IASS Webinar 39: Model-Based Optimal Designs for a Multipurpose Farm Survey

Map Unavailable

Date(s) - 24/04/2024
2:00 pm - 3:30 pm

Category(ies) No Categories

IASS Webinar 39: Model-Based Optimal Designs for a Multipurpose Farm Survey

Speaker:  Jay Breidt

NORC at the University of Chicago

 24 April 2024  at 2pm – 3:30pm (CET)


All are invited to the webinar, organised by the International Association of Survey Statisticians.

Please register for the IASS Webinar at:

https://bit.ly/iass-webinar-39 [bit.ly]

After registering, you will receive a confirmation email containing information about joining the webinar. There will be time for questions. The webinar will be recorded and made available on the IASS and ISI web site. See below for the abstract and biography of the speakers.


Webinar Abstract

We consider model-based optimal sampling designs for multipurpose surveys with multiple measures of size. The problem is motivated by surveys conducted by the United States Department of Agriculture’s National Agricultural Statistics Service (NASS), in which estimates of planted and harvested acres of different crops are of interest, and historical acreages are available on the frame as measures of size. We use convex optimization to find the inclusion probabilities that minimize expected sample size subject to target precision requirements for different study variables, along with other inequality constraints. The precision requirements are computed as anticipated coefficients of variation under models relating study variables to frame measures of size. These same models are used in established NASS strategies for the multipurpose survey problem to obtain Multivariate Probability Proportional to Size (MPPS) inclusion probabilities. MPPS uses the measures of size to determine optimal inclusion probabilities for each model, then maximizes over models. This solution is practical but not optimal. We compare the use of the MPPS and optimal inclusion probabilities under different designs (Poisson sampling and balanced sampling) and different estimators (calibrated and uncalibrated) via a Monte Carlo experiment using a simulated population of farms with realistic size and complexity.


This is joint work with Benjamin Reist (NORC), Ruochen Ma (NORC), and Lu Chen (NISS/NASS).  



Jay Breidt is a Senior Fellow in the Department of Statistics and Data Science for NORC at the University of Chicago and Professor Emeritus of Statistics at Colorado State University. He is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics. Breidt’s expertise is mathematical statistics, with interests that include survey sampling, time series, nonparametric regression, and uncertainty quantification for complex scientific models.