-
1
-
-
84916537550
-
Bayesian analysis of binary and polychotomous response data
-
MR1224394
-
ALBERT, J. H. and CHIB, S. (1993). Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88 669-679. MR1224394
-
(1993)
J. Amer. Statist. Assoc
, vol.88
, pp. 669-679
-
-
Albert, J.H.1
Chib, S.2
-
2
-
-
33947366371
-
Smoothly mixing regressions
-
MR2380699
-
GEWEKE, J. and KEANE, M. (2007). Smoothly mixing regressions. J. Econometrics 138 252-290. MR2380699
-
(2007)
J. Econometrics
, vol.138
, pp. 252-290
-
-
Geweke, J.1
Keane, M.2
-
4
-
-
33845348251
-
Conditional choice probabilities and the estimation of dynamic models
-
MR1236835
-
HOTZ, J. and MILLER, R. (1993). Conditional choice probabilities and the estimation of dynamic models. Rev. Econom. Stud. 60 497-530. MR1236835
-
(1993)
Rev. Econom. Stud
, vol.60
, pp. 497-530
-
-
Hotz, J.1
Miller, R.2
-
5
-
-
0001940458
-
Adaptive mixtures of local experts
-
Available at
-
JACOBS, R. A., JORDAN, M. I., NOWLAN, S. J. and HINTON, G. E. (1991). Adaptive mixtures of local experts. Neural Comput. 3 79-87. Available at http://dx.doi.org/10.1162/neco.1991.3.1.79.
-
(1991)
Neural Comput
, vol.3
, pp. 79-87
-
-
Jacobs, R.A.1
Jordan, M.I.2
Nowlan, S.J.3
Hinton, G.E.4
-
6
-
-
0027273789
-
Maximum likelihood in a generalized linear finite mixture model by using the em algorithm
-
JANSEN, R. C. (1993). Maximum likelihood in a generalized linear finite mixture model by using the em algorithm. Biometrics 49 227-231.
-
(1993)
Biometrics
, vol.49
, pp. 227-231
-
-
Jansen, R.C.1
-
7
-
-
0033248628
-
Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation
-
MR1724038
-
JIANG, W. and TANNER, M. (1999). Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation. Ann. Statist. 27 987-1011. MR1724038
-
(1999)
Ann. Statist
, vol.27
, pp. 987-1011
-
-
Jiang, W.1
Tanner, M.2
-
8
-
-
84985569561
-
Fitting finite mixture models in a regression context
-
JONES, P. and MCLACHLAN, G. J. (1992). Fitting finite mixture models in a regression context. Aust. N. Z. J. Stat. 34 233-240.
-
(1992)
Aust. N. Z. J. Stat
, vol.34
, pp. 233-240
-
-
Jones, P.1
Mclachlan, G.J.2
-
9
-
-
0029617280
-
Convergence results for the em approach to mixtures of experts architectures
-
JORDAN, M. and XU, L. (1995). Convergence results for the em approach to mixtures of experts architectures. Neural Networks 8 1409-1431.
-
(1995)
Neural Networks
, vol.8
, pp. 1409-1431
-
-
Jordan, M.1
Xu, L.2
-
10
-
-
0000262562
-
Hierarchical mixtures of experts and the EM algorithm
-
JORDAN, M. I. and JACOBS, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Comput. 6 181-214.
-
(1994)
Neural Comput
, vol.6
, pp. 181-214
-
-
Jordan, M.I.1
Jacobs, R.A.2
-
11
-
-
0001213803
-
Discrete parameter variation: Efficient estimation of a switching regression model
-
MR0483200
-
KIEFER, N. M. (1978). Discrete parameter variation: Efficient estimation of a switching regression model. Econometrica 46 427-434. MR0483200
-
(1978)
Econometrica
, vol.46
, pp. 427-434
-
-
Kiefer, N.M.1
-
12
-
-
84898948162
-
Mixture density estimation. In
-
MIT Press, Cambridge, MA
-
LI, J. Q. andBARRON, A. R. (1999).Mixture density estimation. In Advances in Neural Information Processing Systems 12 279-285. MIT Press, Cambridge, MA.
-
(1999)
Advances in Neural Information Processing Systems
, vol.12
, pp. 279-285
-
-
Li, J.Q.1
Barron, A.R.2
-
13
-
-
0032166654
-
Approximation bounds for smooth functions in c(rd) by neural and mixture networks
-
MAIOROV, V. andMEIR, R. (1998). Approximation bounds for smooth functions in c(rd) by neural and mixture networks. Neural Networks, IEEE Transactions 9 969-978.
-
(1998)
Neural Networks, IEEE Transactions
, vol.9
, pp. 969-978
-
-
Maiorov, V.1
Andmeir, R.2
-
15
-
-
84860591034
-
Bayesian modeling of joint and conditional distributions
-
Princeton Univ
-
NORETS, A. and PELENIS, J. (2009). Bayesian modeling of joint and conditional distributions. Unpublished manuscript, Princeton Univ.
-
(2009)
Unpublished manuscript
-
-
Norets, A.1
Pelenis, J.2
-
16
-
-
0030327271
-
Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition
-
PENG, F., JACOBS, R. A. and TANNER, M. A. (1996). Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition. J. Amer. Statist. Assoc. 91 953-960.
-
(1996)
J. Amer. Statist. Assoc
, vol.91
, pp. 953-960
-
-
Peng, F.1
Jacobs, R.A.2
Tanner, M.A.3
-
17
-
-
84950321853
-
Estimating mixtures of normal distributions and switching regressions
-
MR0521324
-
QUANDT, R. E. and RAMSEY, J. B. (1978). Estimating mixtures of normal distributions and switching regressions. J. Amer. Statist. Assoc. 73 730-738. MR0521324
-
(1978)
J. Amer. Statist. Assoc
, vol.73
, pp. 730-738
-
-
Quandt, R.E.1
Ramsey, J.B.2
-
18
-
-
21744460005
-
Practical bayesian density estimation using mixtures of normals
-
MR1482121
-
ROEDER, K. andWASSERMAN, L. (1997). Practical bayesian density estimation using mixtures of normals. J. Amer. Statist. Assoc. 92 894-902. MR1482121
-
(1997)
J. Amer. Statist. Assoc
, vol.92
, pp. 894-902
-
-
Roeder, K.1
Andwasserman, L.2
-
19
-
-
70350192140
-
Numerical dynamic programming in economics
-
(H. Amman, D. Kendrick and J. Rust, eds.). North-Holland, Amsterdam. Available at MR1416619
-
RUST, J. (1996). Numerical dynamic programming in economics. In Handbook of Computational Economics (H. Amman, D. Kendrick and J. Rust, eds.). North-Holland, Amsterdam. Available at http://gemini.econ.umd.edu/jrust/sdp/ndp. pdf. MR1416619
-
(1996)
Handbook of Computational Economics
-
-
Rust, J.1
-
20
-
-
84950758368
-
The calculation of posterior distributions by data augmentation
-
MR0898357
-
TANNER, M. A. andWONG, W. H. (1987). The calculation of posterior distributions by data augmentation. J. Amer. Statist. Assoc. 82 528-540. MR0898357
-
(1987)
J. Amer. Statist. Assoc
, vol.82
, pp. 528-540
-
-
Tanner, M.A.1
Wong, W.H.2
-
21
-
-
70349427041
-
Regression density estimation using smooth adaptive Gaussian mixtures
-
VILLANI, M., KOHN, R. and GIORDANI, P. (2009). Regression density estimation using smooth adaptive Gaussian mixtures. J. Econometrics 153 155-173.
-
(2009)
J. Econometrics
, vol.153
, pp. 155-173
-
-
Villani, M.1
Kohn, R.2
Giordani, P.3
-
22
-
-
34249753047
-
A mixture likelihood approach for generalized linear models
-
WEDEL, M. and DESARBO, W. (1995). A mixture likelihood approach for generalized linear models. J. Classification 12 21-55.
-
(1995)
J. Classification
, vol.12
, pp. 21-55
-
-
Wedel, M.1
Desarbo, W.2
-
23
-
-
13844262338
-
Bayesian mixture of splines for spatially adaptive nonparametric regression
-
MR1929159
-
WOOD, S., JIANG, W. and TANNER, M. (2002). Bayesian mixture of splines for spatially adaptive nonparametric regression. Biometrika 89 513-528. MR1929159
-
(2002)
Biometrika
, vol.89
, pp. 513-528
-
-
Wood, S.1
Jiang, W.2
Tanner, M.3
-
24
-
-
0032072087
-
Error bounds for functional approximation and estimation using mixtures of experts
-
MR1616675
-
ZEEVI, A., MEIR, R. and MAIOROV, V. (1998). Error bounds for functional approximation and estimation using mixtures of experts. IEEE Trans. Inform. Theory 44 1010-1025. MR1616675
-
(1998)
IEEE Trans. Inform. Theory
, vol.44
, pp. 1010-1025
-
-
Zeevi, A.1
Meir, R.2
Maiorov, V.3
-
25
-
-
0031037819
-
Density estimation through convex combinations of densities: Approximation and estimation bounds
-
ZEEVI, A. J. and MEIR, R. (1997). Density estimation through convex combinations of densities: Approximation and estimation bounds. Neural Networks 10 99-109.
-
(1997)
Neural Networks
, vol.10
, pp. 99-109
-
-
Zeevi, A.J.1
Meir, R.2
|