Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data

By Jiwei Zhao, Jun Shao. 2015

146 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bac0b43d81e662aa55d8695c8d33b74ed14f1c3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Kosuke Morikawa
2. Jae Kwang Kim
3. Yutaka Kano

In Statistics and Probability

1. Kosuke Morikawa
2. Yutaka Kano
3. Jae Kwang Kim
4. Zhiqiang Tan

Citations in the corpus

• Semiparametric maximum likelihood estimation with data missing not at random (2017, methodology): “Zhao & Shao (2015) extended the method of Tang, Little, & Raghunathan (2003) to generalized linear models, which do not require parametric model assumptions for the response mechanism.”