Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2 results

  • Optimal Online Selection of an Alternating Subsequence: A Central Limit Theorem

  • Part of
  • Alessandro Arlotto, J. Michael Steele
  • Journal:
    Advances in Applied Probability / Volume 46 / Issue 2 / June 2014
    Published online by Cambridge University Press:
    22 February 2016, pp. 536-559
    Print publication:
    June 2014
    • Article
      • You have access
    • PDF
    • Export citation

  • We analyze the optimal policy for the sequential selection of an alternating subsequence from a sequence of n independent observations from a continuous distribution F, and we prove a central limit theorem for the number of selections made by that policy. The proof exploits the backward recursion of dynamic programming and assembles a detailed understanding of the associated value functions and selection rules.

  • Online Selection of Alternating Subsequences from a Random Sample

  • Part of
  • Alessandro Arlotto, Robert W. Chen, Lawrence A. Shepp, J. Michael Steele
  • Journal:
    Journal of Applied Probability / Volume 48 / Issue 4 / December 2011
    Published online by Cambridge University Press:
    14 July 2016, pp. 1114-1132
    Print publication:
    December 2011
    • Article
      • You have access
    • PDF
    • Export citation

  • We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.