Hypothesis testing in mixtures-of-experts of generalized linear time series

IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)

Volumn 2003-January, Issue , 2003, Pages 285-292

  Carvalho, A X ^a   Tanner, M A ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Finance; Gaussian processes; Hidden Markov models; Jacobian matrices; Maximum likelihood estimation; Shape; State estimation; Statistics; Testing; Vectors

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; FINANCE; FINANCIAL DATA PROCESSING; HIDDEN MARKOV MODELS; JACOBIAN MATRICES; MARKOV PROCESSES; MAXIMUM LIKELIHOOD; MIXTURES; STATE ESTIMATION; STATISTICS; TESTING; TIME SERIES; TRELLIS CODES; VECTORS;

FINANCIAL TIME SERIES; GAUSSIAN PROCESSES; GENERALIZED LINEAR MODEL; MAXIMUM LIKELIHOOD ESTIMATOR; MISSPECIFIED MODELS; MIXTURES OF EXPERTS; REGULARITY CONDITION; SHAPE;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 84941334642     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIFER.2003.1196273     Document Type: Conference Paper

References (29)

1. A. Carvalho. Mixtures-of-experts of generalized linear time series. Unpublished Ph.D. Thesis, Department of Statistics, Northwestern University, 2002.
2. A. Carvalho and M. Tanner. Mixtures-of-experts of generalized linear time series: asymptotic normality and model specification. Unpublished Technical Report, Department of Statistics, Northwestern University, 2002.
3. A. Carvalho and M. Tanner. Mixtures-of-experts of generalized linear time series models. Unpublished Technical Report, Department of Statistics, University of British Columbia, 2002.
4. S. Gutta, J. Huang, P. Jonathon, and H. Wechsler. Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Transactions on Neural Networks, 11:948-960,2000.
5. A. Harvey. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, 1994.
6. G. Huerta, W. Jiang, and M. Tanner. Mixtures of time series models. Journal of Computational and Graphical Statistics, 10(1):82-89,2001.
7. R. Jacobs, M. Jordan, S. Nowlan, and G. Hinton. Adaptative mixtures of local experts. Neural Computation, 3:79-87, 1991.
8. N. Jeffries and R. Pfeiffer. A mixture model for the probability distribution of rain rate. Environmetrics, 12:1-10, 2001.
9. W. Jiang and M. Tanner. Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation. Unpublished Technical Report, Department of Statistics, Northwestern University, 1998.
10. W. Jiang and M. Tanner. Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation. The Annals of Statistics, 27(3):987-1011, 1999.
11. W. Jiang and M. Tanner. On the approximation rate of hierarchical mixtures-of-experts for generalized linear models. Neural Computation, 11:1183-1198, 1999.
12. W. Jiang and M. Tanner. On the identifiability of mixtures-of-experts. Neural Networks, 12:1253-1258, 1999.
13. W. Jiang and M. Tanner. On the asymptotic normality of hierarchical mixtures-of-experts for generalized linear models. IEEE Transactions on Information Theory, 46(3):1005-1013, 2000.
14. M. Jordan and R. Jacobs. Hierarchical mixtures-of-experts and the em algorithm. Neural Computation, 6:181-214, 1994.
15. L. Kullback and R. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22:79-86, 1951.
16. R. Kurnik, J. Oliver, S. Waterhouse, T. Dunn, Y. Jayalaksmi, M. Lesho, M. Lopatina, J Tamada, and C. Wei. Application of mixture of experts algorithm for signal processing in a noninvasive glucose monitoring system. Sensors and Actuators B-Chemical, 60:19-26, 1999.
17. W. Li. Time series models based on generalized linear models: Some futher results. Biometrics, 50:506-511, 1994.
18. S Liehr, K. Pawelzik, J. Kohlmorgen, and K. Muller. Hidden markov mixtures of experts with an application to eeg recording from sleep. Theory of Biosciences, 118:246-260, 1999.
19. H. Lin, C. McCulloch, B. Turnbull, E. Slate, and L. Clark. A latent class mixed model for analysing biomarker trajectories with irregularly scheduled observations. Statistics in Medicine, 19:1303-1318, 2000.
20. M. McCullagh and J. Nelder. Generalized Linear Models (Monographs on Statistics and Applied Probability, 37). Chapman and Hall/CRC, 1998.
21. W. Newey and K. West. A simple positive semidefinite, heterokedasticity and autocorrelation consistent covariance matrix. Econometrica, 55:703-708, 1987.
22. W. Peiming and M. Puterman. Mixed logistic regression models. Journal of Agricultural, Biological, and Environmental Statistics, 3:175-200, 1998.
23. E Peng, R. Jacobs, and M. Tanner. Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition. Journal of the American Statistical Association, 91(435):953-960,1996.
24. O. Rosen and M. Tanner. Mixtures of proportional hazard regression models. Statistics in Medicine, 18:1119-1131, 1999.
25. A. Weigend, M. Mangeas, and A. Srivastava. Nonlinear gated experts for time series: Discovering regimes and avoid overfitting. International Journal of Neural Systems, 6:373-399, 1995.
26. H. White. Estimation Inference and Specification Analysis, Econometric Society Monographs. Cambridge University Press, 1996.
27. C. Wong and W. Li. On a logistic mixture autoregressive model. Biometrika, 88(3):833-846, 2001.
28. W. Wong. Theory of partial likelihood. The Annals of Statistics, 14(1): 88-123, 1986.
29. M. Zeevi, R. Meir, and R. Adler. Nonlinear models for time series using mixtures of autorregressive models. Unpublished Technical Report, http://www.isl.stanford.edu/ azeevil, 1999.