Partial identification in the statistical matching problem
| Autor: | Geoffrey J. McLachlan, Daniel Ahfock, Sharon X. Lee, Saumyadipta Pyne |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Mathematical optimization Matching (statistics) Missing data 01 natural sciences Article Set (abstract data type) 010104 statistics & probability symbols.namesake 0502 economics and business Statistical inference 0101 mathematics 050205 econometrics Mathematics Covariance matrix Applied Mathematics 05 social sciences Feasible region Statistical model Statistical matching Computational Mathematics Computational Theory and Mathematics symbols Data integration Positive-definite matrix completion Gibbs sampling |
| Zdroj: | Computational statistics & data analysis |
| ISSN: | 1872-7352 0167-9473 |
| Popis: | The statistical matching problem involves the integration of multiple datasets where some variables are not observed jointly. This missing data pattern leaves most statistical models unidentifiable. Statistical inference is still possible when operating under the framework of partially identified models, where the goal is to bound the parameters rather than to estimate them precisely. In many matching problems, developing feasible bounds on the parameters is equivalent to finding the set of positive-definite completions of a partially specified covariance matrix. Existing methods for characterising the set of possible completions do not extend to high-dimensional problems. A Gibbs sampler to draw from the set of possible completions is proposed. The variation in the observed samples gives an estimate of the feasible region of the parameters. The Gibbs sampler extends easily to high-dimensional statistical matching problems. |
| Databáze: | OpenAIRE |
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