Asymptotic Normality of Quadratic Estimators.

Stoch Process Their Appl

Departments of Biostatistics and Epidemiology, School of Public Health, Harvard University, Mathematical Institute, Leiden University.

Published: December 2016

We prove conditional asymptotic normality of a class of quadratic U-statistics that are dominated by their degenerate second order part and have kernels that change with the number of observations. These statistics arise in the construction of estimators in high-dimensional semi- and non-parametric models, and in the construction of nonparametric confidence sets. This is illustrated by estimation of the integral of a square of a density or regression function, and estimation of the mean response with missing data. We show that estimators are asymptotically normal even in the case that the rate is slower than the square root of the observations.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5232897PMC
http://dx.doi.org/10.1016/j.spa.2016.04.005DOI Listing

Publication Analysis

Top Keywords

asymptotic normality
8
normality quadratic
4
quadratic estimators
4
estimators prove
4
prove conditional
4
conditional asymptotic
4
normality class
4
class quadratic
4
quadratic u-statistics
4
u-statistics dominated
4

Similar Publications

Animal growth is a fundamental component of population dynamics, which is closely tied to mortality, fecundity, and maturation. As a result, estimating growth often serves as the basis of population assessments. In fish, analysing growth typically involves fitting a growth model to age-at-length data derived from counting growth rings in calcified structures.

View Article and Find Full Text PDF

We study kernel-based estimation methods for partially linear varying coefficient additive hazards models, where the effects of one type of covariates can be modified by another. Existing kernel estimation methods for varying coefficient models often use a "local" approach, where only a small local neighborhood of subjects are used for estimating the varying coefficient functions. Such a local approach, however, is generally inefficient as information about some non-varying nuisance parameter from subjects outside the neighborhood is discarded.

View Article and Find Full Text PDF

Coronary artery disease (CAD) is a multigenic condition influenced by both nature and nurture (60% to 40%). Prognosis of CAD is based on familial patterns. This study examined and analyzed the susceptibility of CAD to genetic variants in various Pakistani families.

View Article and Find Full Text PDF

This article addresses the problem of measurement invariance in psychometrics. In particular, its focus is on the invariance assumption of item parameters in a class of models known as Rasch models. It suggests a mixed-effects or random intercept model for binary data together with a conditional likelihood approach of both estimating and testing the effects of multiple covariates simultaneously.

View Article and Find Full Text PDF

Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates.

Biometrics

January 2025

MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, United Kingdom.

Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!