Fetching the paper…
Reading the bibliography…
Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.
kaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the Second International Symposium on Information Theory
1973
Earlier work this paper cites.
tone, M. (1977). An asymptotic equivalence of choice of model cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society B
1977
Earlier work this paper cites.
eisser, S., and Eddy, W. (1979). A predictive approach to model selection. Journal of the American Statistical Association
1979
Earlier work this paper cites.
ubin, D. B. (1981). Estimation in parallel randomized experiments. Journal of Educational Statistics
1981
Earlier work this paper cites.
urman, P. (1989). A comparative study of ordinary cross-validation, v v -fold cross-validation and the repeated learning-testing methods. Biometrika
1989
Earlier work this paper cites.
elfand, A. E., Dey, D. K., and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods. In Bayesian Statistics 4
1992
Earlier work this paper cites.
ernardo, J., and Smith A. F. M (1994). Bayesian theory
1994
Earlier work this paper cites.
elfand, A. E. (1996). Model determination using sampling-based methods. In Markov Chain Monte Carlo in Practice
1996
Earlier work this paper cites.
eruggia, M. (1997). On the variability of case-deletion importance sampling weights in the Bayesian linear model. Journal of the American Statistical Association
1997
Earlier work this paper cites.
oeting, J., Madigan, D., Raftery, A. E., and Volinsky, C. (1999). Bayesian model averaging. Statistical Science
1999
Earlier work this paper cites.
piegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society B
2002
Earlier work this paper cites.
ehtari, A., and Lampinen, J. (2002). Bayesian model assessment and comparison using cross-validation predictive densities. Neural Computation
2002
Earlier work this paper cites.
piegelhalter, D., Thomas, A., Best, N., Gilks, W., and Lunn, D. (1994, 2003). BUGS: Bayesian inference using Gibbs sampling. MRC Biostatistics Unit, Cambridge, England. http://www.mrc-bsu.cam.ac.uk/bugs/
2003
Cited alongside, same era.
an der Linde, A. (2005). DIC in variable selection. Statistica Neerlandica
2005
Cited alongside, same era.
elman, A., and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models
2007
Cited alongside, same era.
neiting, T., and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association
2007
Cited alongside, same era.
pifani, I., MacEachern, S. N., and Peruggia, M. (2008). Case-deletion importance sampling estimators: Central limit theorems and related results. Electronic Journal of Statistics
2008
ehtari, A., and Ojanen, J. (2012). A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys
2012
Later among the works it cites.
elman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin D. B. (2013). Bayesian Data Analysis
2013
Later among the works it cites.
elman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing
2014
Later among the works it cites.
offman, M. D., and Gelman, A. (2014). The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research
2014
Later among the works it cites.
ehtari, A., and Riihimäki, J. (2014). Laplace approximation for logistic Gaussian process density estimation and regression. Bayesian analysis
2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
onides, E. L. (2008). Truncated importance sampling. Journal of Computational and Graphical Statistics
2008
Cited alongside, same era.
lummer, M. (2008). Penalized loss functions for Bayesian model comparison. Biostatistics
2008
Cited alongside, same era.
oopman, S. J., Shephard, N., and Creal, D. (2009). Testing the assumptions behind importance sampling. Journal of Econometrics
2009
Cited alongside, same era.
hang, J., and Stephens, M. A. (2009). A new and efficient estimation method for the generalized Pareto distribution. Technometrics
2009
Cited alongside, same era.
ndo, T., and Tsay, R. (2010). Predictive likelihood for Bayesian model selection and averaging. International Journal of Forecasting
2010
Cited alongside, same era.
rlot, S., and Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys
2010
Cited alongside, same era.
atanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research
2010
Cited alongside, same era.
Later among the works it cites.
ehtari, A., and Gelman, A. (2015). Pareto smoothed importance sampling. arXiv:1507.02646
2015
Closest in time.
abry, J., and Goodrich, B. (2016). rstanarm: Bayesian applied regression modeling via Stan. R package version 2.10.0. http://mc-stan.org/interfaces/rstanarm
2016
Closest in time.
iironen, J., and Vehtari, A. (2016). Comparison of Bayesian predictive methods for model selection. Statistics and Computing
2016
Closest in time.
Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
2016
Closest in time.
ehtari, A., Gelman, A., and Gabry, J. (2016). loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 0.1.6. https://github.com/stan-dev/loo
2016
Closest in time.
ehtari, A., Mononen, T., Tolvanen, V., Sivula, T., and Winther, O. (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. Journal of Machine Learning Research
2016
Closest in time.