References
- Aït-Sahalia, Y., J. Fan, and D. Xiu. 2010. High-frequency covariance estimates with noisy and asynchronous financial data. Journal of the American Statistical Association 105 (492):1504–17. doi:https://doi.org/10.1198/jasa.2010.tm10163.
- Alon, N., Y. Matias, and M. Szegedy. 1999. The space complexity of approximating the frequency moments. Journal of Computer and System Sciences 58 (1):137–47. doi:https://doi.org/10.1006/jcss.1997.1545.
- Ataharul, I. M., and R. I. Chowdhury. 2017. Analysis of repeated measures data. Singapore: Springer.
- Belsley, D., E. Kuh, and R. Welsch. 1980. Regression diagnostics: Identifying influential data and sources of collinearity. New York: Wiley.
- Bergman, P. 2018. The number of repeated observations needed to estimate the habitual physical activity of an individual to a given level of precision. PLoS One 13 (2):e0192117. doi:https://doi.org/10.1371/journal.pone.0192117.
- Brockwell, P. J., and R. A. Davis. 2016. Introduction to time series and forecasting. 3rd ed. Springer International Publishing.
- Bubeck, S., N. Cesa-Bianchi, and G. Lugosi. 2013. Bandits with heavy tails. IEEE Transactions on Information Theory 59 (11):7711–7. doi:https://doi.org/10.1109/TIT.2013.2277869.
- Chen, K., and H. Müller. 2012. Modeling repeated functional observations. Journal of the American Statistical Association 107 (500):1599–609. doi:https://doi.org/10.1080/01621459.2012.734196.
- Cochrane, D., and G. H. Orcutt. 1949. Application of least squares regressions to relationships containing autocorrelated error terms. Journal of the American Statistical Association 44:32–61. doi:https://doi.org/10.1080/01621459.1949.10483290.
- Cristina, B., and Z. Rania. 2016. Sharp minimax tests for large Toeplitz covariance matrices with repeated observations. Journal of Multivariate Analysis 146:164–76.
- Galton, F. 1894. Natural inheritance. 5th ed. New York: Macmillan.
- Goldstein, B., G. Pomann, W. Winkelmayer, and M. Pencina. 2017. A comparison of risk prediction methods using repeated observations: An application to electronic health records for hemodialysis. Statistics in Medicine 36 (17):2750–63. doi:https://doi.org/10.1002/sim.7308.
- Harrison, D., and D. Rubinfeld. 1978. Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management 5 (1):81–102. doi:https://doi.org/10.1016/0095-0696(78)90006-2.
- Kurata, H. 2010. A theorem on the covariance matrix of a generalized least squares estimator under an elliptically symmetric error. Statistical Papers 51 (2):389–95. doi:https://doi.org/10.1007/s00362-009-0199-7.
- Lai, T. L., H. Robbins, and C. Z. Wei. 1978. Strong consistency of least squares estimates in multiple regression. Proceedings of the National Academy of Sciences of the United States of America 75 (7):3034–6. doi:https://doi.org/10.1073/pnas.75.7.3034.
- Lai, Y. H., P. Thompson, and L. A. Chen. 2004. Generalized and pseudo-generalized trimmed means for the linear regression with AR(1) error model. Statistics & Probability Letters 67 (3):203–11. doi:https://doi.org/10.1016/j.spl.2003.08.003.
- Lugosi, G., and S. Mendelson. 2019. Sub-Gaussian estimators of the mean of a random vector. Annals of Statistics 47 (2):83–794. doi:https://doi.org/10.1214/17-AOS1639.
- Sorlie, T., R. Tibshirani, J. Parker, T. Hastie, J. S. Marron, A. Nobel, S. Deng, H. Johnsen, R. Pesich, S. Geisler, et al. 2003. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proceedings of the National Academy of Sciences of the United States of America 100 (14):8418–23. doi:https://doi.org/10.1073/pnas.0932692100.
- Walker, G. 1931. On periodicity in series of related terms. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 131 (818):518–32. doi:https://doi.org/10.1098/rspa.1931.0069.
- Wang, H. S., and Y. C. Xia. 2009. Shrinkage estimation of the varying coefficient model. Journal of the American Statistical Association 104 (486):747–57. doi:https://doi.org/10.1198/jasa.2009.0138.
- Wang, X., Y. Jiang, M. Huang, and H. Zhang. 2013. Robust variable selection with exponential squared loss. Journal of the American Statistical Association 108 (502):632–43. doi:https://doi.org/10.1080/01621459.2013.766613.
- Yule, G. U. 1927. On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London. Series A. Mathematical, Physical and Engineering Sciences 226 (636–646):267–98. doi:https://doi.org/10.1098/rsta.1927.0007.