Complex sampling designs: uniform limit theorems and applications

Abstract

In this paper, we develop a general approach to proving global and local uniform limit theorems for the Horvitz-Thompson empirical process arising from complex sampling designs. Global theorems such as Glivenko-Cantelli and Donsker theorems, and local theorems such as local asymptotic modulus and related ratio-type limit theorems are proved for both the Horvitz-Thompson empirical process, and its calibrated version. Limit theorems of other variants and their conditional versions are also established. Our approach reveals an interesting feature: the problem of deriving uniform limit theorems for the Horvitz-Thompson empirical process is essentially no harder than the problem of establishing the corresponding finite-dimensional limit theorems. These global and local uniform limit theorems are then applied to important statistical problems including (i) $M$-estimation (ii) $Z$-estimation (iii) frequentist theory of Bayes procedures, all with weighted likelihood, to illustrate their wide applicability.

46 pages

References in corpus (8)

• 2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization
• Two likelihood-based semiparametric estimation methods for panel count data with covariates
• Bootstrap consistency for general semiparametric $M$-estimation
• Concentration inequalities and asymptotic results for ratio type empirical processes
• Coupling methods for multistage sampling
• Asymptotic theory for the semiparametric accelerated failure time model with missing data
• Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers
• Oracle posterior contraction rates under hierarchical priors

Cited by in corpus (2)

• A comparison of estimators of mean and its functions in finite populations
• Multiplier U-processes: sharp bounds and applications