REFERENCES
- Diaconis, P., and Holmes, S. (1996), “Are There Still Things to do in Bayesian Statistics?” Erkenntnis, 45, 145–158.
- Gustafson, P. (2015), Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data, Boca Raton, FL: Chapman and Hall/CRC.
- Rossi, P. E., Allenby, G. M., and McCulloch, R. (2005), Bayesian Statistics and Marketing, Chichester, UK: Wiley.
REFERENCES
- Durante, F., Puccetti, G., Scherer, M. (2015), Building Bridges Between Mathematics, Insurance and Finance—An Interview With Paul Embrechts, Dependence Modeling, 3, 17–28.
- Joe, H. (1997), Multivariate Models and Dependence Concepts, London: Chapman & Hall.
REFERENCES
- Bühlmann, P., and van de Geer, S. (2011), Statistics for High-Dimensional Data, Springer Series in Statistics, Heidelberg: Springer.
REFERENCES
- Bishop, C. (2007), Pattern Recognition and Machine Learning, New York: Springer.
- Hastie, T., Tibshirani, R., and Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, New York: Springer.
REFERENCES
- Fan, J., and Yao, Q. (2003), Nonlinear Time Series, New York: Springer-Verlag.
- Fokianos, K. (2012), “12-Count Time Series Models,” in Time Series Analysis: Methods and Applications, Handbook of Statistics (Vol. 30), S. S. R. Tata Subba Rao, and C. Rao, eds., Amsterdam, The Netherlands: North Holland, pp. 315–347.
- Tong, H. (1990), Non-Linear Time Series: A Dynamical Systems Approach, New York: Oxford.
REFERENCES
- Brillinger, D. R. (2001), “Time Series: Data Analysis and Theory,” SIAM Classics in Applied Mathematics.
- Brockwell, P., and Davis, R. (1991), Time Series: Theory and Methods, (2nd ed.), New York: Springer.
- Shumway, R., and Stoffer, D. (2010), Time Series Analysis and Its Applications: With R Examples (3rd ed.), New York: Springer.