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

DOI10.3150/19-BEJ1138zbMath1466.62241arXiv1705.03827OpenAlexW3004020400MaRDI QIDQ2295026

Pier Luigi Conti, Fulvia Mecatti, Daniela Marella, Federico Andreis

Publication date: 12 February 2020

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.03827

zbMATH Keywords

bootstrapconfidence intervalsresamplingcalibrationvariance estimationfinite populations\(\pi\) ps sampling designs

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Sampling theory, sample surveys (62D05) Nonparametric statistical resampling methods (62G09)

Related Items (5)

Accounting for Non‐ignorable Sampling and Non‐response in Statistical Matching ⋮ A unified principled framework for resampling based on pseudo-populations: asymptotic theory ⋮ Efficient unequal probability resampling from finite populations ⋮ Improving the causal treatment effect estimation with propensity scores by the bootstrap ⋮ Bayesian network structural learning from complex survey data: a resampling based approach

Cites Work

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• A survey of bootstrap methods in finite population sampling
• Entropy of unequal probability sampling designs
• Asymptotic properties of sample quantiles from a finite population
• On the distance between some \(\pi\)ps sampling designs
• Some asymptotic theory for the bootstrap
• Bootstrap methods: another look at the jackknife
• Rate of convergence to normal distribution for the Horvitz-Thompson estimator.
• Resampling methods for dependent data
• A survey of limit laws for bootstrapped sums
• Variance estimation with Chao's sampling scheme
• On the estimation of the distribution function of a finite population under high entropy sampling designs, with applications
• A unified principled framework for resampling based on pseudo-populations: asymptotic theory
• Functional central limit theorems for single-stage sampling designs
• Sampling algorithms.
• Object-Oriented Bayesian Networks for Modeling the Respondent Measurement Error
• Empirical Processes in Survey Sampling with (Conditional) Poisson Designs
• A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population
• The Role of Sampling Weights When Modeling Survey Data
• Resampling Inference With Complex Survey Data
• A Resampling Procedure for Complex Survey Data
• Asymptotic Statistics
• Weighted finite population sampling to maximize entropy
• Bootstrap Methods for Finite Populations
• Survey Design Under the Regression Superpopulation Model
• Inference for Quantiles of a Finite Population: Asymptotic versus Resampling Results
• On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling
• Pareto Sampling versus Sampford and Conditional Poisson Sampling
• Asymptotic Theory of Rejective Sampling with Varying Probabilities from a Finite Population
• Second thoughts on the bootstrap
• On the impact of bootstrap in survey sampling and small-area estimation

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