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What is MRP in statistics?
Multilevel regression with poststratification (MRP) (sometimes called “Mister P”) is a statistical technique used for correcting model estimates for known differences between a sample population (the population of the data you have), and a target population (a population you would like to estimate for).
Is MRP a Bayesian?
To be specific, MRP uses Bayesian statistics and multilevel modeling (Gelman and Little 1997; Park, Gelman, and Bafumi 2006) to improve upon the estimation of the effects of individual and geographic predictors.
How do you do post-stratification?
First, you adjust the margin of race, so that each of the weighted totals of race categories aligns with the known population total. (This is precisely post-stratification on race). Then you post-stratify on age, then on gender, then on education, then on income.
When to use multilevel regression and poststratification?
Multilevel regression and poststrati\\fcation (MRP) is an increasingly popular tool for adjusting a non-representative sample to a larger population.
When was the first paper on multilevel regression?
The first paper on MRP was from 1997. And, even then, the component pieces were not new: we were just basically combining two existing ideas from survey sampling: regression estimation and small-area estimation. It would be more accurate to call MRP a methodology from the 1990s, or even the 1970s.
What’s the difference between MRP and multilevel regularization?
“Correcting non-response bias” is not an alternative to MRP; rather, MRP is a method for correcting non-response bias. The whole point of the “multilevel” ( more generally, “regularization”) in MRP is that it allows us to adjust for more factors that could drive nonresponse bias.
How is multilevel regression used to estimate population?
Estimates for smaller population subsets exhibited a greater degree of shrinkage towards the national estimate. Multilevel regression and poststratification provides a promising analytical approach to addressing potential participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.