Conditional prediction intervals for linear regression

8th International Conference on Machine Learning and Applications, ICMLA 2009

Volumn , Issue , 2009, Pages 131-138

  McCullagh, Peter ^a   Vovk, Vladimir ^b   Nouretdinov, Ilia ^b   Devetyarov, Dmitry ^b   Gammerman, Alex ^b  

^a UNIVERSITY OF CHICAGO   (United States)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONFIDENCE LEVELS; COVERAGE PROBABILITIES; GAUSSIANS; LINEAR REGRESSION MODELS; ONLINE MODES; PREDICTION INTERVAL;

LEARNING SYSTEMS; LINEAR REGRESSION;

FORECASTING;

EID: 77950833131     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2009.115     Document Type: Conference Paper

References (12)

1. P. Bloomfield and W. L. Steiger. Least Absolute Deviations: Theory, Applications, and Algorithms. Birkhäuser, Boston, 1983.
2. M. J. Crowder and D. J. Hand. Analysis of Repeated Measures. Chapman and Hall, London, 1990.
3. P. Diaconis. The Markov chain Monte Carlo revolution. Bulletin (New Series) of the American Mathematical Society, 46:179-205, 2008.
4. R. A. Fisher. Two new properties of mathematical likelihood. Proceedings of the Royal Society A, 144:285-307, 1934.
5. D. J. Hand andM. J. Crowder. Practical Longitudinal Data Analysis. Chapman and Hall, London, 1996.
6. K. L. Lange, R. J. A. Little, and J. M. G. Taylor. Robust statistical modelling using the t distribution. Journal of the American Statistical Association, 84:881-896, 1989.
7. J. F. Lawless. On the estimation of safe life when the underlying life distribution is Weibull. Technometrics, 15:857-865, 1973.
8. S. Morgenthaler. Robust confidence intervals for a location parameter: The configural approach. Journal of the American Statistical Association, 81:518-525, 1986.
9. M. J. Schervish. Theory of Statistics. Springer, New York, 1995.
10. G. A. F. Seber and A. J. Lee. Linear Regression Analysis. Wiley, Hoboken, NJ, second edition, 2003.
11. F. Seillier-Moiseiwitsch. Sequential probability forecasts and the probability integral transform. International Statistical Review, 61:395-408, 1993.
12. V. Vovk, A. Gammerman, and G. Shafer. Algorithmic Learning in a Random World. Springer, New York, 2005.