calibrated propensity score method for survey nonresponse in cluster sampling weighting adjustment is commonly used in survey sampling to correct for unit nonresponse in cluster sampling the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster specific nonignorable missingness based on a parametric working model for the response mechanism that incorporates cluster specific nonignorable missingness we propose a method of weighting adjustment we provide a consistent estimator of the mean or totals in cases where the study variable follows a generalized linear mixed effects model the proposed method is robust in the sense that the consistency of the estimator does not require correct specification of the functional forms of the response and outcome models a consistent variance estimator based on taylor linearization is also proposed numerical results including a simulation and a real data application are presented

inseparable local uniformization abstract it is known since the works of zariski in the early 40s that desingularization of varieties along valuations called local uniformization of valuations can be considered as the local part of the desingularization problem it is still an open problem if local uniformization exists in positive characteristic and dimension larger than three in this paper we prove that zariski local uniformization of algebraic varieties is always possible after a purely inseparable extension of the field of rational functions and therefore any valuation can be uniformized by a purely inseparable alteration

resolution of singularities of threefolds in positive characteristic ii abstract together with vincent cossart olivier piltant resolution of singularities of threefolds in positive characteristic i reduction to local uniformization on artin schreier and purely inseparable coverings j algebra 320 3 2008 1051 1082 this article gives a complete proof of desingularization of quasiprojective varieties of dimensional 3 on fields which are differentially finite over perfect fields

likelihood based analysis of causal effects of job training programs using principal stratification government sponsored job training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public the evaluation usually focuses on employment and total earnings although the effect on wages is also of interest because this effect reflects the increase in human capital due to the training program whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage rates estimating the effects of training programs on wages is complicated by the fact that even in a randomized experiment wages are truncated or less accurately censored by nonemployment that is they are only observed and well defined for individuals who are employed in this article we develop a likelihood based approach to estimate the wage effect of the us federally funded job corps training program using principal stratification our estimands are formulated in terms of 1 the effect of the training program on wages

a note on multiple imputation for method of moments estimation multiple imputation is a popular imputation method for general purpose estimation rubin 1987 provided an easily applicable formula for the variance estimation of multiple imputation however the validity of the multiple imputation inference requires the congeniality condition of meng 1994 which is not necessarily satisfied for method of moments estimation this paper presents the asymptotic bias of rubin s variance estimator when the method of moments estimator is used as a complete sample estimator in the multiple imputation procedure a new variance estimator based on over imputation is proposed to provide asymptotically valid inference for method of moments estimation

computation of the inverse laplace transform based on a collocation method which uses only real values we develop a numerical algorithm for inverting a laplace transform lt based on laguerre polynomial series expansion of the inverse function under the assumption that the lt is known on the real axis only the method belongs to the class of collocation methods c methods and is applicable when the lt function is regular at infinity difficulties associated with these problems are due to their intrinsic ill posedness the main contribution of this paper is to provide computable estimates of truncation discretization conditioning and roundoff errors introduced by numerical computations moreover we introduce the pseudoaccuracy which will be used by the numerical algorithm in order to provide uniform scaled accuracy of the computed approximation for any x with respect to e x these estimates are then employed to dynamically truncate the series expansion in other words the number of the terms of the series acts like the regularization parameter which provides the trade off between errors with the aim to validate the reliability and usability of the algorithm experiments were carried out on several test functions

a note about models for selectivity bias on decrit une representation tabulaire simplifiee qui fait ressortir une hypothese structurale implicite importante dans des modeles de selection bases sur la censure

nonparametric estimation of mean functionals with data missing at random abstract this article considers a distribution free estimation procedure for a basic pattern of missing data that often arises from the wellknown double sampling in survey methodology without parametric modeling of the missing mechanism or the joint distribution kernel regression estimators are used to estimate mean functionals through empirical estimation of the missing pattern a generalization of the method of cheng and wei is verified under the assumption of missing at random asymptotic distributions are derived for estimating the mean of the incomplete data and for estimating the mean treatment difference in a nonrandomized observational study the nonparametric method is compared with a naive pairwise deletion method and a linear regression method via the asymptotic relative efficiencies and a simulation study the comparison shows that the proposed nonparametric estimators attain reliable performances in general

counterexamples to local monomialization in positive characteristic a local monomialization of an algebraic map between varieties along a valuation is a sequence of blowups of nonsingular subvarieties along the valuation in the domain and target of the map such that the resulting map can be expressed by monomial inclusion in a suitable sense in this paper we give examples showing that a local monomialization does not always exist over fields of positive characteristic it was shown in earlier work of the author that a local monomialization always exists over fields of characteristic zero

the model theory of separably tame valued fields a henselian valued field k is called separably tame if its separable algebraic closure ksep is a tame extension that is the ramification field of the normal extension ksep k is separable algebraically closed every separable algebraically maximal kaplansky field is a separably tame field but not conversely in this paper we prove ax kochen ershov principles for separably tame fields this leads to model completeness and completeness results relative to the value group and residue field as the maximal immediate extensions of separably tame fields are in general not unique the proofs have to use much deeper valuation theoretical results than those for other classes of valued fields which have already been shown to satisfy ax kochen ershov principles our approach also yields alternate proofs of known results for separably closed valued fields

semiparametric pseudo likelihoods in generalized linear models with nonignorable missing data we consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified we adopt a pseudo likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data explicit conditions for the identifiability of parameters are given some asymptotic properties of the parameter estimators based on maximizing the pseudo likelihood are established explicit asymptotic covariance matrix and its estimator are also derived in some cases for the numerical maximization of the pseudo likelihood we develop a two step iteration algorithm that decomposes a nonconcave maximization problem into two problems of maximizing concave functions some simulation results and an application to a dataset from cotton factory workers are also presented

local sensitivity approximations for selectivity bias summary observational data analysis is often based on tacit assumptions of ignorability or randomness the paper develops a general approach to local sensitivity analysis for selectivity bias which aims to study the sensitivity of inference to small departures from such assumptions if m is a model assuming ignorability we surround m by a small neighbourhood at defined in the sense of kullback leibler divergence and then compare the inference for models in iv with that for m interpretable bounds for such differences are developed applications to missing data and to observational comparisons are discussed local approximations to sensitivity analysis are model robust and can be applied to a wide range of statistical problems

more on the weeks method for the numerical inversion of the laplace transform most of the numerical methods for the inversion of the laplace transform require the values of several incidental parameters generally these parameters are related to the properties of the algorithm and to the analytical properties of the laplace transform functionf s

jackknife variance estimation with survey data under hot deck imputation summary hot deck imputation is commonly employed for item nonresponse in sample surveys it is also a common practice to treat the imputed values as if they are true values and then compute the variance estimates using standard formulae this procedure however could lead to serious underestimation of the true variance when the proportion of missing values for an item is appreciable we propose a jackknife variance estimator for stratified multistage surveys which is obtained by first adjusting the imputed values for each pseudo replicate and then applying the standard jackknife formula the proposed jackknife variance estimator is shown to be consistent as the sample size increases assuming equal response probabilities within imputation classes and using a particular hot deck imputation

algebraic independence of elements in immediate extensions of valued fields abstract refining a constructive combinatorial method due to maclane and schilling we give several criteria for a valued field that guarantee that all of its maximal immediate extensions have infinite transcendence degree if the value group of the field has countable cofinality then these criteria give the same information for the completions of the field the criteria have applications to the classification of valuations on rational function fields we also apply the criteria to the question of which extensions of a maximal valued field algebraic or of finite transcendence degree are again maximal in the case of valued fields of infinite p degree we obtain the worst possible examples of nonuniqueness of maximal immediate extensions fields which admit an algebraic maximal immediate extension as well as one of infinite transcendence degree

places of algebraic function fields in arbitrary characteristic we consider the zariski space of all places of an algebraic function field f k of arbitrary characteristic and investigate its structure by means of its patch topology we show that certain sets of places with nice properties e g prime divisors places of maximal rank zero dimensional discrete places lie dense in this topology further we give several equivalent characterizations of fields that are large in the sense of f pop s annals paper embedding problems over large fields we also study the question whether a field k is existentially closed in an extension field l if l admits a k rational place in the appendix we prove the fact that the zariski space with the zariski topology is quasi compact and that it is a spectral space

an analysis of bilinear transform polynomial methods of inversion of laplace transforms methods for the numerical inversion of a laplace transform f s which use a special bilinear transformation of s are particularly effective in many cases and are widely used the main purpose of this paper is to analyze the convergence and conditioning properties of a special class of such methods characterized by the use of lagrange interpolation the results derived apply both to complex and real inversion and show that some known inversion methods are in fact in this class

on the bias of the multiple imputation variance estimator in survey sampling summary multiple imputation is a method of estimating the variances of estimators that are constructed with some imputed data we give an expression for the bias of the multiple imputation variance estimator for data that are collected with a complex sample design the bias may be sizable for certain estimators such as domain means when a large fraction of the values are imputed a bias adjusted variance estimator is suggested

adjusting for nonignorable drop out using semiparametric nonresponse models abstract consider a study whose design calls for the study subjects to be followed from enrollment time t 0 to time t t at which point a primary endpoint of interest y is to be measured the design of the study also calls for measurements on a vector v t of covariates to be made at one or more times t during the interval 0 t we are interested in making inferences about the marginal mean 0 of y when some subjects drop out of the study at random times q prior to the common fixed end of follow up time t the purpose of this article is to show how to make inferences about 0 when the continuous drop out time q is modeled semiparametrically and no restrictions are placed on the joint distribution of the outcome and other measured variables in particular we consider two models for the conditional hazard of drop out given v t y where v t denotes the history of the process v t through time t t 0 t in the first model we assume that q t v t y exp 0 y where 0 is a scalar paramet

robust out of sample inference this paper presents analytical empirical and simulation results concerning inference about the moments of nondifferentiable functions of out of sample forecasts and forecast errors special attention is given to the measurement of a model s predictive ability using the test of equal mean absolute error tests for equal mean absolute error and mean square error are used to evaluate predictions of excess returns to the s p 500 composite simulations indicate that appropriately constructed tests for equal mean absolute error can provide more accurately sized and more powerful tests than inappropriately constructed tests for equal mean absolute error and mean square error

optimal pseudolikelihood estimation in the analysis of multivariate missing data with nonignorable nonresponse tang et al 2003 considered a regression model with missing response where the missingness mechanism depends on the value of the response variable and hence is nonignorable they proposed three pseudolikelihood estimators based on different treatments of the probability distribution of the completely observed covariates the first assumes the distribution of the covariate to be known the second estimates this distribution parametrically and the third estimates the distribution nonparametrically while it is not hard to show that the second estimator is more efficient than the first tang et al 2003 only conjectured that the third estimator is more efficient than the first two in this paper we investigate the asymptotic behaviour of the third estimator by deriving a closed form representation of its asymptotic variance we then prove that the third estimator is more efficient than the other two our result can be straightforwardly applied to missingness mechanisms that are more general than that in tang et al 2003

a semiparametric estimation of mean functionals with nonignorable missing data parameter estimation with nonignorable missing data is a challenging problem in statistics the fully parametric approach for joint modeling of the response model and the population model can produce results that are quite sensitive to the failure of the assumed model we propose a more robust modeling approach by considering the model for the nonresponding part as an exponential tilting of the model for the responding part the exponential tilting model can be justified under the assumption that the response probability can be expressed as a semiparametric logistic regression model in this paper based on the exponential tilting model we propose a semiparametric estimation method of mean functionals with nonignorable missing data a semiparametric logistic regression model is assumed for the response probability and a nonparametric regression approach for missing data discussed in cheng 1994 is used in the estimator by adopting nonparametric components for the model the estimation method can be mad

regression analysis for categorical variables with outcome subject to nonignorable nonresponse abstract we develop a log linear model for categorical response subject to nonignorable nonresponse the paper differs from fay 1986 in its focus on estimation and hypothesis testing in a regression setting as opposed to imputation in a multivariate setting we present several new results concerning the existence of solutions on the boundary of the parameter space and the construction of confidence intervals for estimates we illustrate the method by estimating the proportion of voters preferring truman in a 1948 preelection poll mosteller hyman mccarthy marks and truman 1949 results may depend strongly on the model assumed for nonresponse goodness of fit tests are available for comparing alternative models

local model uncertainty and incomplete data bias with discussion problems of the analysis of data with incomplete observations are all too familiar in statistics they are doubly difficult if we are also uncertain about the choice of model we propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small loss of efficiency in parameter estimation due to incompleteness in the data has a dual interpretation the increase in variance when an assumed model is correct the bias in estimation when the model is incorrect examples include non ignorable missing data hidden confounders in observational studies and publication bias in meta analysis doubling variances before calculating confidence intervals or test statistics is suggested as a crude way of addressing the possibility of undetectably small departures from the model the problem of assessing the risk of lung cancer from passive smoking is used as a motivating example

on estimating average effects for multiple treatment groups we propose to estimate average exposure or treatment effects from observational data for multiple exposure groups by fitting an approximation of the marginal sample distribution of the response variable in each exposure group to the data the marginal sample distribution is a function of the true distribution of the response variable in the population and the assignment rule governing the allocation of the subjects to different exposure groups the assignment rule can depend on the response variable in addition to measured covariates and hence the method is appropriate even when the assumption of ignorable treatment assignment is not justified we estimate the exposure effects are estimated based on the population expectation pe of the outcome variable we compare the pe approach with an instrumental variable method and with several other methods including propensity score based approaches that assume ignorable assignment mechanisms we evaluate the robustness of the pe method under model misspecifications and illustrate it using data from a study of the impact of soy consumption on urinary concentrations of estrogen and estrogen metabolites in asian american women published 2012 this article is a us government work and is in the public domain in the usa

parametric models for response biased sampling summary suppose that subjects in a population follow the model f y lx 6 where y denotes a response x denotes a vector of covariates and 6 is the parameter to be estimated we consider response biased sampling in which a subject is observed with a probability which is a function of its response such response biased sampling frequently occurs in econometrics epidemiology and survey sampling the semiparametric maximum likelihood estimate of 6 is derived along with its asymptotic normality efficiency and variance estimates the estimate proposed can be used as a maximum partial likelihood estimate in stratified response selective sampling some computation algorithms are also provided

missing covariates in generalized linear models when the missing data mechanism is non ignorable we propose a method for estimating parameters in generalized linear models with missing covariates and a non ignorable missing data mechanism we use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one dimensional conditional distributions with each one dimensional conditional distribution consisting of a logistic regression we allow the covariates to be either categorical or continuous the joint covariate distribution is also modelled via a sequence of one dimensional conditional distributions and the response variable is assumed to be completely observed we derive the e and m steps of the em algorithm with non ignorable missing covariate data for categorical covariates we derive a closed form expression for the e and m steps of the em algorithm for obtaining the maximum likelihood estimates mles for continuous covariates we use a monte carlo version of the em algorithm to obtain the mles via the gibbs sampler computational techniques for gibbs sampling are proposed and implemented the parametric form of the assumed missing data mechanism itself is not testable from the data and thus the non ignorable modelling considered here can be viewed as a sensitivity analysis concerning a more complicated model therefore although a model may have passed the tests for a certain missing data mechanism this does not mean that we have captured even approximately the correct missing data mechanism hence model checking for the missing data mechanism and sensitivity analyses play an important role in this problem and are discussed in detail several simulations are given to demonstrate the methodology in addition a real data set from a melanoma cancer clinical trial is presented to illustrate the methods proposed

efficient quantile regression analysis with missing observations this article examines the problem of estimation in a quantile regression model when observations are missing at random under independent and nonidentically distributed errors we consider three approaches of handling this problem based on nonparametric inverse probability weighting estimating equations projection and a combination of both an important distinguishing feature of our methods is their ability to handle missing response and or partially missing covariates whereas existing techniques can handle only one or the other but not both we prove that our methods yield asymptotically equivalent estimators that achieve the desirable asymptotic properties of unbiasedness normality and n consistency because we do not assume that the errors are identically distributed our theoretical results are valid under heteroscedasticity a particularly strong feature of our methods under the special case of identical error distributions all of our proposed estimators achieve the semiparametric efficiency b

causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation causal inference with observational data can be performed under an assumption of no unobserved confounders unconfoundedness assumption there is however seldom clear subject matter or empirical

parametric fractional imputation for missing data analysis under a parametric model for missing data the em algorithm is a popular tool for flnding the maximum likelihood estimates mle of the parameters of the model imputation when carefully done can be used to facilitate the parameter estimation by applying the complete sample estimators to the imputed dataset the basic idea is to generate the imputed values from the conditional distribution of the missing data given the observed data multiple imputation is a bayesian approach to generate the imputed values from the conditional distribution in this article parametric fractional imputation is proposed as a parametric approach for generating imputed values using fractional weights the e step of the em algorithm can be approximated by the weighted mean of the imputed data likelihood where the fractional weights are computed from the current value of the parameter estimates some computational e ciency can be achieved using the idea of importance sampling in the monte carlo approximation of the conditional expectation the resulting estimator of the specifled parameters will be identical to the mle under missing data if the fractional weights are adjusted using a calibration step the proposed imputation method provides e cient parameter estimates for the model parameters specifled and also provides reasonable estimates for parameters that are not part of the imputation model for example domain means thus the proposed imputation method is a useful tool for general purpose data analysis variance estimation is covered and results from a limited simulation study are presented

computational error bounds for laplace transform inversion based on smoothing splines abstract in the numerical methods for the laplace transform inversion the errors quantification in computational processes is a crucial issue in this paper we propose two inversion methods based on smoothing splines combined with a procedure for the derivation of error bounds in particular we numerically study the impact of the fitting error amplification through the analysis of several sources of error and their propagation

the relative approximation degree in valued function fields we continue the work of kaplansky on immediate valued field extensions and determine special properties of elements in such extensions in particular we are interested in the question when an immediate valued function field of transcendence degree 1 is henselian rational i e generated modulo henselization by one element if so then ramification can be eliminated in this valued function field the results presented in this paper are crucial for the first author s proof of henselian rationality over tame fields which in turn is used in his work on local uniformization

protective estimation of mixed effects logistic regression when data are not missing at random we consider estimation of mixed effects logistic regression models for longitudinal data when missing outcomes are not missing at random a typology of missingness mechanisms is presented that includes missingness dependent on observed or missing current outcomes observed or missing lagged outcomes and subject specific effects when data are not missing at random consistent estimation by maximum marginal likelihood generally requires correct parametric modelling of the missingness mechanism which hinges on unverifiable assumptions we show that standard maximum conditional likelihood estimators are protective in the sense that they are consistent for monotone or intermittent missing data under a wide range of missingness mechanisms our approach requires neither specification of parametric models for the missingness mechanism nor refreshment samples and is straightforward to implement in standard software 2013 biometrika trust

analysis of multivariate missing data with nonignorable nonresponse we consider multivariate regression analysis with missing data in the outcome variables when the nonresponse mechanism depends on the underlying values of the responses and hence is nonignorable related problems include response biased sampling where data are sampled with probability depending only on the univariate response our methods do not require specification of the form of the nonresponse mechanism we show that under certain regularity conditions all the regression parameters can be identified from a conditional likelihood based on the complete cases if the marginal distribution of the covariates is known if the marginal distribution of the covariates is estimated from the data then the regression parameters are identified from a pseudolikelihood resulting from substituting the estimated marginal distribution of the covariates in the above conditional likelihood simulation studies suggest that the pseudolikelihood method is approximately unbiased in order to identify the model parameters usually the dimension of the covariates and observed responses is required to be at least as large as the dimension of the missing responses the method can also be modified to handle partial information about the missing data mechanism we also consider the special case where the missing data have a monotone pattern where better use of the incomplete information can be made under certain assumptions

comparing k populations with linear rank statistics abstract there are many nonparametric tests available for the analysis of k independent samples for example the kruskal wallis test wilcoxon rank sum test when k 2 is widely used for detecting location differences and mood or ansari bradley or sigel tukey rank statistics are often suggested for detecting scale differences if more general alternatives are of interest then one can use omnibus tests such as the generalized kolmogorov smirnov and cramer von mises statistics see conover 1980 secs 6 3 and 6 4 or kiefer 1959 unfortunately significant results from these omnibus tests are hard to interpret instead i would like to propose some new tests based on a 4 k table of linear rank statistics that are simple to interpret because the four rows of the table are aimed at specific alternatives location scale skewness and kurtosis row summary statistics consist of weighted sums of squares of the individual entries the first two of these are the kruskal wallis statistic and mood s k sa

a test of missing completely at random for multivariate data with missing values abstract a common concern when faced with multivariate data with missing values is whether the missing data are missing completely at random mcar that is whether missingness depends on the variables in the data set one way of assessing this is to compare the means of recorded values of each variable between groups defined by whether other variables in the data set are missing or not although informative this procedure yields potentially many correlated statistics for testing mcar resulting in multiple comparison problems this article proposes a single global test statistic for mcar that uses all of the available data the asymptotic null distribution is given and the small sample null distribution is derived for multivariate normal data with a monotone pattern of missing data the test reduces to a standard t test when the data are bivariate with missing data confined to a single variable a limited simulation study of empirical sizes for the test applied to normal and nonnormal data suggests th

semiparametric regression for repeated outcomes with nonignorable nonresponse abstract we consider inference about the parameter indexing the conditional mean of a vector of correlated outcomes given a vector of explanatory variables when some of the outcomes are missing in a subsample of the study and the probability of response depends on both observed and unobserved data values that is nonresponse is nonignorable we propose a class of augmented inverse probability of response weighted estimators that are consistent and asymptotically normal can for estimating when the response probabilities can be parametrically modeled and a can estimator exists the proposed estimators do not require full specification of a parametric likelihood and their computation does not require numerical integration our estimators can be viewed as an extension of generalized estimating equation estimators that allows for nonignorable nonresponse we show that our class essentially consists of all can estimators of we also show that the asymptotic variance of the optimal estimator in our

asymptotic distribution of auc nris and idi based on theory of u statistics the change in area under the curve auc the integrated discrimination improvement idi and net reclassification index nri are commonly used measures of risk prediction model performance some authors have reported good validity of associated methods of estimating their standard errors se and construction of confidence intervals whereas others have questioned their performance to address these issues we unite the auc idi and three versions of the nri under the umbrella of the u statistics family we rigorously show that the asymptotic behavior of auc nris and idi fits the asymptotic distribution theory developed for u statistics we prove that the auc nris and idi are asymptotically normal unless they compare nested models under the null hypothesis in the latter case asymptotic normality and existing se estimates cannot be applied to auc nris or idi in the former case se formulas proposed in the literature are equivalent to se formulas obtained from u statistics theory if we ignore adjustment for estimated parameters we use sukhatme randles dewet condition to determine when adjustment for estimated parameters is necessary we show that adjustment is not necessary for ses of the auc and two versions of the nri when added predictor variables are significant and normally distributed the ses of the idi and three category nri should always be adjusted for estimated parameters these results allow us to define when existing formulas for se estimates can be used and when resampling methods such as the bootstrap should be used instead when comparing nested models we also use the u statistic theory to develop a new se estimate of auc copyright 2017 john wiley sons ltd

the algebra and model theory of tame valued fields a henselian valued field k is called a tame field if its algebraic closure k is a tame extension that is the ramification field of the normal extension k k is algebraically closed every algebraically maximal kaplansky field is a tame field but not conversely we develop the algebraic theory of tame fields and then prove ax kochen ershov principles for tame fields this leads to model completeness and completeness results relative to value group and residue field as the maximal immediate extensions of tame fields will in general not be unique the proofs have to use much deeper valuation theoretical results than those for other classes of valued fields which have already been shown to satisfy ax kochen ershov principles the results of this paper have been applied to gain insight in the zariski space of places of an algebraic function field and in the model theory of large fields

a unified approach to linearization variance estimation from survey data after imputation for item nonresponse variance estimation after imputation is an important practical problem in survey sampling when deterministic imputation or stochastic imputation is used we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach we provide some applications of the approach to regression imputation fractional categorical imputation multiple imputation and composite imputation results from a simulation study under a factorial structure for the sampling response and imputation mechanisms show that the proposed linearization variance estimator performs well in terms of relative bias assuming a missing at random response mechanism

semiparametric inverse propensity weighting for nonignorable missing data to estimate unknown population parameters based on data having nonignorable missing values with a semiparametric exponential tilting propensity kim yu 2011 assumed that the tilting parameter is known or can be estimated from external data in order to avoid the identifiability issue to remove this serious limitation on the methodology we use an instrument i e a covariate related to the study variable but unrelated to the missing data propensity to construct some estimating equations because these estimating equations are semiparametric we profile the nonparametric component using a kernel type estimator and then estimate the tilting parameter based on the profiled estimating equations and the generalized method of moments once the tilting parameter is estimated so is the propensity and then other population parameters can be estimated using the inverse propensity weighting approach consistency and asymptotic normality of the proposed estimators are established the finite sample performance of the estimators is studied through simulation and a real data example is also presented

elimination of ramification i the generalized stability theorem we prove a general version of the stability theorem if k is a valued field such that the ramification theoretical defect is trivial for all of its finite extensions and if f k is a finitely generated transcendental extension of valued fields for which equality holds in the abhyankar inequality then the defect is also trivial for all finite extensions of f this theorem is applied to eliminate ramification in such valued function fields it has applications to local uniformization and to the model theory of valued fields in positive characteristic

indirect methods of imputation of missing data based on available units one of the most difficult problems confronting investigators who analyze data from surveys is how to treat missing data many statistical procedures cannot be used immediately if any values are missing imputation of missing data before starting statistical analysis is then necessary this paper proposes imputation methods of the mean based on indirect estimators of available cases a complete simulation study was performed to test the proposed techniques

inference for non random samples observational data are often analysed as if they had resulted from a controlled study and yet the tacit assumption of randomness can be crucial for the validity of inference we take some simple statistical models and supplement them by adding a parameter which reflects the degree of non randomness in the sample for a randomized study is known to be 0 we examine the profile log likelihood for and the sensitivity of inference to small non zero values of particular models cover the analysis of survey data with item non response the paired comparison t test and two group comparisons using observational data with covariates some practical examples are discussed allowing for sampling bias increases the uncertainty of estimation and weakens the significance of treatment effects sometimes substantially so

testing whether a survival distribution is new better than used of an unknown specified age a survival variable is a nonnegative random variable x with distribution function f and a survival function f 1 f this variable is said to be new better than used of specified age t 0 if f x t 0 f x f t 0 for all x 0 and a fixed t 0 testing h 0 f x t 0 f x f t 0 for all x 0 against h 1 f x t 0 f x f t 0 for x 0 when the point t 0 is unknown but can be estimated from the data is proposed when t 0 p the mean of f and also when t 0 p the pth percentile of f it is shown that while the known tests for known t 0 hollander park proschan 1986 ebrahimi habibullah 1990 continue to hold when t 0 and is estimated by x a simpler and distribution free test is possible when t 0 p and is estimated by x np the performance of this test is presented

monte carlo evidence on the choice between sample selection and two part models abstract this monte carlo study examines the relative performance of sample selection and two part models for data with a cluster at zero the data are drawn from a bivariate normal distribution with a positive correlation the alternative estimators are examined in terms of means squared error mean bias and pointwise bias the sample selection estimators include liml and fiml the two part estimators include a naive the true specification omitting the correlation coefficient and a data analytic testimator variant in the absence of exclusion restrictions the two part models are no worse and often appreciably better than selection models in terms of mean behavior but can behave poorly for extreme values of the independent variable liml had the worst performance of all four models empirically selection effects are difficult to distinguish from a non linear e g quadratic response with exclusion restrictions simple selection models were significantly better behaved than a naive two part model over subranges of the data but were negligibly better than the data analytic version

a data driven smooth test of symmetry in this paper we propose a data driven smooth test of symmetry we first transform the raw data via the probability integral transformation according to a symmetrized empirical distribution and show that under the null hypothesis of symmetry the transformed data has a limiting uniform distribution reducing testing for symmetry to testing for uniformity employing neyman s smooth test of uniformity we show that only odd ordered orthogonal moments of the transformed data are required in constructing the test statistic we present a standardized smooth test that is distribution free asymptotically and derive the asymptotic behavior of the test and establish its consistency extension to dependent data case is discussed we investigate the finite sample performance of the proposed tests on both homogeneous and mixed distributions with unobserved heterogeneity an empirical application on testing symmetry of wage adjustment process based on heterogeneous wage contracts with different durations is provided

key polynomials and pseudo convergent sequences abstract in this paper we introduce a new concept of key polynomials for a given valuation on k x we prove that such polynomials have many of the expected properties of key polynomials as those defined by maclane and vaquie for instance that they are irreducible and that the truncation of associated to each key polynomial is a valuation moreover we prove that every valuation on k x admits a sequence of key polynomials that completely determines in the sense which we make precise in the paper we also establish the relation between these key polynomials and pseudo convergent sequences defined by kaplansky

alternative methods for cps income imputation abstract the u s bureau of the census imputes missing income items in the income supplement of the current population survey cps by a technique commonly known as the cps hot deck this article compares cps hot deck imputations of wages and salary amounts with alternatives based on regression models for the logarithm of wages and salary and for the wage rate comparisons are effected by comparing imputations with an internal revenue service irs wages and salary amount found by an exact match of cps data to irs records although limitations in the matching and in the comparison variable preclude a definitive conclusion we find that a the cps hot deck does not underestimate income aggregates to any serious extent b model based alternatives have slightly smaller mean absolute error than the hot deck when comparable data bases of respondents are used to carry out imputations and c multivariate models for imputing recipiency weeks and hours worked and earnings need to be developed to provide re

pseudo population bootstrap methods for imputed survey data the most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables treating the imputed values as if they were observed may lead to invalid inferences bootstrap variance estimators for various finite population parameters are obtained using two pseudo population bootstrap schemes we establish the asymptotic properties of the resulting bootstrap variance estimators for population totals and population quantiles a simulation study suggests that the methods perform well in terms of relative bias and coverage probability

resolution of singularities of arithmetical threefolds ii abstract we prove grothendieck s conjecture on resolution of singularities for quasi excellent schemes x of dimension three and of arbitrary characteristic this applies in particular to x spec a a a reduced complete noetherian local ring of dimension three and to algebraic or arithmetical varieties of dimension three similarly if f is a number field a complete discretely valued field or more generally the quotient field of any excellent dedekind domain o any regular projective surface x f has a proper and flat model x over o which is everywhere regular

handling estimating equation with nonignorably missing data based on sir algorithm abstract in this paper the estimation of parameters in estimating equation with nonignorably missing data is considered based on a logistic regression model for the response mechanism and an assumed parametric model for the distribution of respondents we propose to use the sampling importance resampling sir algorithm to calculate the conditional expectation for non respondents the proposed method can avoid the difficulty of high dimensional kernel smoothing on calculating the conditional expectation under nonignorably missing data based on the sir algorithm we derive a modified estimating equation to facilitate comparison we apply the empirical likelihood method to derive the parameters estimator some numerical simulation results based on linear and nonlinear models show that the proposed method is robust and performs the best among the compared existing methods so we extend the estimation method of e g y using sir algorithm with nonignorably missing response to the estimation of parameters in generalized estimating equation and obtain a new and more robust estimation method

estimation of conditional prevalence from group testing data with missing covariates we consider estimating the conditional prevalence of a disease from data pooled according to the group testing mechanism consistent estimators have been proposed in the literature but they rely o

an efficient method of estimation for longitudinal surveys with monotone missing data panel attrition is frequently encountered in panel sample surveys when it is related to the observed study variable the classical approach of nonresponse adjustment using a covariate dependent dropout mechanism can be biased we consider an efficient method of estimation with monotone panel attrition when the response probability depends on the previous values of study variable as well as other covariates because of the monotone structure of the missing pattern the response mechanism is missing at random the proposed estimator is asymptotically optimal in the sense that it minimizes the asymptotic variance of a class of estimators that can be written as a linear combination of the unbiased estimators of the panel estimates for each wave and incorporates all available information using generalized least squares variance estimation is discussed and results from a simulation study are presented copyright 2012 oxford university press

value groups residue fields and bad places of rational function fields we classify all possible extensions of a valuation from a ground field k to a rational function field in one or several variables over k we determine which value groups and residue fields can appear and we show how to construct extensions having these value groups and residue fields in particular we give several constructions of extensions whose corresponding value group and residue field extensions are not finitely generated in the case of a rational function field k x in one variable we consider the relative algebraic closure of k in the henselization of k x with respect to the given extension and we show that this can be any countably generated separable algebraic extension of k in the tame case we show how to determine this relative algebraic closure finally we apply our methods to power series fields and the p adics

empirical likelihood based inference under imputation for missing response data inference under kernel regression imputation for missing response data is considered an adjusted empirical likelihood approach to inference for the mean of the response variable is developed a nonparametric version of wilks theorem is proved for the adjusted empirical log likelihood ratio by showing that it has an asymptotic standard chi squared distribution and the corresponding empirical likelihood confidence interval for the mean is constructed with auxiliary information an empirical likelihood based estimator is defined and an adjusted empirical log likelihood ratio is derived asymptotic normality of the estimator is proved also it is shown that the adjusted empirical log likelihood ratio obeys wilks theorem a simulation study is conducted to compare the adjusted empirical likelihood and the normal approximation methods in terms of coverage accuracies and average lengths of confidence intervals based on biases and standard errors a comparision is also made by simulation between the empirical likelihood based estimator and related estimators our simulation indicates that the adjusted empirical likelihood method performs competitively and that the use of auxiliary information provides improved inferences

imputation of missing values when the probability of response depends on the variable being imputed abstract a method is developed for imputing missing values when the probability of response depends upon the variable being imputed the missing data problem is viewed as one of parameter estimation in a regression model with stochastic censoring of the dependent variable the prediction approach to imputation is used to solve this estimation problem wages and salaries are imputed to non respondents in the current population survey and the results are compared to the nonrespondents irs wage and salary data the stochastic censoring approach gives improved results relative to a prediction approach that ignores the response mechanism

every place admits local uniformization in a finite extension of the function field we prove that every place p of an algebraic function field f k of arbitrary characteristic admits local uniformization in a finite extension f of f we show that f f can be chosen to be galois after a finite purely inseparable extension of the ground field k instead of being galois the extension can also be chosen such that the induced extension fp fp of the residue fields is purely inseparable and the value group of f only gets divided by the residue characteristic if f lies in the completion of an abhyankar place then no extension of f is needed our proofs are based solely on valuation theoretical theorems which are of particular importance in positive characteristic they are also applicable when working over a subring r k and yield similar results if r is regular and of dimension smaller than 3

variance estimation for survey data with composite imputation and nonnegligible sampling fractions abstract this article considers variance estimation for horvitz thompson type estimated totals based on survey data with imputed non respondents and with nonnegligible sampling fractions a method based on a variance decomposition is proposed our method can be applied to complicated situations where a composite of some deterministic and or random imputation methods is used including using imputed data in subsequent imputations although here linearization or taylor expansion type techniques are adopted replication methods such as the jackknife balanced repeated replication and random groups can also be used in applying our method to derive variance estimators using our method variance estimators can be derived under either the customary design based approach or the model assisted approach and are asymptotically unbiased and consistent the transportation annual survey conducted at the u s census bureau in which nonrespondents are imputed using a composite of cold deck and ratio type imputation m

multiply robust imputation procedures for the treatment of item nonresponse in surveys summaryitem nonresponse in surveys is often treated through some form of imputation we introduce multiply robust imputation in finite population sampling this is closely related to multiple robustness which extends double robustness in practice multiple nonresponse models and multiple imputation models may be fitted each involving different subsets of covariates and possibly different link functions an imputation procedure is said to be multiply robust if the resulting estimator is consistent when all models but one are misspecified a jackknife variance estimator is proposed and shown to be consistent random and fractional imputation procedures are discussed a simulation study suggests that the proposed estimation procedures have low bias and high efficiency

semiparametric models and inference for the effect of a treatment when the outcome is nonnegative with clumping at zero the outcome in a randomized experiment is sometimes nonnegative with a clump of observations at zero and continuously distributed positive values one widely used model for a nonnegative outcome with a clump at zero is the tobit model which assumes that the treatment has a shift effect on the distribution of a normally distributed latent variable and the observed outcome is the maximum of the latent variable and zero we develop a class of semiparametric models and inference procedures that extend the tobit model in two useful directions first we consider more flexible models for the treatment effect than the shift effect of the tobit model for example our models allow for the treatment to have a larger in magnitude effect for upper quantiles second we make semiparametric inferences using empirical likelihood that allow the underlying latent variable to have any distribution unlike the original tobit model that assumes the latent variable is normally distributed we apply our approach to data from the rand health insurance experiment we also extend our approach to observational studies in which treatment assignment is strongly ignorable

key polynomials and minimal pairs abstract in this paper we establish the relation between key polynomials as defined in 12 and minimal pairs of definition of a valuation we also discuss truncations of valuations on a polynomial ring k x we prove that a valuation is equal to its truncation on some polynomial if and only if is valuation transcendental another important result of this paper is that if is any extension of to k x and is a complete sequence of key polynomials for without last element then for each q there exists a suitable root a q k of q such that a q q is a pseudo convergent sequence defining

estimating distribution functions from survey data summary a simple method for estimating population distribution functions and associated quantiles from sample survey data is described and some asymptotic theory for it presented the method assumes a model based approach to survey estimation and allows auxiliary population information to be directly incorporated into the estimation process monte carlo results comparing the proposed method with conventional design based methods are given these suggest that the model based approach offers significant gains when the auxiliary population information is linearly related to the survey variables of interest

semiparametric bayesian estimation for marginal parametric potential outcome modeling application to causal inference we propose a new semiparametric bayesian model for causal inference in which assignment to treatment depends on potential outcomes the model uses the probit stick breaking process mixture proposed by chung and dunson 2009 a variant of the dirichlet process mixture modeling in contrast to previous bayesian models the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes while it relaxes the strong ignorability assumption and requires no parametric model assumption for the assignment model and conditional distribution of the covariate vector the proposed estimation method is more robust than maximum likelihood estimation in that it does not require knowledge of the full joint distribution of potential outcomes covariates and assignments in addition the method is more efficient than fully nonparametric bayes methods we apply this model to infer the differential effects of cognitive and noncognitive skills on the wages of production and nonprodu

fractional hot deck imputation to compensate for item nonresponse hot deck imputation procedures replace missing values with values that occur in the sample fractional hot deck imputation replaces each missing observation with a set of imputed values and assigns a weight to each imputed value under the model in which observations in an imputation cell are independently and identically distributed fractional hot deck imputation is shown to be an effective imputation procedure a consistent replication variance estimation procedure for estimators computed with fractional imputation is suggested simulations show that fractional imputation and the suggested variance estimator are superior to multiple imputation estimators in general and much superior to multiple imputation for estimating the variance of a domain mean

jackknife variance estimation for nearest neighbor imputation nearest neighbor imputation is a popular hot deck imputation method used to compensate for nonresponse in sample surveys although this method has a long history of application the problem of variance estimation after nearest neighbor imputation has not been fully investigated because nearest neighbor imputation is a nonparametric method a nonparametric variance estimation technique such as the jackknife is desired we show that the naive jackknife that treats imputed values as observed data produces serious underestimation we also show that rao and shao s adjusted jackknife or the jackknife with each pseudoreplicate reimputed which produces asymptotically unbiased and consistent jackknife variance estimators for other imputation methods such as mean imputation random hot deck imputation and ratio or regression imputation produces serious overestimation in the case of nearest neighbor imputation two partially reimputed and a partially adjusted jackknife variance estimators are proposed and s

on structural equation modeling with data that are not missing completely at random a general latent variable model is given which includes the specification of a missing data mechanism this framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data here missing completely at random is not a prerequisite for unbiased estimation in large samples as when using the traditional listwise or pairwise present data approaches the theory is connected with old and new results in the area of selection and factorial invariance it is pointed out that in many applications maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software such as lisrel and liscomp several sets of artifical data are generated within the general model framework the proposed estimator is compared to the two traditional ones and found superior

error analysis of a collocation method for numerically inverting a laplace transform in case of real samples in s cuomo l d amore a murli m r rizzardi computation of the inverse laplace transform based on a collocation method which uses only real values j comput appl math 198 1 2007 98 115 the authors proposed a collocation method c method for real inversion of laplace transforms lt based on the truncated laguerre expansion of the inverse function f n x e s x k 0n 1c ke b xl k 2bx where s b are parameters and c k k n are the maclaurin coefficients of a function depending on the lt the computational kernel of a c method is the solution of a vandermonde linear system where the right hand side is obtained evaluating the lt on the real axis the bjorck pereira algorithm has been used for solving the vandermonde linear system providing a computable componentwise error bound on the solution for an inversion problem on discrete data f is known on a pre assigned set of points we refer to these points as samples of f only and the major challenge is to deal with a significative loss of information a natural approach to overcome this intrinsic difficulty is to construct a suitable fitting model that approximates the given data in this case we show that such approach leads to a c method with perturbed right hand side and then we use again the bjorck pereira algorithm starting from the error introduced by the fitting model we study its propagation in order to determine the maximum attainable accuracy on f n moreover we derive a computable error bound that allows to get the suitable value of the parameter n that gives the maximum attainable accuracy