A Roadmap to Asymptotic Properties with Applications to COVID-19 Data

10/07/2022
by   Elvis Han Cui, et al.
0

Asymptotic properties of statistical estimators play a significant role both in practice and in theory. However, many asymptotic results in statistics rely heavily on the independent and identically distributed (iid) assumption, which is not realistic when we have fixed designs. In this article, we build a roadmap of general procedures for deriving asymptotic properties under fixed designs and the observations need not to be iid. We further provide their applications in many statistical applications. Finally, we apply our results to Poisson regression using a COVID-19 dataset as an illustration to demonstrate the power of these results in practice.

READ FULL TEXT
research
04/13/2023

On the asymptotic properties of a bagging estimator with a massive dataset

Bagging is a useful method for large-scale statistical analysis, especia...
research
11/02/2018

Semiparametric Mixture Regression with Unspecified Error Distributions

In fitting a mixture of linear regression models, normal assumption is t...
research
10/28/2022

Minimum Kernel Discrepancy Estimators

For two decades, reproducing kernels and their associated discrepancies ...
research
10/11/2018

Kaplan-Meier V and U-statistics

In this paper we study Kaplan-Meier V and U-statistics defined as θ(F̂_n...
research
11/27/2019

On Robust Pseudo-Bayes Estimation for the Independent Non-homogeneous Set-up

The ordinary Bayes estimator based on the posterior density suffers from...
research
05/17/2022

Sampling with replacement vs Poisson sampling: a comparative study in optimal subsampling

Faced with massive data, subsampling is a commonly used technique to imp...
research
08/19/2022

Approximating Symmetrized Estimators of Scatter via Balanced Incomplete U-Statistics

We derive limiting distributions of symmetrized estimators of scatter, w...

Please sign up or login with your details

Forgot password? Click here to reset