A characteristic function-based approach to approximate maximum likelihood estimation

Luca Trapin, M. Bee, L. Trapin

Risultato della ricerca: Contributo in rivistaArticolo in rivista

1 Citazioni (Scopus)

Abstract

The choice of the summary statistics in approximate maximum likelihood is often a crucial issue. We develop a criterion for choosing the most effective summary statistic and then focus on the empirical characteristic function. In the iid setting, the approximating posterior distribution converges to the approximate distribution of the parameters conditional upon the empirical characteristic function. Simulation experiments suggest that the method is often preferable to numerical maximum likelihood. In a time-series framework, no optimality result can be proved, but the simulations indicate that the method is effective in small samples.
Lingua originaleEnglish
pagine (da-a)3138-3160
Numero di pagine23
RivistaCOMMUNICATIONS IN STATISTICS. THEORY AND METHODS
Volume47
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Characteristic function
  • Intractable likelihood
  • Statistics and Probability
  • Summary statistics
  • κ-Nearest neighbor entropy

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