RT Journal Article
SR Electronic(1)
A1 Davidian, MarieYR 2022
T1 Methods Based on Semiparametric Theory for Analysis in the Presence of Missing Data
JF Annual Review of Statistics and Its Application,
VO 9
IS Volume 9, 2022
SP 167
OP 196
DO https://doi.org/10.1146/annurev-statistics-040120-025906
PB Annual Reviews,
SN 2326-831X,
AB A statistical model is a class of probability distributions assumed to contain the true distribution generating the data. In parametric models, the distributions are indexed by a finite-dimensional parameter characterizing the scientific question of interest. Semiparametric models describe the distributions in terms of a finite-dimensional parameter and an infinite-dimensional component, offering more flexibility. Ordinarily, the statistical model represents distributions for the full data intended to be collected. When elements of these full data are missing, the goal is to make valid inference on the full-data-model parameter using the observed data. In a series of fundamental works, Robins, Rotnitzky, and colleagues derived the class of observed-data estimators under a semiparametric model assuming that the missingness mechanism is at random, which leads to practical, robust methodology for many familiar data-analytic challenges. This article reviews semiparametric theory and the key steps in this derivation.,
UL https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-040120-025906