Downloads
CWP131919.pdf
PDF | 355.02 KB
Efron's elegant approach to g-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.
Authors
UCL
Jiaying Gu
Working Paper details
- DOI
- 10.1920/wp.cem.2019.1319
- Publisher
- The IFS
Suggested citation
Gu, J and Koenker, R. (2019). Minimalist G-modelling: A comment on Efron. London: The IFS. Available at: https://ifs.org.uk/publications/minimalist-g-modelling-comment-efron (accessed: 15 May 2024).
More from IFS
Understand this issue
Gender norms, violence and adolescent girls’ trajectories: Evidence from India
24 October 2022
Council funding is a numbers game in which everybody is losing
13 May 2024
Empty defence spending promises are a shot in the dark
29 April 2024
Policy analysis
IFS Deputy Director Carl Emmerson appointed to the UK Statistics Authority Methodological Assurance Review Panel
14 April 2023
ABC of SV: Limited Information Likelihood Inference in Stochastic Volatility Jump-Diffusion Models
We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.
12 August 2014
Is there really an NHS productivity crisis?
17 November 2023
Academic research
Understanding Society: minimising selection biases in data collection using mobile apps
2 February 2024
Sample composition and representativeness on Understanding Society
2 February 2024
Robust analysis of short panels
8 January 2024