Skip to main content Accessibility help
×
Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-27T03:23:57.785Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 April 2015

Simon N. Wood
Affiliation:
University of Bath
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Core Statistics , pp. 241 - 244
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B., Petran and F., Csaaki (Eds.), International symposium on information theory, Budapest: Akadeemiai Kiadi, pp. 267–281.Google Scholar
Berger, J. O. and L. R., Pericchi (1996). The intrinsic Bayes factor for model selection and prediction. Journal Of The American Statistical Association 91(433), 109–122.CrossRefGoogle Scholar
Casella, G. and R., Berger (1990). Statistical inference. Belmont, CA: Duxbury Press.Google Scholar
Cox, D. R. (1992). Planning of experiments. New York: Wiley Classics Library.
Cox, D. R. and D. V., Hinkley (1974). Theoretical statistics. London: Chapman & Hall.CrossRefGoogle Scholar
Davis, T. A. (2006). Direct methods for sparse linear systems. Philadelphia: SIAM.CrossRefGoogle Scholar
Davison, A. C. (2003). Statistical models. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
De Groot, M. H. and M. J., Schervish (2002). Probability and statistics. Boston: Addison-Wesley.Google Scholar
Fahrmeir, L., T., Kneib, and S., Lang (2004). Penalized structured additive regression for space-time data: a Bayesian perspective. Statistica Sinica 14(3), 731–761.Google Scholar
Friel, N. and A., Pettitt (2008). Marginal likelihood estimation via power posteriors. Journal of the Royal Statistical Society, Series B 70(3), 589–607.CrossRefGoogle Scholar
Gage, J. and P., Tyler (1985). Growth and recruitment of the deep-sea urchin echinus affinis. Marine Biology 90(1), 41–53.CrossRefGoogle Scholar
Gamerman, D. and H., Lopes (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference, Volume 68. Boca Raton, FL: Chapman & Hall CRC.Google Scholar
Gelman, A., J. B., Carlin, H. S., Stern, D. B., Dunson, A., Vehtari, and D. B., Rubin (2013). Bayesian data analysis. Boca Raton, FL: CRC press.Google Scholar
Gentle, J. (2003). Random number generation and Monte Carlo methods (2nd ed.). New York: Springer.Google Scholar
Gill, P. E., W., Murray, and M. H., Wright (1981). Practical optimization. London: Academic Press.Google Scholar
Golub, G. H. and C. F., Van Loan (2013). Matrix computations (4th ed.). Baltimore: Johns Hopkins University Press.Google Scholar
Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4), 711–732.CrossRefGoogle Scholar
Griewank, A. and A., Walther (2008). Evaluating derivatives: principles and techniques ofalgorithmic differentiation. Philadelphia: SIAM.CrossRefGoogle Scholar
Grimmett, G. and D., Stirzaker (2001). Probability and random processes (3rd ed.). Oxford: Oxford University Press.Google Scholar
Gurney, W. S. C. and R. M., Nisbet (1998). Ecological dynamics. Oxford: Oxford University Press.Google Scholar
Hastie, T., R., Tibshirani, and J., Friedman (2001). The Elements of Statistical Learning. New York: Springer.CrossRefGoogle Scholar
Kass, R. and A., Raftery (1995). Bayes factors. Journal of the American Statistical Association 90(430), 773–795.CrossRefGoogle Scholar
Klein, J. and M., Moeschberger (2003). Survival analysis: techniques for censored and truncated data (2nd ed.). New York: Springer.Google Scholar
Marsaglia, G. (2003). Xorshift random number generators. Journal of Statistical Software 8(14), 1–16.Google Scholar
Matsumoto, M. and T., Nishimura (1998). Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation 8, 3–30.Google Scholar
McCullagh, P. and J. A., Nelder (1989). Generalized linear models (2nd ed.). London: Chapman & Hall.CrossRefGoogle Scholar
Neal, R. M. (2003). Slice sampling. Annals of Statistics 31, 705–767.Google Scholar
Nocedal, J. and S., Wright (2006). Numerical optimization (2nd ed.). New York: Springer verlag.Google Scholar
O'Hagan, A. (1995). Fractional Bayes factors for model comparison. Journal of the Royal Statistical Society. Series B (Methodological) 57(1), 99–138.Google Scholar
Pinheiro, J. C. and D. M., Bates (2000). Mixed-effects models in S and S-PLUS. New York: Springer-Verlag.CrossRefGoogle Scholar
Plummer, M., N., Best, K., Cowles, and K., Vines (2006). Coda: convergence diagnosis and output analysis for MCMC. R News 6(1), 7–11.Google Scholar
Press, W., S., Teukolsky, W., Vetterling, and B., Flannery (2007). Numerical recipes (3rd ed.). Cambridge: Cambridge University Press.Google Scholar
R Core Team (2012). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
Ripley, B. D. (1987). Stochastic simulation. New York: Wiley.CrossRefGoogle Scholar
Robert, C. (2007). The Bayesian choice: from decision-theoretic foundations to computational implementation. New York: Springer.Google Scholar
Robert, C. and G., Casella (2009). Introducing Monte Carlo methods with R. New York: Springer.Google Scholar
Roberts, G. O., A., Gelman, and W. R., Gilks (1997). Weak convergence and optimal scaling of random walk metropolis algorithms. The Annals of Applied Probability 7(1), 110–120.Google Scholar
Rue, H., S., Martino, and N., Chopin (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the royal statistical society: Series B 71(2), 319–392.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6(2), 461–464.CrossRefGoogle Scholar
Silvey, S. D. (1970). Statistical inference. London: Chapman & Hall.Google Scholar
Spiegelhalter, D. J., N. G., Best, B. P., Carlin, and A., van der Linde (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B 64(4), 583–639.CrossRefGoogle Scholar
Steele, B. M. (1996). A modified EM algorithm for estimation in generalized mixed models. Biometrics 52(4), 1295–1310.CrossRefGoogle ScholarPubMed
Tierney, L., R., Kass, and J., Kadane (1989). Fully exponential Laplace approximations to expectations and variances of nonpositive functions. Journal of the American Statistical Association 84(407), 710–716.CrossRefGoogle Scholar
Watkins, D. S. (1991). Fundamentals of matrix computation. New York: Wiley.Google Scholar
Wichmann, B. and I., Hill (1982). Efficient and portable pseudo-random number generator. Applied Statistics 31, 188–190.CrossRefGoogle Scholar
Wood, S. N. (2006). Generalized additive models: an introduction with R. Boca Raton, FL: CRC press.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Simon N. Wood, University of Bath
  • Book: Core Statistics
  • Online publication: 05 April 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781107741973.012
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Simon N. Wood, University of Bath
  • Book: Core Statistics
  • Online publication: 05 April 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781107741973.012
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Simon N. Wood, University of Bath
  • Book: Core Statistics
  • Online publication: 05 April 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781107741973.012
Available formats
×