Computational Statistics 2

This page contains the material for the course of Computational Statistics 2 (first part) for the PhD in Economics, Statistics and Data Science at Università degli studi Milano Bicocca

Course Syllabus

Slides

• Introduction and Model Validation

• Jacknife

• Bootstrap

• Bootstrap confidence interval and tests

• EM

Lab

• Model Validation

• Monte Carlo

• Jacknife

• Bootstrap

• Bootstrap confidence intervals

• Bootstrap confidence intervals for regression

• Bootstrap tests

• TestScore data

Exam

Students must prepare a presentation (about 15 min) on one of the papers on the following list. After choosing one paper, please send me an email at email to notify the paper you chose. If more than one student chose the same paper, I will assign it to the first one sending the mail, and I will ask the others to choose again. If you want to propose a paper that is not on the list, write me an email.

Bootstrap - classical papers

• Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.
• Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, 1(1), 54–75.
• DiCiccio, T. J., & Efron, B. (1996). Bootstrap confidence intervals. Statistical Science, 11(3), 189–228.
• Hall, P. (1988). Theoretical comparison of bootstrap confidence intervals. The Annals of Statistics, 16(3), 927–953.
• Efron, B. (1994). Missing data, imputation, and the bootstrap. Journal of the American Statistical Association, 89(426), 463–475.

EM Algorithm — classical papers

• Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.
• Moon, T. K. (1996). The expectation-maximization algorithm. IEEE Signal Processing Magazine, 13(6), 47–60.

Bootstrap in Econometrics

• Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics, 90(3), 414–427.
• Gonçalves, S., & White, H. (2004). Maximum likelihood and the bootstrap for nonlinear dynamic models. Journal of Econometrics, 119(1), 199–219.
• Horowitz, J. L. (2019). Bootstrap methods in econometrics. Annual Review of Economics, 11, 193–224.

Bootstrap — Complex and High-Dimensional Data

• Efron, B. (2014). Estimation and accuracy after model selection. Journal of the American Statistical Association, 109(507), 991–1007.
• Lopes, M. E., Blandino, A, Aue, A. (2019). Bootstrapping spectral statistics in high dimensions. Biometrika, 106(4), 781–801.

EM in Econometrics

• Arcidiacono, P., & Jones, J. B. (2003). Finite mixture distributions, sequential likelihood and the EM algorithm. Econometrica, 71(3), 933–946.
• Bartolucci, F., & Nigro, V. (2010). A dynamic model for binary panel data with unobserved heterogeneity admitting a root-n consistent conditional estimator. Econometrica, 78(2), 719–733.
• Karlis, D., & Xekalaki, E. (2003). Choosing initial values for the EM algorithm for finite mixtures. Computational Statistics & Data Analysis, 41(3–4), 577–590.

EM — Complex and High-Dimensional Data

• Städler, N., Bühlmann, P., & Van De Geer, S. (2010). l1-penalization for mixture regression models. Test, 19(2), 209–256.
• Wei, G. C., & Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. Journal of the American Statistical Association, 85(411), 699–704.
• Salibian-Barrera, M., & Zamar, R. H. (2002). Bootstrapping robust estimates of regression. The Annals of Statistics, 30(2), 556–582.