Simple estimators for network sampling

04/03/2018
by   Steve Thompson, et al.
0

Some conceptually simple estimators for network sampling are introduced. The new estimators are based on using fast sampling designs or processes on the network sample to estimate relative approximate inclusion probabilities of the actual sampling. We evaluate the effectiveness of the new estimators with simulations using a well-known empirical network data set as the exper- imental population. We find that the new estimators can bring substantial reductions in bias and mean square error compared to estimators in common use.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/04/2018

Reducing Seed Bias in Respondent-Driven Sampling by Estimating Block Transition Probabilities

Respondent-driven sampling (RDS) is a popular approach to study marginal...
research
07/01/2019

Transformed Naive Ratio and Product Based Estimators for Estimating Population Mode in Simple Random Sampling

In this paper, we propose a transformed naïve ratio and product based es...
research
02/14/2019

Optimal disclosure risk assessment

Protection against disclosure is a legal and ethical obligation for agen...
research
05/21/2022

Design-based estimators of distribution function in ranked set sampling with an application

Empirical distribution functions (EDFs) based on ranked set sampling (RS...
research
01/14/2021

Enhanced Cube Implementation For Highly Stratified Population

A balanced sampling design should always be the adopted strategies if au...
research
07/08/2020

A new generalized newsvendor model with random demand

Newsvendor problem is an extensively researched topic in inventory manag...

Please sign up or login with your details

Forgot password? Click here to reset