Stochastic approximation: Difference between revisions

Revision as of 21:51, 9 December 2023

Revision as of 04:54, 3 May 2024

Line 133:

stochastic approximation to [[Robust statistics|robust]] [[estimation]].<ref>{{Cite journal | last1 = Martin | first1 = R. | last2 = Masreliez | first2 = C. | doi = 10.1109/TIT.1975.1055386 | title = Robust estimation via stochastic approximation | journal = IEEE Transactions on Information Theory | volume = 21 | issue = 3 | pages = 263 | year = 1975 }}</ref>

The main tool for analyzing stochastic approximations algorithms (including the Robbins–Monro and the Kiefer–Wolfowitz algorithms) is a theorem by [[Aryeh Dvoretzky]] published in the proceedings of the third Berkeley symposium on mathematical statistics and probability, 1956.<ref>{{Citecite journal|last=Dvoretzky|first=Aryeh|date=1956-01-01|title=On Stochastic Approximation|url=http://projecteuclid.org/euclid.bsmsp/1200501645|language=EN|publisher=The Regents of the University of California}}</ref>conference

| last = Dvoretzky | first = Aryeh | author-link = Aryeh Dvoretzky

| editor-last = Neyman | editor-first = Jerzy | editor-link = Jerzy Neyman

| contribution = On stochastic approximation

| contribution-url = https://projecteuclid.org/euclid.bsmsp/1200501645

| mr = 84911

| pages = 39–55

| publisher = University of California Press

| title = Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1954–1955, vol. I

| year = 1956}}</ref>

==See also==