A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data

By Jae Kwang Kim, C. Yu. 2011

217 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Evolution, Behavior and Systematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/16bcafbc62f8cde494c68a32c92d626c16f210dd

How researchers see this paper

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

In Artificial Intelligence

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

In Ecology, Evolution, Behavior and Systematics

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

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): “In this section we apply our proposed methods to the Korea Labor and Income Panel Survey (KLIPS) data, which was previously analyzed by Kim & Yu (2011), Wang, Shao, & Kim (2014), and Shao & Wang (2016).”
• Efficient Quantile Regression Analysis With Missing Observations (2015, unknown): “One can modify the EEs along the lines of Kim and Yu (2011) when the object is to estimate the conditional mean, but their method also depends on parametric specification of the propensity score and the availability of a validation sample.”