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19
What to do about missing values in time series crosssection data
, 2009
"... Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last halfdecade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficien ..."
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Cited by 14 (4 self)
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Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last halfdecade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the timeseries cross section data structures common in these fields. Weattempttorectify this situation with three related developments. First, we build a multiple imputation model that allows smooth time trends, shifts across crosssectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficulttointerpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler crosssectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms. We develop an approach to analyzing data with
LargeScale Imputation For Complex Surveys
 Eltinge, and R.J.A Little, (Eds.) Survey Nonresponse
, 1999
"... this paper, we provide more detail on the algorithm than has previously been given and present some new data on the magnitude of the imputation. ..."
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Cited by 2 (0 self)
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this paper, we provide more detail on the algorithm than has previously been given and present some new data on the magnitude of the imputation.
Bootstrapping Sample Quantiles Based On Complex Survey Data Under Hot Deck Imputation
 Statistica Sinica
, 1998
"... The bootstrap method works for both smooth and nonsmooth statistics, and replaces theoretical derivations by routine computations. With survey data sampled using a stratified multistage sampling design, the consistency of the bootstrap variance estimators and bootstrap confidence intervals was estab ..."
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Cited by 2 (1 self)
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The bootstrap method works for both smooth and nonsmooth statistics, and replaces theoretical derivations by routine computations. With survey data sampled using a stratified multistage sampling design, the consistency of the bootstrap variance estimators and bootstrap confidence intervals was established for smooth statistics such as functions of sample means (Rao and Wu, 1988). However, similar results are not available for nonsmooth statistics such as the sample quantiles and the sample low income proportion. We consider a more complicate situation where the data set contains nonrespondents imputed using a random hot deck method. We establish the consistency of the bootstrap procedures for the sample quantiles and the sample low income proportion. Some empirical results are also presented. Key words and phrases. Stratified multistage sampling, Low income proportion, Imputation classes. Short Title: BOOTSTRAPPING QUANTILES FOR SURVEY DATA 1 The research was partially supported by Na...
Balanced Repeated Replication For Stratified Multistage Survey Data Under Imputation
"... Balanced repeated replication (BRR) is a popular method for variance estimation in surveys. The standard BRR method works by first creating a set of "balanced" pseudoreplicated data sets from the original data set. For a survey estimator `, the BRR variance estimator is the average of squared devi ..."
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Cited by 2 (0 self)
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Balanced repeated replication (BRR) is a popular method for variance estimation in surveys. The standard BRR method works by first creating a set of "balanced" pseudoreplicated data sets from the original data set. For a survey estimator `, the BRR variance estimator is the average of squared deviations ` (r) \Gamma `, where ` (r) is the same as ` but based on the data in the rth pseudoreplicated data set only. When there are a large number of imputed missing values (nonrespondents), however, treating the imputed values as observed data and applying the standard BRR variance estimation formula does not produce valid variance estimators. Intuitively, the variation due to imputation can be captured by the BRR method if every pseudoreplicated data set is imputed in exactly the same way as the original data set is imputed (assuming that the data set contains flags for nonrespondents). When a random imputation method (such as random hot deck imputation, random ratio imputation,...
Variance Estimation For Imputed Survey Data With NonNegligible Sampling Fractions
"... We consider variance estimation for HorvitzThompson type estimated totals based on survey data with imputed nonrespondents 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 som ..."
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We consider variance estimation for HorvitzThompson type estimated totals based on survey data with imputed nonrespondents 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 to impute. We mainly adopt the linearization or Taylor expansion type techniques, but 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 designbased approach or the modelbased 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 methods, is used as ...
EMPIRICAL LIKELIHOOD FOR ESTIMATING EQUATIONS WITH MISSING VALUES
, 903
"... We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wideranging, we propose a nonparametric imputation of the missing values from ..."
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We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wideranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more efficient parameter estimation. The proposed method is further evaluated by simulation and an empirical study on a genetic dataset on recombinant inbred mice. 1. Introduction. Missing
INCORPORATING THE FINITE POPULATION CORRECTION INTO VARIANCE ESTIMATES FROM IMPUTED DATA
"... bootstrap, without replacement sampling jackknife, 1. ..."
METHODOLOGICAL DOCUMENTS HANDBOOK "HOW TO MAKE A QUALITY REPORT" CONTENTS
"... "Assessment of quality in statistics" ..."
A PSEUDO EMPIRICAL LIKELIHOOD APPROACH FOR STRATIFIED SAMPLES WITH NONRESPONSE
, 903
"... Nonresponse is common in surveys. When the response probability of a survey variable Y depends on Y through an observed auxiliary categorical variable Z (i.e., the response probability of Y is conditionally independent of Y given Z), a simple method often used in practice is to use Z categories as i ..."
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Nonresponse is common in surveys. When the response probability of a survey variable Y depends on Y through an observed auxiliary categorical variable Z (i.e., the response probability of Y is conditionally independent of Y given Z), a simple method often used in practice is to use Z categories as imputation cells and construct estimators by imputing nonrespondents or reweighting respondents within each imputation cell. This simple method, however, is inefficient when some Z categories have small sizes and ad hoc methods are often applied to collapse small imputation cells. Assuming a parametric model on the conditional probability of Z given Y and a nonparametric model on the distribution of Y, we develop a pseudo empirical likelihood method to provide more efficient survey estimators. Our method avoids any ad hoc collapsing small Z categories, since reweighting or imputation is done across Z categories. Asymptotic distributions for estimators of population means based on the pseudo empirical likelihood method are derived. For variance estimation, we consider a bootstrap procedure and its consistency is established. Some simulation results are provided to assess the finite sample performance of the proposed estimators. 1. Introduction. Nonresponse