-
1
-
-
0002016642
-
On least squares and linear compbinations of observations
-
Aitken, A. (1935). On least squares and linear compbinations of observations. Proceedings of the Royal Statistical Society, 55:42-48.
-
(1935)
Proceedings of the Royal Statistical Society
, vol.55
, pp. 42-48
-
-
Aitken, A.1
-
4
-
-
0001263851
-
Modelling conditional probability distributions for periodic variables
-
Bishop, C. M. and Nabney, I. T. (1996). Modelling conditional probability distributions for periodic variables. Neural Computation, 8:1123-1133.
-
(1996)
Neural Computation
, vol.8
, pp. 1123-1133
-
-
Bishop, C.M.1
Nabney, I.T.2
-
6
-
-
42449156579
-
Generalized autoregressive conditional heteroskedasticity
-
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31:307-327.
-
(1986)
Journal of Econometrics
, vol.31
, pp. 307-327
-
-
Bollerslev, T.1
-
7
-
-
0038256442
-
The generalized hyperbolic model: Financial derivatives and risk measures
-
Eberlein, E. and Prause, K. (2000). The generalized hyperbolic model: financial derivatives and risk measures. Mathematical Finance.
-
(2000)
Mathematical Finance
-
-
Eberlein, E.1
Prause, K.2
-
8
-
-
0000051984
-
Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation
-
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50(4):987-1007.
-
(1982)
Econometrica
, vol.50
, Issue.4
, pp. 987-1007
-
-
Engle, R.F.1
-
9
-
-
0003907281
-
-
Chapman and Hall, New York
-
Fang, K.-T., Kotz, S., and Ng, K.-W. (1990). Symmetric Multivariate and Related Distributions. Chapman and Hall, New York.
-
(1990)
Symmetric Multivariate and Related Distributions
-
-
Fang, K.-T.1
Kotz, S.2
Ng, K.-W.3
-
11
-
-
0024866495
-
On the approximate realization of continuous mappings by neural networks
-
Funahashi, K. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2(3):183-192.
-
(1989)
Neural Networks
, vol.2
, Issue.3
, pp. 183-192
-
-
Funahashi, K.1
-
13
-
-
0001248728
-
Distribution theory of spherical distributions and a location-scale parameter
-
Kelker, D. (1970). Distribution theory of spherical distributions and a location-scale parameter. Sankhya, A, 32:419-430.
-
(1970)
Sankhya, A
, vol.32
, pp. 419-430
-
-
Kelker, D.1
-
16
-
-
0002245041
-
Estimation of conditional densities: A comparison of neural network approaches
-
Neuneier, R., Hergert, F., Finnhoff, W., and Ormoneit, D. (1994). Estimation of conditional densities: A comparison of neural network approaches. ICANN94-Proceedings of the International Conference on Artificial Neural Networks, pages 689-692.
-
(1994)
ICANN94-proceedings of the International Conference on Artificial Neural Networks
, pp. 689-692
-
-
Neuneier, R.1
Hergert, F.2
Finnhoff, W.3
Ormoneit, D.4
-
20
-
-
0001473437
-
On estimation of probability density function and mode
-
Parzen, E. (1962). On estimation of probability density function and mode. Ann. Math. Stat., 35:1065-1076.
-
(1962)
Ann. Math. Stat.
, vol.35
, pp. 1065-1076
-
-
Parzen, E.1
-
21
-
-
0004030839
-
A theory of networks for approximation and learning
-
MIT Artificial Intelligence Laboratory
-
Poggio, T. and Girosi, F. (1989). A theory of networks for approximation and learning. Technical Report Memo No. 1140, MIT Artificial Intelligence Laboratory.
-
(1989)
Technical Report Memo No. 1140
, vol.1140
-
-
Poggio, T.1
Girosi, F.2
-
23
-
-
0021404166
-
Mixture densities, maximum likelihood and the em algorithm
-
Redner, R. and Walker, H. (1984). Mixture densities, maximum likelihood and the em algorithm. SIAM Review, 26.
-
(1984)
SIAM Review
, pp. 26
-
-
Redner, R.1
Walker, H.2
-
24
-
-
0002896913
-
Stochastic global optimization methods, part i: Clustering methods, part ii: Multi-level methods
-
Rinnooy Kan, A. and Timmer, G. (1987). Stochastic global optimization methods, part i: Clustering methods, part ii: Multi-level methods. Mathematical Programming, 39(1):26-78.
-
(1987)
Mathematical Programming
, vol.39
, Issue.1
, pp. 26-78
-
-
Rinnooy Kan, A.1
Timmer, G.2
-
25
-
-
0003444646
-
-
Rumelhart, D. and McClelland, J., editors. MIT Press, Cambridge
-
Rumelhart, D. and McClelland, J., editors (1986). Parallel Distributed Processing, volume 1. MIT Press, Cambridge.
-
(1986)
Parallel Distributed Processing
, vol.1
-
-
-
26
-
-
0001344388
-
Forecasting time-dependent conditional densities: A neural network approach
-
Schittenkopf, C., Dorffher, G., and Dockner, E. J. (2000). Forecasting time-dependent conditional densities: A neural network approach. Journal of Forecasting, 19(4):355-374.
-
(2000)
Journal of Forecasting
, vol.19
, Issue.4
, pp. 355-374
-
-
Schittenkopf, C.1
Dorffher, G.2
Dockner, E.J.3
-
27
-
-
0003477556
-
-
Arnold, London
-
Stuart, A., Ord, K., and Arnold, S. (1999). Kendall's Advanced Theory of Statistics, Volume II A: Classical Inference and the Linear Model Arnold, London.
-
(1999)
Kendall's Advanced Theory of Statistics, Volume II A: Classical Inference and the Linear Model
, vol.2
-
-
Stuart, A.1
Ord, K.2
Arnold, S.3
-
29
-
-
0003188086
-
Predicting daily probability distributions of s&p500 returns
-
Weigend, A. S. and Shi, S. (2000). Predicting daily probability distributions of s&p500 returns. Journal of Forecasting, 19(4):375-392.
-
(2000)
Journal of Forecasting
, vol.19
, Issue.4
, pp. 375-392
-
-
Weigend, A.S.1
Shi, S.2
-
30
-
-
0030585112
-
Using neuronal networks to model conditional multivariate densities
-
Williams, P. M. (1996a). Using neuronal networks to model conditional multivariate densities. Neural Computation, 8:843-854.
-
(1996)
Neural Computation
, vol.8
, pp. 843-854
-
-
Williams, P.M.1
-
31
-
-
0030585112
-
Using neuronal networks to model conditional multjvariate densities
-
Williams, P. M. (1996b). Using neuronal networks to model conditional multjvariate densities. Neural Computation, 8:843-854.
-
(1996)
Neural Computation
, vol.8
, pp. 843-854
-
-
Williams, P.M.1
|