Preferences Entropy Conditional Maximum in the Case of the Buyers’ Optimal Preferences Distribution for the Price Choice

2021 ◽  
Author(s):  
Andriy Goncharenko
Keyword(s):  
Conditional Maximum

scholarly journals A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

scholarly journals Estimating a resource selection function with line transect sampling

2002 ◽  
Vol 6 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Bryan F. J. Manly
Keyword(s):  
Resource Selection ◽  
Probability Function ◽  
Resource Selection Function ◽  
Line Transect ◽  
Selection Function ◽  
Line Transect Sampling ◽  
Selection Probability ◽  
Transect Sampling ◽  
Conditional Maximum ◽  
Habitat Variables

A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice.

Environmental resource collection: implications for children's schooling in Tigray, northern Ethiopia

2013 ◽  
Vol 19 (2) ◽  
pp. 182-200 ◽  
Author(s):  
Bahre Gebru ◽  
Sosina Bezu
Keyword(s):  
Gender Bias ◽  
Likelihood Estimation ◽  
Northern Ethiopia ◽  
Estimation Technique ◽  
Cross Sectional ◽  
Child Schooling ◽  
Cent Increase ◽  
Resource Collection ◽  
Children's Schooling ◽  
Conditional Maximum

AbstractThis paper examines the adverse effect of natural resources scarcity on children's schooling and the possible gender bias of resource collection work against girls' schooling. It uses cross-sectional data on 316 children aged 7–18 years collected from 120 rural households in Tigray, northern Ethiopia. The two-stage conditional maximum likelihood estimation technique is employed to take care of endogeneity between schooling and collection intensity decisions. The results revealed that a 50 per cent increase in collection intensity reduces the likelihood of child schooling by approximately 11 per cent. However, we find no evidence of gender bias against girls' schooling.

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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