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Institution

London School of Economics and Political Science

EducationLondon, United Kingdom
About: London School of Economics and Political Science is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Politics & Population. The organization has 8759 authors who have published 35017 publications receiving 1436302 citations.


Papers
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Journal ArticleDOI
John W. Belmont1, Paul Hardenbol, Thomas D. Willis, Fuli Yu1, Huanming Yang2, Lan Yang Ch'Ang, Wei Huang3, Bin Liu2, Yan Shen3, Paul K.H. Tam4, Lap-Chee Tsui4, Mary M.Y. Waye5, Jeffrey Tze Fei Wong6, Changqing Zeng2, Qingrun Zhang2, Mark S. Chee7, Luana Galver7, Semyon Kruglyak7, Sarah S. Murray7, Arnold Oliphant7, Alexandre Montpetit8, Fanny Chagnon8, Vincent Ferretti8, Martin Leboeuf8, Michael S. Phillips8, Andrei Verner8, Shenghui Duan9, Denise L. Lind10, Raymond D. Miller9, John P. Rice9, Nancy L. Saccone9, Patricia Taillon-Miller9, Ming Xiao10, Akihiro Sekine, Koki Sorimachi, Yoichi Tanaka, Tatsuhiko Tsunoda, Eiji Yoshino, David R. Bentley11, Sarah E. Hunt11, Don Powell11, Houcan Zhang12, Ichiro Matsuda13, Yoshimitsu Fukushima14, Darryl Macer15, Eiko Suda15, Charles N. Rotimi16, Clement Adebamowo17, Toyin Aniagwu17, Patricia A. Marshall18, Olayemi Matthew17, Chibuzor Nkwodimmah17, Charmaine D.M. Royal16, Mark Leppert19, Missy Dixon19, Fiona Cunningham20, Ardavan Kanani20, Gudmundur A. Thorisson20, Peter E. Chen21, David J. Cutler21, Carl S. Kashuk21, Peter Donnelly22, Jonathan Marchini22, Gilean McVean22, Simon Myers22, Lon R. Cardon22, Andrew P. Morris22, Bruce S. Weir23, James C. Mullikin24, Michael Feolo24, Mark J. Daly25, Renzong Qiu26, Alastair Kent, Georgia M. Dunston16, Kazuto Kato27, Norio Niikawa28, Jessica Watkin29, Richard A. Gibbs1, Erica Sodergren1, George M. Weinstock1, Richard K. Wilson9, Lucinda Fulton9, Jane Rogers11, Bruce W. Birren25, Hua Han2, Hongguang Wang, Martin Godbout30, John C. Wallenburg8, Paul L'Archevêque, Guy Bellemare, Kazuo Todani, Takashi Fujita, Satoshi Tanaka, Arthur L. Holden, Francis S. Collins24, Lisa D. Brooks24, Jean E. McEwen24, Mark S. Guyer24, Elke Jordan31, Jane Peterson24, Jack Spiegel24, Lawrence M. Sung32, Lynn F. Zacharia24, Karen Kennedy29, Michael Dunn29, Richard Seabrook29, Mark Shillito, Barbara Skene29, John Stewart29, David Valle21, Ellen Wright Clayton33, Lynn B. Jorde19, Aravinda Chakravarti21, Mildred K. Cho34, Troy Duster35, Troy Duster36, Morris W. Foster37, Maria Jasperse38, Bartha Maria Knoppers39, Pui-Yan Kwok10, Julio Licinio40, Jeffrey C. Long41, Pilar N. Ossorio42, Vivian Ota Wang33, Charles N. Rotimi16, Patricia Spallone29, Patricia Spallone43, Sharon F. Terry44, Eric S. Lander25, Eric H. Lai45, Deborah A. Nickerson46, Gonçalo R. Abecasis41, David Altshuler47, Michael Boehnke41, Panos Deloukas11, Julie A. Douglas41, Stacey Gabriel25, Richard R. Hudson48, Thomas J. Hudson8, Leonid Kruglyak49, Yusuke Nakamura50, Robert L. Nussbaum24, Stephen F. Schaffner25, Stephen T. Sherry24, Lincoln Stein20, Toshihiro Tanaka 
18 Dec 2003-Nature
TL;DR: The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance the ability to choose targets for therapeutic intervention.
Abstract: The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.

5,926 citations

Journal ArticleDOI
28 Aug 2015-Science
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Abstract: Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.

5,532 citations

Journal ArticleDOI
Gregory A. Roth1, Gregory A. Roth2, Degu Abate3, Kalkidan Hassen Abate4  +1025 moreInstitutions (333)
TL;DR: Non-communicable diseases comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2).

5,211 citations

Book
30 Mar 1990
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Abstract: List of figures Acknowledgement Preface Notation and conventions List of abbreviations 1. Introduction 2. Univariate time series models 3. State space models and the Kalman filter 4. Estimation, prediction and smoothing for univariate structural time series models 5. Testing and model selection 6. Extensions of the univariate model 7. Explanatory variables 8. Multivariate models 9. Continuous time Appendices Selected answers to exercises References Author index Subject index.

5,071 citations

Journal ArticleDOI
TL;DR: In this paper, the gravity equation for trade was used to provide new estimates of this equation, and significant differences between the estimated estimator and those obtained with the traditional method were found.
Abstract: Although economists have long been aware of Jensen's inequality, many econometric applications have neglected an important implication of it: the standard practice of interpreting the parameters of log-linearized models estimated by ordinary least squares as elasticities can be highly misleading in the presence of heteroskedasticity. This paper explains why this problem arises and proposes an appropriate estimator. Our criticism to conventional practices and the solution we propose extends to a broad range of economic applications where the equation under study is log-linearized. We develop the argument using one particular illustration, the gravity equation for trade, and apply the proposed technique to provide new estimates of this equation. We find significant differences between estimates obtained with the proposed estimator and those obtained with the traditional method. These discrepancies persist even when the gravity equation takes into account multilateral resistance terms or fixed effects

4,492 citations


Authors

Showing all 9081 results

NameH-indexPapersCitations
Ichiro Kawachi149121690282
Amartya Sen149689141907
Peter Hall132164085019
Philippe Aghion12250773438
Robert West112106153904
Keith Beven11051461705
Andrew Pickles10943655981
Zvi Griliches10926071954
Martin Knapp106106748518
Stephen J. Wood10570039797
Jianqing Fan10448858039
Timothy Besley10336845988
Richard B. Freeman10086046932
Sonia Livingstone9951032667
John Van Reenen9844040128
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023135
2022457
20212,030
20201,835
20191,636
20181,561