekeeping procedures for clinical trials with hierarchical objectives such problems frequently exhibit complex structures including multiple families of hypotheses and logical restrictions the proposed framework is based on combining multiple procedures across families it enables the construction of powerful and flexible gatekeeping procedures that account for general logical restrictions among the hypotheses of interest a clinical trial in patients with schizophrenia is used to illustrate the approach for parallel gatekeeping whereas another clinical trial in patients with hypertension is used to illustrate the approach for gatekeeping with general logical restrictions

quantization of the poisson su 2 and its poisson homogeneous space the 2 sphere we show that deformation quantizations of the poisson structures on the poisson lie groupsu 2 and its homogeneous space the 2 sphere are compatible with woronowicz s deformation quantization ofsu 2 s group structure and podles deformation quantization of 2 sphere s homogeneous structure respectively so in a certain sense the multiplicativity of the lie poisson structure onsu 2 at the classical level is preserved under quantization

tree structured gatekeeping tests in clinical trials with hierarchically ordered multiple objectives this paper discusses a new class of multiple testing procedures tree structured gatekeeping procedures with clinical trial applications these procedures arise in clinical trials with hierarchically ordered multiple objectives for example in the context of multiple dose control tests with logical restrictions or analysis of multiple endpoints the proposed approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches developed by westfall and krishen j statist planning infer 2001 99 25 41 and dmitrienko et al statist med 2003 22 2387 2400 the proposed testing methodology is illustrated using a clinical trial with multiple endpoints primary secondary and tertiary and multiple objectives superiority and non inferiority testing as well as a dose finding trial with multiple endpoints

powerful short cuts for multiple testing procedures with special reference to gatekeeping strategies in this paper we present a general testing principle for a class of multiple testing problems based on weighted hypotheses under moderate conditions this principle leads to powerful consonant multiple testing procedures furthermore short cut versions can be derived which simplify substantially the implementation and interpretation of the related test procedures it is shown that many well known multiple test procedures turn out to be special cases of this general principle important examples include gatekeeping procedures which are often applied in clinical trials when primary and secondary objectives are investigated and multiple test procedures based on hypotheses which are completely ordered by importance we illustrate the methodology with two real clinical studies

simulated maximum likelihood estimation of dynamic discrete choice statistical models some monte carlo results this article reports monte carlo results on the simulated maximum likelihood estimation of discrete dynamic panel models introduced by james heckman 1981a the simulated maximum likelihood method is numerically stable even for long panels regression models and polya and renewal models can be better estimated than markov models with a moderate number of simulation draws most of these complex models can be adequately estimated for panels with length up to 30 polya and renewal models can be accurately estimated for panels up to 50 periods estimates of markov models can be sensitive to misspecified initial states but polya renewal and habit persistence models may not

estimating count data models with endogenous switching sample selection and endogenous treatment effects count data regression models are extended to account for endogenous switching and its two most common incarnations viz endogenous dummy variables treatment effects and sample selection fully parametric and partially parametric versions of the extended model are discussed in the parametric version of the model a full information maximum likelihood fiml approach to estimation is introduced under the correct model specification the fiml estimator is efficient but computationally burdensome for the relatively robust partially parametric version of the model i develop a two stage method of moments tsm estimator the tsm estimator is a nonlinear least squares analog to the popular heckman 1976 1979 estimator and therefore avoids the computational requirements of fiml estimation a nonlinear weighted least squares nwls estimator is offered for the fully parametric case the nwls estimator is computationally efficient relative to fiml estimation and statistically efficient relative to the tsm estimator for illustrative purposes the tsm and nwls estimators are applied to the estimation of a regression model with household trip frequency as the dependent variable and a potentially endogenous dummy variable indicating vehicle ownership among the regressors

a note on testing families of hypotheses using graphical procedures regulatory guidelines for drug development suggest a strong control of the familywise error rate when multiple hypotheses are simultaneously tested in confirmatory clinical trials accordingly a variety of multiple test procedures exist for pharmaceutical trial applications which are typically defined on a single structured family of null hypotheses for some confirmatory clinical trials with multiple objectives however it is advantageous to address the arising multiplicity problems via grouped families of hypotheses graphical test procedures have been developed to construct visualize and perform multiple test procedures that are tailored to either a single or multiple families of structured hypotheses of interest this note complements the existing literature by introducing a general algorithm to calculate adjusted p values for any sequentially rejective graphical test procedure where the test procedures within the individual families of hypotheses are not necessarily graphical

an adaptive confirmatory trial with interim treatment selection practical experiences and unbalanced randomization this article has been motivated by an ongoing international adaptive confirmatory trial at the interim analysis of this two stage trial none one or two active treatment regimens are selected for further study in the second stage a combination test approach is used in this practical setting with an extension of the theory to unbalanced randomization we show that a combination test with suitable weights can still preserve the overall type i error rate provided that the test statistic is chosen appropriately and the unpooled z test for proportions is not used the accuracy of stagewise p values is also discussed in a more general framework monte carlo simulations confirm the validity of the approach retained and evaluate the necessary sample size additional issues addressed during the design of the trial are also examined such as multiplicity due to testing hypotheses on key secondary endpoints a non inferiority comparison to an active treatment and covariate adjusted analyses for various types of outcome

ergodic actions of compact groups on operator algebras iii classification for su 2 we prove that the only ergodic actions ofsu 2 are on type i von neumann algebras using the theory developed in 8

a recycling framework for the construction of bonferroni based multiple tests in this paper we describe bonferroni based multiple testing procedures mtps as strategies to split and recycle test mass here test mass refers to parts of the nominal level alpha at which the family wise error rate is controlled briefly test mass is split between different null hypotheses and whenever a null hypothesis is rejected the part of alpha allocated to it may be recycled to the testing of other hypotheses these recycling mtps are closed testing procedures based on raw p values associated with testing the individual null hypotheses and the class of such mtps includes for example serial and parallel gatekeeping fallback and holm procedures graphical displays and a concise algebraic notation are provided for such mtps this recycling approach has pedagogical advantages and may facilitate the tailoring of mtps for different purposes

key multiplicity issues in clinical drug development much progress has been made over the past decade with the development of novel methods for addressing increasingly more complex multiplicity problems arising in confirmatory phase iii clinical trials this includes traditional problems with a single source of multiplicity for example analysis of multiple endpoints or dose placebo contrasts in addition more advanced problems with several sources of multiplicity have attracted attention in clinical drug development these problems include two or more families of objectives such as multiple endpoints evaluated at multiple dose levels or in multiple patient populations this paper provides a review of concepts that play a central role in defining and solving multiplicity problems error rate definitions and introduces main classes of multiple testing procedures widely used in clinical trials nonparametric semiparametric and parametric procedures the paper also presents recent advances in multiplicity research including gatekeeping procedures for clinical trials with multiple sets of objectives the concepts and methods introduced in the paper are illustrated using several case studies on the basis of real clinical trials software implementation of commonly used multiple testing and gatekeeping procedures is discussed copyright 2012 john wiley sons ltd

stochastic frontier models with time varying conditional variances abstract in this paper we introduce stochastic frontier models in which either the inefficiency or the noise component or both the components follow generalized autoregressive conditional heteroskedasticity garch 1 1 process bayesian estimation of the technology parameters are proposed using a half normal exponential distribution for the inefficiency component and a normal distribution for the noise component we show in simulations that predictions of inefficiency ignoring the garch 1 1 process are not aligned with their true values we use real panel data on electricity distribution and show how to estimate our proposed model and predict inefficiency moreover we examine the effect of ignoring garch specification on economic measures like input elasticities technical change and returns to scale we also provide test for garch vs no garch models using bayes factors finally we examine variants of the garch family such as the arch and egarch models and we compared garch models of different orders

estimation and testing in the random effects probit model abstract this paper examines the finite sample properties of the random effects probit estimator in comparison to the standard probit estimator and the standard probit estimator with a corrected asymptotic covariance matrix the monte carlo experiment considers data generating processes consistent with longitudinal data and also data from sample surveys the probit estimator with corrected asymptotic covariance matrix works surprisingly well over a wide range of parametric configurations and is recommended as long as an estimate of the error correlation is not of high importance

a time varying true individual effects model with endogenous regressors abstract we propose a fairly general individual effects stochastic frontier model which allows both heterogeneity and inefficiency to change over time moreover our model handles the endogeneity problems if either at least one of the regressors or one sided error term is correlated with the two sided error term our monte carlo experiments show that our estimator performs well we employed our methodology to the us banking data and found a negative relationship between return on revenue and cost efficiency estimators ignoring time varying heterogeneity or endogeneity did not perform well and gave very different estimates compared to our estimator

an obstruction to quantization of the sphere in the standard example of strict deformation quantization of the symplectic sphere s2 the set of allowed values of the quantization parameter is not connected indeed it is almost discrete li recently constructed a class of examples including s2 in which can take any value in an interval but these examples are badly behaved here i identify a natural additional axiom for strict deformation quantization and prove that it implies that the parameter set for quantizing s2 is never connected

lie group convolution algebras as deformation quantizations of linear poisson structures introduction let l be a finite dimensional lie algebra over the real numbers r and let l be its dual vector space it is well known 24 that the lie algebra structure on l defines a natural poisson structure on l in fact this was already known to lie 24 and these poisson structures are exactly what are now called the linear poisson structures given a manifold m equipped with a poisson structure one can seek deformation quantizations in the direction of as first studied in 3 these are loosely speaking one parameter families h ler of deformations of the pointwise multiplication on c m or an appropriate subalgebra such that

on the estimation of technical inefficiency in the stochastic frontier production function model abstract the error term in the stochastic frontier model is of the form v u where v is a normal error term representing pure randomness and u is a non negative error term representing technical inefficiency the entire v u is easily estimated for each observation but a previously unsolved problem is how to separate it into its two components v and u this paper suggests a solution to this problem by considering the expected value of u conditional on v u an explicit formula is given for the half normal and exponential cases

technical and allocative efficiency in a panel stochastic production frontier system model abstract in this paper we consider a state of the art panel stochastic production frontier model and estimate it using a system that includes both technical and allocative inefficiency the system consists of the optimal input choice rule the first order conditions focs under cost minimization together with the production function the focs are used to take care of endogeneity of inputs allocative inefficiency is modeled as non fulfillment of the focs we use distributional assumptions on the noise and inefficiency components and estimate the model parameters using the maximum likelihood method in estimating technical and allocative inefficiency components and costs therefrom we control for fixed firm effects in each equation of the system we also correct for the incidental parameter bias by using the half panel jackknife estimator

toeplitz quantization of kahler manifolds ang gl n n limits for general compact kahler manifolds it is shown that both toeplitz quantization and geometric quantization lead to a well defined by operator norm estimates classical limit this generalizes earlier results of teh authors and klimek and lesniewski obtained for the tours and higher genus riemann surfaces respectively we thereby arrive at an approximation of the poisson algebra by a sequence of finitedimensional matrix algebrasgl n n

quantum riemann surfaces for arbitrary planck s constant we continue our study of quantum riemann surfaces initiated in refs s klimek and a lesniewski commun math phys 146 103 122 1992 lett math phys 24 125 139 1992 32 45 61 1994 we construct a one parameter family of deformations of compact riemann surfaces of genus g 2 our construction does not require any discreteness condition on the value of planck s constant it coincides with the construction of ref lett math phys 24 125 139 1992 in the case when planck s constant assumes the discrete set of values dictated by geometric quantization

quantization of multiply connected manifolds the standard berezin toeplitz geometric quantization of a compact kahler manifold is restricted by integrality conditions these restrictions can be circumvented by passing to the universal covering space provided that the lift of the symplectic form is exact i relate this construction to the baum connes assembly map and prove that it gives a strict quantization of the original manifold i also propose a further generalization classify the required structure and provide a means of computing the resulting algebras these constructions involve twisted group c algebras of the fundamental group which are determined by a group cocycle constructed from the cohomology class of the symplectic form this provides an algebraic counterpart to the morita equivalence of a symplectic manifold with its fundamental group

categorical perspective on quantization of poisson algebra we propose a generalization of quantization using a categorical approach for a fixed poisson algebra quantization categories are defined as subcategories of the r module category equipped with the structure of classical limits we then construct the generalized quantization categories including matrix regularization strict deformation quantization prequantization and poisson enveloping algebra it is shown that the categories of strict deformation quantization prequantization and matrix regularization with certain conditions are equivalent categories on the other hand the categories of poisson enveloping algebra are not equivalent to the other categories

compatible simultaneous lower confidence bounds for the holm procedure and other bonferroni based closed tests we consider the problem of simultaneously testing multiple one sided null hypotheses single step procedures such as the bonferroni test are characterized by the fact that the rejection or non rejection of a null hypothesis does not take the decision for any other hypothesis into account for stepwise test procedures such as the holm procedure the rejection or non rejection of a null hypothesis may depend on the decision of other hypotheses it is well known that stepwise test procedures are by construction more powerful than their single step counterparts this power advantage however comes only at the cost of increased difficulties in constructing compatible simultaneous confidence intervals for the parameters of interest for example such simultaneous confidence intervals are easily obtained for the bonferroni method but surprisingly hard to derive for the holm procedure in this paper we discuss the inherent problems and show that ad hoc solutions used in practice typically do not control the pre specified simultaneous confidence level instead we derive simultaneous confidence intervals that are compatible with a certain class of closed test procedures using weighted bonferroni tests for each intersection hypothesis the class of multiple test procedures covered in this paper includes gatekeeping procedures based on bonferroni adjustments fixed sequence procedures the simple weighted or unweighted bonferroni procedure by holm and the fallback procedure we illustrate the results with a numerical example

a spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers abstract by blending seminal literature on non spatial stochastic frontier models with key contributions to spatial econometrics we develop a spatial autoregressive sar stochastic frontier for panel data the specification of the sar frontier allows efficiency to vary over time and across the cross sections efficiency is calculated from a composed error structure by assuming a half normal distribution for inefficiency the sar frontier is estimated using maximum likelihood methods taking into account the endogenous sar variable the application of the estimator to an aggregate production frontier for european countries highlights among other things the asymmetry between efficiency spillovers to and from a country

identifying effective and or safe doses by stepwise confidence intervals for ratios typical randomized clinical dose finding studies consist of the comparison of several doses of a drug versus a placebo interest lies in estimating relevant doses among those under investigation for efficacy and safety variables such as the minimum effective dose or the maximum safe dose or estimating both doses simultaneously step down procedures have been proposed for comparing the standardized differences of the dose groups against placebo in this paper we consider the ratio of population means and propose stepwise confidence intervals for these ratios these confidence intervals do not require multiplicity adjustments and yield the same decisions as the associated test procedures in addition several power concepts are investigated within the present framework the results allow sample size determination in the design phase of a study for the probability of estimating correctly the dose of interest auxiliary results of a numerical study show the range of application of these methods

deformation quantization of heisenberg manifolds form a smooth manifold equipped with a poisson bracket we formulate ac algebra framework for deformation quantization including the possibility of invariance under a lie group of diffeomorphisms preserving the poisson bracket we then show that the much studied non commutative tori give examples of such deformation quantizations invariant under the usual action of ordinary tori going beyond this the main results of the paper provide a construction of invariant deformation quantizations for those poisson brackets on heisenberg manifolds which are invariant under the action of the heisenberg lie group and for various generalizations suggested by this class of examples interesting examples are obtained of simplec algebras on which the heisenberg group acts ergodically

using follow up data to avoid omitted variable bias an application to cardiovascular epidemiology omitted variable bias is discussed in the context of linear models it is shown that the effect of omitted variables can be controlled in linear models for metric dependent variables by using data from follow up studies two different models for analysing such data are proposed in the first model the omitted variables are assumed to be uncorrelated with the explanatory variables in the model and to be constant over time these assumptions lead to a special structure of the covariance matrix of the errors over time efficient estimation of the parameters in the linear model has to take this special covariance matrix of the errors into account by using appropriate generalized least squares or maximum likelihood methods in the second model the omitted variables are assumed to be time constant additionally they are allowed to be correlated with the explanatory variables that is these variables are omitted confounders in the usual epidemiological sense it is shown that even in this case the parameters of the linear model can be estimated consistently with ordinary least squares if a follow up study is available the differences between the parameter estimates under the first and the second model may be used to construct a hausman test for misspecification the models the estimation methods and the hausman test are illustrated by the example that explores the determinants of serum cholesterol in german adoloscents of both sexes

estimating fixed effect panel stochastic frontier models by model transformation abstract traditional panel stochastic frontier models do not distinguish between unobserved individual heterogeneity and inefficiency they thus force all time invariant individual heterogeneity into the estimated inefficiency greene 2005 proposes a true fixed effect stochastic frontier model which in theory may be biased by the incidental parameters problem the problem usually cannot be dealt with by model transformations owing to the nonlinearity of the stochastic frontier model in this paper we propose a class of panel stochastic frontier models which create an exception we show that first difference and within transformation can be analytically performed on this model to remove the fixed individual effects and thus the estimator is immune to the incidental parameters problem consistency of the estimator is obtained by either n or t which is an attractive property for empirical researchers

quantization and the resolvent algebra abstract we introduce a novel commutative c algebra c r x of functions on a symplectic vector space x admitting a complex structure along with a strict deformation quantization that maps a dense subalgebra of c r x to the resolvent algebra introduced by buchholz and grundling 2 the associated quantization map is a field theoretical weyl quantization compatible with the work of binz honegger and rieckers 1 we also define a berezin type quantization map on all of c r x which continuously and bijectively maps it onto the resolvent algebra the commutative resolvent algebra c r x generally defined on a real inner product space x intimately depends on the finite dimensional subspaces of x we thoroughly analyze the structure of this algebra in the finite dimensional case by giving a characterization of its elements and by computing its gelfand spectrum

testing strategies in multi dose experiments including active control inferential test strategies for multi arm trials are adapted or proposed for the special situation when more than one dose of a test treatment placebo and active control s are compared this includes between doses dose placebo and dose active control comparisons the procedures refer to situations when detailed comparisons make sense only if the sensitivity of the trial has been shown for example if a dose response relationship or a difference between active control and placebo has been established split strategies hierarchical assuming an order restriction among doses or linked procedures are introduced in linked procedures equivalence to the active control will be established only if the dose is also shown to be effective as compared to placebo all the inferential procedures control the experimentwise error rate in the strong sense for the respective sets of null hypotheses considered

a graphical approach to sequentially rejective multiple test procedures for clinical trials with multiple treatment arms or endpoints a variety of sequentially rejective weighted bonferroni type tests have been proposed such as gatekeeping procedures fixed sequence tests and fallback procedures they allow to map the difference in importance as well as the relationship between the various research questions onto an adequate multiple test procedure since these procedures rely on the closed test principle they usually require the explicit specification of a large number of intersection hypotheses tests the underlying test strategy may therefore be difficult to communicate we propose a simple iterative graphical approach to construct and perform such bonferroni type tests the resulting multiple test procedures are represented by directed weighted graphs where each node corresponds to an elementary hypothesis together with a simple algorithm to generate such graphs while sequentially testing the individual hypotheses the approach is illustrated with the visualization of several common gatekeeping strategies a case study is used to illustrate how the methods from this article can be used to tailor a multiple test procedure to given study objectives

geometric quantization of vector bundles and the correspondence with deformation quantization i repeat my definition for quantization of a vector bundle for the cases of the toeplitz and geometric quantizations of a compact kahler manifold i give a construction for quantizing any smooth vector bundle which depends functorially on a choice of connection on the bundle