Bibliography

This section provides a curated list of a few landmark papers and books on statistical modelling, covering topics in the current set of notes, and beyond (extra topics include missing data, EM algorithms, mixture models, learning under sparsity, causal inference, graphical models). Of course, given the breadth of the field and the speed at which it it developing, any list like the one below can hardly provide fair coverage, and will be missing many old and recent core developments. Its purpose it to be a helpful starting point for engaging more with key ideas and developments in statistical modelling.

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Czáki (Eds.), Second international symposium on information theory (pp. 267–281). Akademiai Kiadó. https://doi.org/10.1007/978-1-4612-0919-5_38

Albert, J. H. (2007). Bayesian computation with r. Springer-Verlag. https://doi.org/10.1007/978-0-387-92298-0

Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669–679. https://doi.org/10.2307/2290350

Best, N., & Thomas, A. (2000). Bayesian graphical models and software for GLMs. In D. K. Dey, S. K. Ghosh, & B. K. Mallick (Eds.), Generalized linear models: A Bayesian perspective (pp. 387–406). Marcel Dekker. https://doi.org/10.1201/9781482293456

Breslow, N. E., & Clayton, D. G. (1993). Appproximate inference in generalised linear mixed models. Journal of the American Statistical Association, 88, 9–25. https://doi.org/10.2307/2290687

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multi-model inference: A practical information theoretic approach (Second). Springer. https://doi.org/10.1007/978-1-4757-2917-7

Candès, E., & Tao, T. (2007). The Dantzig selector: Statistical estimation when p is much larger than n (with discussion). Annals of Statistics, 35, 2313–2404. https://doi.org/10.1214/009053606000001523

Claeskens, G., & Hjort, N. L. (2008). Model selection and model averaging. Cambridge University Press. https://doi.org/10.1017/CBO9780511790485

Cowell, R. G., Dawid, A. P., Lauritzen, S. L., & Spiegelhelter, D. J. (1999). Probabilistic networks and expert systems. Springer-Verlag. https://doi.org/10.1007/b97670

Crowder, M. J., & Hand, D. J. (1990). Analysis of repeated measures. Chapman; Hall/CRC. https://doi.org/10.1201/9781315137421

Davison, A. C. (2003). Statistical models. Cambridge University Press. https://doi.org/10.1017/CBO9780511815850

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society Series B, 39, 1–38. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

Diggle, P. J., Heagerty, P., Liang, K.-Y., & Zeger, S. (2002). Analysis of longitudinal data (2nd ed.). Oxford University Press. https://global.oup.com/academic/product/analysis-of-longitudinal-data-9780199676750

Draper, D. (1995). Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society Series B, 57, 45–97. https://doi.org/10.1111/j.2517-6161.1995.tb02015.x

Efron, B. (1975). Defining the curvature of a statistical problem (with applications to second order efficiency). The Annals of Statistics, 3(6), 1189–1242. https://doi.org/10.1214/aos/1176343282

Fahrmeir, L., Kneib, T., Lang, S., & Marx, B. (Eds.). (2013). Regression: Models, methods and applications. Springer. https://doi.org/10.1007/978-3-642-34333-9

Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96, 1348–1360.

Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space (with Discussion). Journal of the Royal Statistical Society Series B, 70, 849–911. https://doi.org/10.1198/016214501753382273

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2004). Bayesian data analysis (3rd ed.). Chapman; Hall/CRC. https://doi.org/10.1201/b16018

Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press. https://doi.org/10.1017/9781139161879

Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov chain monte carlo in practice. Chapman & Hall.

Green, P. J., Hjørt, N. L., & Richardson, S. (Eds.). (2003). Highly structured stochastic systems. Chapman & Hall/CRC. 10.1201/b14835

Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical learning with sparsity: The Lasso and generalizations. Chapman; Hall/CRC. https://doi.org/10.1201/b18401

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial (with discussion). Statistical Science, 14, 382–417. https://doi.org/10.1214/ss/1009212519

Jamshidian, M., & Jennrich, R. I. (1997). Acceleration of the EM algorithm by using quasi-Newton methods. Journal of the Royal Statistical Society Series B, 59, 569–587. https://doi.org/10.1111/1467-9868.00083

Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795. https://doi.org/10.1080/01621459.1995.10476572

Liang, K., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22. https://doi.org/10.1093/biomet/73.1.13

Linhart, H., & Zucchini, W. (1986). Model selection. Wiley. https://doi.org/10.1007/978-3-642-04898-2_373

Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Wiley. https://doi.org/10.1002/9781119013563

Marin, J.-M., & Robert, C. P. (2007). Bayesian core: A practical approach to computational bayesian statistics. Springer-Verlag. https://doi.org/10.1007/978-0-387-38983-7

McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10.1214/aos/1035844977

McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Chapman & Hall. https://doi.org/10.1201/9780203753736

McCulloch, C. E., Searle, S. R., & Neuhaus, J. M. (2008). Generalized, linear, and mixed models (2nd ed.). Wiley. https://doi.org/10.1002/0471722073

McLachlan, G. J., & Krishnan, T. (2008). The EM algorithm and extensions (2nd ed.). Wiley. https://doi.org/10.1002/9780470191613

McQuarrie, A. D. R., & Tsai, C.-L. (1998). Regression and time series model selection. World Scientific. https://doi.org/10.1142/3573

Meng, X.-L., & van Dyk, D. (1997). The EM algorithm — an old folk-song sung to a fast new tune (with discussion). Journal of the Royal Statistical Society Series B, 59, 511–567. https://doi.org/10.1111/1467-9868.00082

Nelder, J. A., Lee, Y., & Pawitan, Y. (2017). Generalized linear models with random effects: A unified approach via h-likelihood (2nd ed.). Chapman; Hall/CRC. https://doi.org/10.1201/9781315119953

Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. https://doi.org/10.2307/2344614

O’Hagan, A., & Forster, J. J. (2004). Kendall’s advanced theory of statistics. Volume 2B: Bayesian inference (Second). Hodder Arnold. https://www.wiley.com/en-us/Kendall%27s+Advanced+Theory+of+Statistic+2B-p-9780470685693

Oakes, D. (1999). Direct calculation of the information matrix via the EM algorithm. Journal of the Royal Statistical Society Series B, 61, 479–482. https://doi.org/10.1111/1467-9868.00188

Ogden, H. (2021). On the error in laplace approximations of high-dimensional integrals. Stat, 10(1), e380. https://doi.org/10.1002/sta4.380

Ogden, H. E. (2017). On asymptotic validity of naive inference with an approximate likelihood. Biometrika, 104(1), 153–164. https://doi.org/10.1093/biomet/asx002

Peters, J., Janzing, D., & Schlkopf, B. (2017). Elements of causal inference: Foundations and learning algorithms. The MIT Press. https://mitpress.mit.edu/books/elements-causal-inference

Pinheiro, J., & Bates, D. M. (2002). Mixed effects models in S and S-PLUS. New York:Springer-Verlag. https://doi.org/10.1007/b98882

Raftery, A. E., Madigan, D., & Hoeting, J. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92, 179–191. https://doi.org/10.1080/01621459.1997.10473615

Richardson, S., & Green, P. J. (1997). On bayesian analysis of mixtures with an unknown number of components (with discussion). Journal of the Royal Statistical Society Series B, 59, 731–792. https://doi.org/10.1111/1467-9868.00095

Rissanen, J. (1987). Stochastic complexity (with discussion). Journal of the Royal Statistical Society, Series B, 49, 223–239. https://doi.org/10.1111/j.2517-6161.1987.tb01694.x

Schwartz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. https://doi.org/10.1214/aos/1176344136

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Linde, A. van der. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B, 64, 583–639. https://doi.org/10.1111/1467-9868.00353

Tanner, M. A. (1996). Tools for statistical inference: Methods for the exploration of posterior distributions and likelihood functions (Third). Springer. https://doi.org/10.1007/978-1-4612-4024-2

Venables, W., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). Springer-Verlag. https://doi.org/10.1007/978-0-387-21706-2

Wedderburn, R. W. M. (1974). Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss-Newton Method. Biometrika, 61(3), 439. https://doi.org/10.2307/2334725

Wood, S. N. (2017). Generalized additive models: An introduction with r (2nd ed.). Chapman; Hall/CRC. https://doi.org/10.1201/9781315370279