Summary Nan Laird has an enormous and growing impact on computational statistics. Her paper with Dempster and Rubin on the expectation‐maximisation (EM) algorithm is the second most cited paper in statistics. Her papers and book on longitudinal modelling are nearly as impressive. In this brief survey, we revisit the derivation of some of her most useful algorithms from the perspective of the minorisation‐maximisation (MM) principle. The MM principle generalises the EM principle and frees it from the shackles of missing data and conditional expectations. Instead, the focus shifts to the construction of surrogate functions via standard mathematical inequalities. The MM principle can deliver a classical EM algorithm with less fuss or an entirely new algorithm with a faster rate of convergence. In any case, the MM principle enriches our understanding of the EM principle and suggests new algorithms of considerable potential in high‐dimensional settings where standard algorithms such as Newton's method and Fisher scoring falter.
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This content will become publicly available on December 1, 2026
Introduction to the Mathematical Legacy of Hel Braun
Abstract Hel Braun was an extraordinary mathematician. Chances are, however, that you are unfamiliar with her. A serendipitous encounter with archival documents set the author on a path to learning about Braun, her mathematical contributions, and surprising factors that helped shape her legacy and our understanding of mathematical knowledge more broadly.
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- Award ID(s):
- 2302011
- PAR ID:
- 10653689
- Publisher / Repository:
- Mitteilungen der Deutschen Mathematiker-Vereinigung
- Date Published:
- Journal Name:
- Mitteilungen der Deutschen Mathematiker-Vereinigung
- Volume:
- 33
- Issue:
- 4
- ISSN:
- 0947-4471
- Page Range / eLocation ID:
- 268 to 275
- Subject(s) / Keyword(s):
- Hel Braun Helene Braun
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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