A survey of bootstrap methods in finite population sampling

Mashreghi, Zeinab; Haziza, David; Léger, Christian

doi:10.1214/16-ss113

Cited by 58 publications

(44 citation statements)

References 57 publications

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“…In order to estimate variance correctly after imputation we can consider the extension of our approach to multiple imputation (see Rubin 1987) or use resampling techniques such as the bootstrap or jackknife adapted to account for the fact that were dealing with a finite population (see e.g. Efron andTibshirani 1979Mashreghi, Haziza andLéger 2016).…”

Section: Discussionmentioning

confidence: 99%

Calibrated Hot Deck Imputation for Numerical Data Under Edit Restrictions

Waal¹,

Coutinho²,

Shlomo³

2017

Journal of Survey Statistics and Methodology

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We develop a non-parametric imputation method for item non-response based on the wellknown hot-deck approach. The proposed imputation method is developed for imputing numerical data that ensure that all record-level edit rules are satisfied and previously estimated or known totals are exactly preserved. We propose a sequential hot-deck imputation approach that takes into account survey weights. Original survey weights are not changed, rather the imputations themselves are calibrated so that weighted estimates will equal known or estimated population totals. Edit rules are preserved by integrating the sequential hot-deck imputation with Fourier-Motzkin elimination which defines the range of feasible values that can be used for imputation such that all record-level edits will be satisfied. We apply the proposed imputation method under different scenarios of random and nearest-neighbour hot-deck on two data sets: an annual structural business survey and a synthetically generated data set with a large proportion of missing data. We compare the proposed imputation methods to standard imputation methods based on a set of evaluation measures.

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Section: Discussionmentioning

confidence: 99%

Calibrated Hot Deck Imputation for Numerical Data Under Edit Restrictions

Waal¹,

Coutinho²,

Shlomo³

2017

Journal of Survey Statistics and Methodology

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“…In this paper, we focus on the bootstrap procedure under simple random sampling without replacement. See Mashreghi, Haziza, and Léger [53] for bootstrap methods which handle other complex sampling designs.…”

Section: Variance Estimationmentioning

confidence: 99%

A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

McConville

Moisen

Frescino

2020

Forests

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National forest inventories in many countries combine expensive ground plot data with remotely-sensed information to improve precision in estimators of forest parameters. A simple post-stratified estimator is often the tool of choice because it has known statistical properties, is easy to implement, and is intuitive to the many users of inventory data. Because of the increased availability of remotely-sensed data with improved spatial, temporal, and thematic resolutions, there is a need to equip the inventory community with a more diverse array of statistical estimators. Focusing on generalized regression estimators, we step the reader through seven estimators including: Horvitz Thompson, ratio, post-stratification, regression, lasso, ridge, and elastic net. Using forest inventory data from Daggett county in Utah, USA as an example, we illustrate how to construct, as well as compare the relative performance of, these estimators. Augmented by simulations, we also show how the standard variance estimator suffers from greater negative bias than the bootstrap variance estimator, especially as the size of the assisting model grows. Each estimator is made readily accessible through the new R package, mase. We conclude with guidelines in the form of a decision tree on when to use which an estimator in forest inventory applications.

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“…Step 2 For each bootstrap sample, compute the corresponding Hájek estimator (25). They will be denoted by F * H,m (y), m = 1, .…”

Section: Phase 2: Resampling Design From the Pseudo-populationmentioning

confidence: 99%

A unified principled framework for resampling based on pseudo-populations: Asymptotic theory

Conti¹,

Marella²,

Mecatti³

et al. 2020

Bernoulli

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In this paper, a class of resampling techniques for finite populations under complex sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in:(i) constructing a "pseudo-population" on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the "sampling design level". Theoretical justifications based on large sample theory are provided. New approaches to construct pseudo populations based on various forms of calibrations are proposed. Finally, a simulation study is performed.

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A survey of bootstrap methods in finite population sampling

Cited by 58 publications

References 57 publications

Calibrated Hot Deck Imputation for Numerical Data Under Edit Restrictions

Calibrated Hot Deck Imputation for Numerical Data Under Edit Restrictions

A Tutorial on Model-Assisted Estimation with Application to Forest Inventory

A unified principled framework for resampling based on pseudo-populations: Asymptotic theory

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