On maximum likelihood estimation for count data models

1990 ◽  
Vol 9 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Werner Hürlimann
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Count Data ◽  
Likelihood Estimation ◽  
Data Models ◽  
Count Data Models

Prevalence data—models, likelihood, and binomial regression

2020 ◽  
pp. 47-63
Author(s):  
Bendix Carstensen
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Statistical Model ◽  
Likelihood Estimation ◽  
Data Models ◽  
Epidemiological Methods ◽  
Prevalence Data ◽  
Binomial Regression ◽  
Disease Condition ◽  
Age And Sex

This chapter examines prevalence data, using a dataset which contains the number of diabetes patients and the total number of persons in Denmark as of January 1, 2010, classified by age and sex. Prevalence of a disease condition in a population is merely the proportion of affected people. The chapter uses prevalence to illustrate core modelling concepts: the model itself, the likelihood, the maximum likelihood estimation principle, and the properties of the results, all of which underlies most modern epidemiological methods. It also explains the concept of a statistical model leading to the distinction between empirical and theoretical prevalences. The chapter then focuses on the task of comparing different models for the same data, models that describe data in various degrees of detail.

scholarly journals Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models

2016 ◽  
Vol 16 (4) ◽  
pp. 1013-1038 ◽  
Author(s):  
Sebastian Kripfganz
Keyword(s):  
Maximum Likelihood ◽  
Panel Data ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Linear Dynamic ◽  
Quasi Maximum Likelihood

In this article, I describe the xtdpdqml command for the quasi–maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Based on the theoretical groundwork by Bhargava and Sargan (1983, Econometrica 51: 1635–1659) and Hsiao, Pesaran, and Tahmiscioglu (2002, Journal of Econometrics 109: 107–150), the marginal distribution of the initial observations is modeled as a function of the observed variables to circumvent a short- T dynamic panel-data bias. Both random-effects and fixed-effects versions are available.

A comparison of salmon escapement estimates using a hierarchical Bayesian approach versus separate maximum likelihood estimation of each year's return

2001 ◽  
Vol 58 (8) ◽  
pp. 1663-1671 ◽  
Author(s):  
Milo D Adkison ◽  
Zhenming Su
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Bayesian Approach ◽  
Count Data ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Hierarchical Bayesian ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical ◽  
The Bayesian Approach

In this simulation study, we compared the performance of a hierarchical Bayesian approach for estimating salmon escapement from count data with that of separate maximum likelihood estimation of each year's escapement. We simulated several contrasting counting schedules resulting in data sets that differed in information content. In particular, we were interested in the ability of the Bayesian approach to estimate escapement and timing in years where few or no counts are made after the peak of escapement. We found that the Bayesian hierarchical approach was much better able to estimate escapement and escapement timing in these situations. Separate estimates for such years could be wildly inaccurate. However, even a single postpeak count could dramatically improve the estimability of escapement parameters.

A generalization of Poisson-Sujatha distribution and its applications to ecology

2019 ◽  
Vol 12 (02) ◽  
pp. 1950013
Author(s):  
Rama Shanker ◽  
Kamlesh Kumar Shukla
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Poisson Distribution ◽  
Count Data ◽  
Coefficient Of Variation ◽  
Goodness Of Fit ◽  
Likelihood Estimation ◽  
Lindley Distribution ◽  
Index Of Dispersion

A generalization of Poisson Sujatha distribution (AGPSD), which includes Poisson-Lindley distribution (PLD) and Poisson-Sujatha distribution (PSD) as particular cases, has been proposed and studied. Its moments and moments-based measures including coefficient of variation, skewness, kurtosis and index of dispersion have been obtained and their behaviors have been discussed. The estimation of its parameters has been discussed with maximum likelihood estimation. The applications of the proposed distribution has been explained through two examples of count data from ecology and the goodness of fit of the distribution has been compared with Poisson distribution, PLD and PSD.

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