References
- Chung CF, Agterberg FP. Regression models for estimating mineral resources from geological map data. Math Geol. 1980;12:473–488.
- Barnes S, Hamrock E, Toerper M, et al. Real-time prediction of inpatient length of stay for discharge prioritization. J Am Med Inform Assoc. 2016;23:2–10.
- Breiman L, Friedman J, Stone CJ, et al. Classification and regression trees. 1st ed. Belmont (CA): Wadsworth; 1984.
- Breiman L. Bagging predictors. Mach Learn. 1996;24:123–140.
- Breiman L. Random forests. Mach Learn. 2001;45:5–32.
- Bühlmann P. Bagging, subagging and bragging for improving some prediction algorithms. In: Proceedings of Seminar for Statistic. Zurich: Eidgenössische Technische Hochschule (ETH); 2003.
- Zhou Y, Gallins P. A review and tutorial of machine learning methods for microbiome host trait prediction. Front Genet. 2019;10:579.
- Quenouille MH. Approximate tests of correlation in time series. J R Stat Soc Ser B. 1949;11:18–84.
- Quenouille MH. Notes on bias in estimation. Biometrika. 1956;43:353–360.
- Tukey JW. Bias and confidence in not-quite large samples (abstract). Ann Math Statist. 1958;29:614.
- Miller RG. The jackknife–a review. Biometrika. 1974;61(1):1–15.
- Jaeckel LA. The infinitesimal jackknife. Bell Telephone Laboratories; 1972. (Technical report).
- Mentch L, Hooker G. Quantifying uncertainty in random forest via confidence intervals and hypothesis tests. J Mach Learn Res. 2016;17:1–41.
- Wager S, Hastie T, Efron B. Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. J Mach Learn Res. 2014;15:1625–1651.
- Zhang H, Zimmerman J, Nettleton D, et al. Random forest prediction intervals. Am Stat. 2020;74:392–406.
- Wang Q, Lindsay BG. Variance estimation of a general U-statistic with application to cross-validation. Stat Sin. 2014;24(3):1117–1141.
- Friedman JH. Multivariate adaptive regression splines. Ann Statist. 1991;19(1):1–67.
- Halmos PR. The theory of unbiased estimation. Ann Math Statist. 1946;17(1):34–43.
- Hoeffding W. A class of statistics with asymptotically normal distribution. Ann Math Statist. 1948;19(3):293–325.
- Lee AJ. U-statistics: theory and practice. New York (NY): Marcel Dekker; 1990.
- Frees EW. Infinite order U-statistics. Scand J Statist. 1989;16(1):29–45.
- Halmos PR. The theory of unbiased estimation. Ann Math Statist. 1946;17(1):34–43.
- Blom G. Some properties of incomplete U-statistics. Biometrika. 1976;63(3):573–580.
- Peng W, Coleman T, Mentch L. Asymptotic normality and rates of convergence for random forests via generalized U-statistics [PhD thesis]. University of Pittsburgh; 2021.
- Efron B. Estimation and accuracy after model selection. J Am Stat Assoc. 2014;109(507):991–1007.
- Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2018;113(523):1228–1242.
- Sexton J, Lakke P. Standard errors for bagged and random forest estimators. Comput Stat Data Anal. 2009;53:801–811.
- Bühlmann P, Yu B. Analyzing bagging. Ann Statist. 2002;30(4):927–961.
- Andonova S, Elissee A, Evgeniou T, et al. A simple algorithm for learning stable machines. In: Proceedings of the 15th European Conference on Artificial Intelligence, Lyon, France; 2002. p. 513–517.
- Liaw A, Wiener M. Classification and regression by randomforest. R News. 2002;2(3):18–22.
- Yeh I, Hsu T. Building real estate valuation models with comparative approach through case-based reasoning. Soft Comput. 2018;65(C):260–271.