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Institution

University of Chicago

EducationChicago, Illinois, United States
About: University of Chicago is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 66716 authors who have published 160098 publications receiving 9644339 citations. The organization is also known as: Chicago University & U of C.


Papers
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Book ChapterDOI
TL;DR: In this article, the product-limit (PL) estimator was proposed to estimate the proportion of items in the population whose lifetimes would exceed t (in the absence of such losses), without making any assumption about the form of the function P(t).
Abstract: In lifetesting, medical follow-up, and other fields the observation of the time of occurrence of the event of interest (called a death) may be prevented for some of the items of the sample by the previous occurrence of some other event (called a loss). Losses may be either accidental or controlled, the latter resulting from a decision to terminate certain observations. In either case it is usually assumed in this paper that the lifetime (age at death) is independent of the potential loss time; in practice this assumption deserves careful scrutiny. Despite the resulting incompleteness of the data, it is desired to estimate the proportion P(t) of items in the population whose lifetimes would exceed t (in the absence of such losses), without making any assumption about the form of the function P(t). The observation for each item of a suitable initial event, marking the beginning of its lifetime, is presupposed. For random samples of size N the product-limit (PL) estimate can be defined as follows: L...

52,450 citations

Journal ArticleDOI
TL;DR: In this article, the authors identify five common risk factors in the returns on stocks and bonds, including three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity.

24,874 citations

Book
03 Mar 1992
TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
Abstract: Introduction The Logic of Hierarchical Linear Models Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models An Illustration Applications in Organizational Research Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known Three-Level Models Assessing the Adequacy of Hierarchical Models Technical Appendix

23,126 citations

Journal ArticleDOI
TL;DR: Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends thatclinical molecular genetic testing should be performed in a Clinical Laboratory Improvement Amendments–approved laboratory, with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or the equivalent.

17,834 citations

Journal ArticleDOI
TL;DR: In this article, the authors consider the prospects for constructing a neoclassical theory of growth and international trade that is consistent with some of the main features of economic development, and compare three models and compared to evidence.

16,965 citations


Authors

Showing all 67909 results

NameH-indexPapersCitations
George M. Whitesides2401739269833
Solomon H. Snyder2321222200444
Eugene Braunwald2301711264576
Kari Stefansson206794174819
Hagop M. Kantarjian2043708210208
David Miller2032573204840
Martin White1962038232387
Craig B. Thompson195557173172
Robert C. Nichol187851162994
Jing Wang1844046202769
Patrick O. Brown183755200985
Yusuke Nakamura1792076160313
H. S. Chen1792401178529
Joseph Biederman1791012117440
Daniel J. Eisenstein179672151720
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023300
20221,492
20217,416
20207,488
20196,751
20186,364