Conditional maximum likelihood estimates for INAR(1) processes and their application to modelling epileptic seizure counts

1993 ◽  
pp. 310-330 ◽  
Author(s):  
J. Franke ◽  
T. Seligmann
Keyword(s):  
Maximum Likelihood ◽  
Epileptic Seizure ◽  
Maximum Likelihood Estimates ◽  
Conditional Maximum Likelihood ◽  
Conditional Maximum

THE INFORMATION BOUND OF A DYNAMIC PANEL LOGIT MODEL WITH FIXED EFFECTS

2001 ◽  
Vol 17 (5) ◽  
pp. 913-932 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Fixed Effects ◽  
Dynamic Panel Data ◽  
Consistent Estimator ◽  
Likelihood Estimator ◽  
Dynamic Panel ◽  
Conditional Maximum Likelihood ◽  
Information Bound ◽  
Conditional Maximum

In this paper, I calculate the semiparametric information bound in two dynamic panel data logit models with individual specific effects. In such a model without any other regressors, it is well known that the conditional maximum likelihood estimator yields a √n-consistent estimator. In the case where the model includes strictly exogenous continuous regressors, Honoré and Kyriazidou (2000, Econometrica 68, 839–874) suggest a consistent estimator whose rate of convergence is slower than √n. Information bounds calculated in this paper suggest that the conditional maximum likelihood estimator is not efficient for models without any other regressor and that √n-consistent estimation is infeasible in more general models.

Hidden Neural Networks

1999 ◽  
Vol 11 (2) ◽  
pp. 541-563 ◽  
Author(s):  
Anders Krogh ◽  
Søren Kamaric Riis
Keyword(s):  
Neural Networks ◽  
Maximum Likelihood ◽  
Graphical Model ◽  
Markov Models ◽  
Compact Representation ◽  
Conditional Maximum Likelihood ◽  
The Neural Networks ◽  
Maximum Likelihood Criterion ◽  
Performance Gains ◽  
Conditional Maximum

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.

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