Identification and estimation of statistical functionals using incomplete data
By Joel L. Horowitz, Charles F. Manski. 2006
58 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7304435ed412563081bb73a13892b3944c360500
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Masashi Sugiyama
- Anton Schwaighofer
- Andrés Santos
- Patrick M. Kline
- Tilmann Gneiting
- Neil D. Lawrence
- Joaquin Quionero-Candela
- Charles F. Manski
In Statistics and Probability
- Andrés Santos
- Patrick M. Kline
- Masashi Sugiyama
- Tilmann Gneiting
- Neil D. Lawrence
- Anton Schwaighofer
- Joaquin Quionero-Candela
- Charles F. Manski
Citations in the corpus
- Dataset Shift in Machine Learning (2009, methodology): “A similar approach with the goal of identifying the possible range of probability measures responsible for the training data without making any prior assumptions on the sampling process has been studied by Manski and Horowitz [Manski, 1989; Horowitz and Manski, 2006].”
- Partial Identification in Econometrics (2010, background): “Horowitz and Manski (1998; 2000) and Zaffalon (2002) study nonparametric prediction when nothing is known about the missingness process; Horowitz et al. (2003) and Horowitz and Manski (2005) consider the computationally challenging problem of parametric prediction.”
- Estimation of Treatment Effects from Combined Data: Identification versus Data Security (2015, unknown): “Manski (2003), Horowitz, Manski, Ponomareva, and Stoye (2003), Horowitz and Manski (2006), Magnac and Maurin (2008) have understood that many data modifications such as top-coding suppression of attributes and stratification lead to the loss of point identification of parameters of interest.”
- Making and Evaluating Point Forecasts (2009, background): “Technically, our discussion connects to M-estimation (Huber 1964; Huber and Ronchetti 2009) and quasi-likelihood (Wedderburn 1974; Dawid and Lauritzen 2006).”
- Sensitivity to Missing Data Assumptions: Theory and an Evaluation of the U.S. Wage Structure (2010, methodology): “Following Horowitz and Manski (2006), Stoye (2007), and Ponomareva and Tamer (2009), we extend this approach and develop methods for conducting inference on potentially misspecified parametric models under partial identification.”