Using structural recursion as query mechanism for data models with references

Author(s):  
Wolfram Clauss

1991 ◽  
Author(s):  
Muruganandan Kumar
Keyword(s):  


Author(s):  
С.И. Рябухин

Процессные модели предметной области широко применяются при проектировании баз данных, а именно в ходе концептуального моделирования данных. Предлагается решение проблемы неоднозначности преобразования процессных доменных моделей типа SADT в концептуальные модели данных. Domain process models are widely used in database design, namely in conceptual data modeling. The solution of the problem of ambiguity of transformation of process domain models of the SADT type into conceptual data models is proposed.



2015 ◽  
Vol 50 (9) ◽  
pp. 127-139
Author(s):  
Paul Downen ◽  
Philip Johnson-Freyd ◽  
Zena M. Ariola
Keyword(s):  




2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.



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