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On fast computation of the non‐parametric maximum likelihood estimate of a mixing distribution
Author(s) -
Wang Yong
Publication year - 2007
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2007.00583.x
Subject(s) - mixing (physics) , computation , parametric statistics , quadratic growth , convergence (economics) , algorithm , mathematics , set (abstract data type) , mathematical optimization , computer science , distribution (mathematics) , statistics , mathematical analysis , physics , quantum mechanics , economics , programming language , economic growth
Summary. A fast algorithm for computing the non‐parametric maximum likelihood estimate of a mixing distribution is presented. At each iteration, the algorithm adds new important points to the support set as guided by the gradient function, updates all mixing proportions via a quadratically convergent method and discards redundant support points straightaway. With its convergence being theoretically established, numerical studies show that it is very fast and stable, compared with several other algorithms that are available in the literature.