References
- Broman, K. W., & Speed, T. P. (2002). A model selection approach for the identification of quantitative trait loci in experiemental crosses. Journal of the Royal Statistical Society, Series B, 64, 641–656. doi: 10.1111/1467-9868.00354
- Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95, 759–771. doi: 10.1093/biomet/asn034
- Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96, 1348–1360. doi: 10.1198/016214501753382273
- Hartley, H. O., & Rao, J. N. K. (1967). Maximum likelihood estimation for the mixed analysis of variance model. Biometrika, 54, 93–108. doi: 10.1093/biomet/54.1-2.93
- Harville, D. A. (1977). maximum likelihood approaches to variance components estimation and related problems. Journal of the American Statistical Association, 72, 320–340. doi: 10.1080/01621459.1977.10480998
- Jiang, J., & Nguyen, T. (2015). The fence methods. Singapore: World Scientific.
- Jiang, J., Rao, J. S., Gu, Z., & Nguyen, T. (2008). Fence methods for mixed model selection. The Annals of Statistics, 36, 1669–1692. doi: 10.1214/07-AOS517
- Miller, J. J. (1977). Asymptotic properties of maximum likelihood estimates in the mixed model of analysis of variance. The Annals of Statistics, 5, 746–762. doi: 10.1214/aos/1176343897
- Müller, S., Scealy, J. L., & Welsh, A. H. (2013). Model selection in linear mixed models. Statistical Science, 28, 135–167. doi: 10.1214/12-STS410
- Ye, J. (1998). On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association, 93, 120–131. doi: 10.1080/01621459.1998.10474094