| Registro completo |
| Provedor de dados: |
AgEcon
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| País: |
United States
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| Título: |
Maximum Empirical Likelihood: Empty Set Problem
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| Autores: |
Grendar, Marian
Judge, George G.
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| Data: |
2009-09-10
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| Ano: |
2009
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| Palavras-chave: |
Statistical theory
Statistics
Mathematical analysis
Mathematical statistic
Research Methods/ Statistical Methods
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| Resumo: |
In the Empirical Estimating Equations (E^3) approach to estimation and inference estimating equations are replaced by their data-dependent empirical counterparts. It is odd but with E^3 there are models where the E^3-based estimator does not exist for some data set, and does exist for others. This depends on whether or not a set of data-supported probability mass functions that satisfy the empirical estimating equations is empty for the data set. In a finite sample context, this unnoted feature invalidates methods of estimation and inference, such as the Maximum Empirical Likelihood, that operate within E^3. The empty set problem of E^3 is illustrated by several examples and possible remedies are discussed.
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| Tipo: |
Working or Discussion Paper
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| Idioma: |
Inglês
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| Identificador: |
http://purl.umn.edu/53402
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| Relação: |
University of California, Berkeley>Department of Agricultural and Resource Economics>CUDARE Working Papers
CUDARE Working Papers
1090
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| Formato: |
10
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