arXiv 2 Mar 2019 · Econometrics · publishedJournal of Econometrics (2020) · 3 citations (OpenAlex)
arXiv:1903.00716 · PDF · DOI · OpenAlex · Extracted main text
The maximum utility estimation proposed by Elliott and Lieli (2013) can be viewed as cost-sensitive binary classification; thus, its in-sample overfitting issue is similar to that of perceptron learning. A utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation. We establish non-asymptotic upper bounds on the difference between the maximal expected utility and the generalized expected utility of the UMPR. Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than common estimators in the binary classification if the conditional probability of the binary outcome is misspecified.
appendix boundary found by appendix_command · 57% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Elliott, G., Lieli, R.P (2013) Predicting binary outcomes | 0.974 | 26 | 6 | 92% |
| 2 | Bartlett, P.L., Boucheron, S., Lugosi, G (2002) Model selection and error estimation | 0.874 | 7 | 2 | 100% |
| 3 | Fromont, M (2007) Model selection by bootstrap penalization for classification | 0.754 | 7 | 3 | 43% |
| 4 | Devroye, L., Györfi, L., Lugosi, G (1996) A Probabilistic Theory of Pattern Recognition | 0.737 | 3 | 3 | 67% |
| 5 | Fung, G.M., Mangasarian, O (2004) A feature selection newton method for support vector machine classification | 0.737 | 3 | 3 | 67% |
| 6 | Anthony, M., Bartlett, P.L (1999) Neural Network Learning: Theoretical Foundations | 0.737 | 3 | 2 | 100% |
| 7 | Koltchinskii, V (2001) Rademacher penalties and structural risk minimization | 0.737 | 3 | 2 | 100% |
| 8 | Konishi, S., Kitagawa, G (2008) Information Criteria and Statistical Modeling | 0.737 | 3 | 2 | 100% |
| 9 | Kosorok, M.R (2008) Introduction to Empirical Processes and Semiparametric Inference | 0.644 | 4 | 1 | 100% |
| 10 | Massart, P (2000) Some applications of concentration inequalities to statistics | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness | 0.511 | 2 | 1 |