Jump to ContentJump to Main Navigation
Statistical ThoughtA Perspective and History$
Users without a subscription are not able to see the full content.

Shoutir Kishore Chatterjee

Print publication date: 2003

Print ISBN-13: 9780198525318

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780198525318.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 20 June 2019

PRO-SUBJECTIVE APPROACH LOSES AS SAMPLING THEORY GAINS GROUND

PRO-SUBJECTIVE APPROACH LOSES AS SAMPLING THEORY GAINS GROUND

Chapter:
(p.223) 8 PRO-SUBJECTIVE APPROACH LOSES AS SAMPLING THEORY GAINS GROUND
Source:
Statistical Thought
Author(s):

Shoutir Kishore Chatterjee

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198525318.003.0008

In the first half of the 19th century, Laplace, Gauss, and a number of other contributors enriched statistical thought. The quest for a suitable model for the distribution of observational errors led to Gauss’s derivation of the normal model from the ‘A. M. postulate’. Laplace’s derivation of the Central Limit Theorem gave further support to the model. Different methods of curve fitting, of which the Least Squares method — which was heuristically proposed by Legendre and to which Gauss provided first a Bayesian and then a sampling theory justification — was the most important. During this period, Laplace worked on the large sample sampling theory approach to inference, and both he and Gauss introduced the idea of relative efficiency of estimates in the context of particular problems. In fact, the seeds of some later concepts like that of sufficiency, variance component models, and diffusion processes can be found in works carried out at this time.

Keywords:   Laplace, Gauss, A. M. postulate, Central Limit Theorem, Legendre, Least Squares method, large sample inference

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .