for Journals by Title or ISSN
for Articles by Keywords
help

Publisher: Oxford University Press   (Total: 397 journals)

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 | Last   [Sort by number of followers]   [Restore default list]

Showing 1 - 200 of 397 Journals sorted alphabetically
ACS Symposium Series     Full-text available via subscription   (SJR: 0.189, CiteScore: 0)
Acta Biochimica et Biophysica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.79, CiteScore: 2)
Adaptation     Hybrid Journal   (Followers: 9, SJR: 0.143, CiteScore: 0)
Advances in Nutrition     Hybrid Journal   (Followers: 53, SJR: 2.196, CiteScore: 5)
Aesthetic Surgery J.     Hybrid Journal   (Followers: 6, SJR: 1.434, CiteScore: 1)
African Affairs     Hybrid Journal   (Followers: 66, SJR: 1.869, CiteScore: 2)
Age and Ageing     Hybrid Journal   (Followers: 91, SJR: 1.989, CiteScore: 4)
Alcohol and Alcoholism     Hybrid Journal   (Followers: 19, SJR: 1.376, CiteScore: 3)
American Entomologist     Full-text available via subscription   (Followers: 8)
American Historical Review     Hybrid Journal   (Followers: 161, SJR: 0.467, CiteScore: 1)
American J. of Agricultural Economics     Hybrid Journal   (Followers: 45, SJR: 2.113, CiteScore: 3)
American J. of Clinical Nutrition     Hybrid Journal   (Followers: 169, SJR: 3.438, CiteScore: 6)
American J. of Epidemiology     Hybrid Journal   (Followers: 190, SJR: 2.713, CiteScore: 3)
American J. of Hypertension     Hybrid Journal   (Followers: 25, SJR: 1.322, CiteScore: 3)
American J. of Jurisprudence     Hybrid Journal   (Followers: 19, SJR: 0.281, CiteScore: 1)
American J. of Legal History     Full-text available via subscription   (Followers: 8, SJR: 0.116, CiteScore: 0)
American Law and Economics Review     Hybrid Journal   (Followers: 27, SJR: 1.053, CiteScore: 1)
American Literary History     Hybrid Journal   (Followers: 16, SJR: 0.391, CiteScore: 0)
Analysis     Hybrid Journal   (Followers: 22, SJR: 1.038, CiteScore: 1)
Animal Frontiers     Hybrid Journal   (Followers: 1)
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 16, SJR: 1.423, CiteScore: 3)
Annals of Botany     Hybrid Journal   (Followers: 37, SJR: 1.721, CiteScore: 4)
Annals of Oncology     Hybrid Journal   (Followers: 56, SJR: 5.599, CiteScore: 9)
Annals of the Entomological Society of America     Full-text available via subscription   (Followers: 10, SJR: 0.722, CiteScore: 1)
Annals of Work Exposures and Health     Hybrid Journal   (Followers: 34, SJR: 0.728, CiteScore: 2)
Antibody Therapeutics     Open Access  
AoB Plants     Open Access   (Followers: 4, SJR: 1.28, CiteScore: 3)
Applied Economic Perspectives and Policy     Hybrid Journal   (Followers: 18, SJR: 0.858, CiteScore: 2)
Applied Linguistics     Hybrid Journal   (Followers: 58, SJR: 2.987, CiteScore: 3)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1, SJR: 1.241, CiteScore: 1)
Arbitration Intl.     Full-text available via subscription   (Followers: 20)
Arbitration Law Reports and Review     Hybrid Journal   (Followers: 14)
Archives of Clinical Neuropsychology     Hybrid Journal   (Followers: 30, SJR: 0.731, CiteScore: 2)
Aristotelian Society Supplementary Volume     Hybrid Journal   (Followers: 3)
Arthropod Management Tests     Hybrid Journal   (Followers: 2)
Astronomy & Geophysics     Hybrid Journal   (Followers: 44, SJR: 0.146, CiteScore: 0)
Behavioral Ecology     Hybrid Journal   (Followers: 52, SJR: 1.871, CiteScore: 3)
Bioinformatics     Hybrid Journal   (Followers: 326, SJR: 6.14, CiteScore: 8)
Biology Methods and Protocols     Hybrid Journal  
Biology of Reproduction     Full-text available via subscription   (Followers: 9, SJR: 1.446, CiteScore: 3)
Biometrika     Hybrid Journal   (Followers: 20, SJR: 3.485, CiteScore: 2)
BioScience     Hybrid Journal   (Followers: 29, SJR: 2.754, CiteScore: 4)
Bioscience Horizons : The National Undergraduate Research J.     Open Access   (Followers: 1, SJR: 0.146, CiteScore: 0)
Biostatistics     Hybrid Journal   (Followers: 17, SJR: 1.553, CiteScore: 2)
BJA : British J. of Anaesthesia     Hybrid Journal   (Followers: 178, SJR: 2.115, CiteScore: 3)
BJA Education     Hybrid Journal   (Followers: 65)
Brain     Hybrid Journal   (Followers: 68, SJR: 5.858, CiteScore: 7)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 51, SJR: 2.505, CiteScore: 5)
Briefings in Functional Genomics     Hybrid Journal   (Followers: 3, SJR: 2.15, CiteScore: 3)
British J. for the Philosophy of Science     Hybrid Journal   (Followers: 36, SJR: 2.161, CiteScore: 2)
British J. of Aesthetics     Hybrid Journal   (Followers: 25, SJR: 0.508, CiteScore: 1)
British J. of Criminology     Hybrid Journal   (Followers: 599, SJR: 1.828, CiteScore: 3)
British J. of Social Work     Hybrid Journal   (Followers: 85, SJR: 1.019, CiteScore: 2)
British Medical Bulletin     Hybrid Journal   (Followers: 6, SJR: 1.355, CiteScore: 3)
British Yearbook of Intl. Law     Hybrid Journal   (Followers: 33)
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4, SJR: 1.376, CiteScore: 1)
Cambridge J. of Economics     Hybrid Journal   (Followers: 65, SJR: 0.764, CiteScore: 2)
Cambridge J. of Regions, Economy and Society     Hybrid Journal   (Followers: 11, SJR: 2.438, CiteScore: 4)
Cambridge Quarterly     Hybrid Journal   (Followers: 10, SJR: 0.104, CiteScore: 0)
Capital Markets Law J.     Hybrid Journal   (Followers: 2, SJR: 0.222, CiteScore: 0)
Carcinogenesis     Hybrid Journal   (Followers: 2, SJR: 2.135, CiteScore: 5)
Cardiovascular Research     Hybrid Journal   (Followers: 14, SJR: 3.002, CiteScore: 5)
Cerebral Cortex     Hybrid Journal   (Followers: 46, SJR: 3.892, CiteScore: 6)
CESifo Economic Studies     Hybrid Journal   (Followers: 18, SJR: 0.483, CiteScore: 1)
Chemical Senses     Hybrid Journal   (Followers: 1, SJR: 1.42, CiteScore: 3)
Children and Schools     Hybrid Journal   (Followers: 6, SJR: 0.246, CiteScore: 0)
Chinese J. of Comparative Law     Hybrid Journal   (Followers: 5, SJR: 0.412, CiteScore: 0)
Chinese J. of Intl. Law     Hybrid Journal   (Followers: 22, SJR: 0.329, CiteScore: 0)
Chinese J. of Intl. Politics     Hybrid Journal   (Followers: 10, SJR: 1.392, CiteScore: 2)
Christian Bioethics: Non-Ecumenical Studies in Medical Morality     Hybrid Journal   (Followers: 10, SJR: 0.183, CiteScore: 0)
Classical Receptions J.     Hybrid Journal   (Followers: 27, SJR: 0.123, CiteScore: 0)
Clean Energy     Open Access   (Followers: 1)
Clinical Infectious Diseases     Hybrid Journal   (Followers: 70, SJR: 5.051, CiteScore: 5)
Communication Theory     Hybrid Journal   (Followers: 24, SJR: 2.424, CiteScore: 3)
Communication, Culture & Critique     Hybrid Journal   (Followers: 27, SJR: 0.222, CiteScore: 1)
Community Development J.     Hybrid Journal   (Followers: 27, SJR: 0.268, CiteScore: 1)
Computer J.     Hybrid Journal   (Followers: 9, SJR: 0.319, CiteScore: 1)
Conservation Physiology     Open Access   (Followers: 3, SJR: 1.818, CiteScore: 3)
Contemporary Women's Writing     Hybrid Journal   (Followers: 9, SJR: 0.121, CiteScore: 0)
Contributions to Political Economy     Hybrid Journal   (Followers: 5, SJR: 0.906, CiteScore: 1)
Critical Values     Full-text available via subscription  
Current Developments in Nutrition     Open Access   (Followers: 2)
Current Legal Problems     Hybrid Journal   (Followers: 29)
Current Zoology     Full-text available via subscription   (Followers: 3, SJR: 1.164, CiteScore: 2)
Database : The J. of Biological Databases and Curation     Open Access   (Followers: 8, SJR: 1.791, CiteScore: 3)
Digital Scholarship in the Humanities     Hybrid Journal   (Followers: 14, SJR: 0.259, CiteScore: 1)
Diplomatic History     Hybrid Journal   (Followers: 20, SJR: 0.45, CiteScore: 1)
DNA Research     Open Access   (Followers: 5, SJR: 2.866, CiteScore: 6)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 4)
Early Music     Hybrid Journal   (Followers: 16, SJR: 0.139, CiteScore: 0)
Economic Policy     Hybrid Journal   (Followers: 42, SJR: 3.584, CiteScore: 3)
ELT J.     Hybrid Journal   (Followers: 24, SJR: 0.942, CiteScore: 1)
English Historical Review     Hybrid Journal   (Followers: 54, SJR: 0.612, CiteScore: 1)
English: J. of the English Association     Hybrid Journal   (Followers: 15, SJR: 0.1, CiteScore: 0)
Environmental Entomology     Full-text available via subscription   (Followers: 11, SJR: 0.818, CiteScore: 2)
Environmental Epigenetics     Open Access   (Followers: 3)
Environmental History     Hybrid Journal   (Followers: 27, SJR: 0.408, CiteScore: 1)
EP-Europace     Hybrid Journal   (Followers: 3, SJR: 2.748, CiteScore: 4)
Epidemiologic Reviews     Hybrid Journal   (Followers: 9, SJR: 4.505, CiteScore: 8)
ESHRE Monographs     Hybrid Journal  
Essays in Criticism     Hybrid Journal   (Followers: 19, SJR: 0.113, CiteScore: 0)
European Heart J.     Hybrid Journal   (Followers: 63, SJR: 9.315, CiteScore: 9)
European Heart J. - Cardiovascular Imaging     Hybrid Journal   (Followers: 9, SJR: 3.625, CiteScore: 3)
European Heart J. - Cardiovascular Pharmacotherapy     Full-text available via subscription   (Followers: 2)
European Heart J. - Quality of Care and Clinical Outcomes     Hybrid Journal  
European Heart J. : Case Reports     Open Access  
European Heart J. Supplements     Hybrid Journal   (Followers: 8, SJR: 0.223, CiteScore: 0)
European J. of Cardio-Thoracic Surgery     Hybrid Journal   (Followers: 9, SJR: 1.681, CiteScore: 2)
European J. of Intl. Law     Hybrid Journal   (Followers: 195, SJR: 0.694, CiteScore: 1)
European J. of Orthodontics     Hybrid Journal   (Followers: 4, SJR: 1.279, CiteScore: 2)
European J. of Public Health     Hybrid Journal   (Followers: 20, SJR: 1.36, CiteScore: 2)
European Review of Agricultural Economics     Hybrid Journal   (Followers: 10, SJR: 1.172, CiteScore: 2)
European Review of Economic History     Hybrid Journal   (Followers: 30, SJR: 0.702, CiteScore: 1)
European Sociological Review     Hybrid Journal   (Followers: 42, SJR: 2.728, CiteScore: 3)
Evolution, Medicine, and Public Health     Open Access   (Followers: 12)
Family Practice     Hybrid Journal   (Followers: 16, SJR: 1.018, CiteScore: 2)
Fems Microbiology Ecology     Hybrid Journal   (Followers: 15, SJR: 1.492, CiteScore: 4)
Fems Microbiology Letters     Hybrid Journal   (Followers: 28, SJR: 0.79, CiteScore: 2)
Fems Microbiology Reviews     Hybrid Journal   (Followers: 32, SJR: 7.063, CiteScore: 13)
Fems Yeast Research     Hybrid Journal   (Followers: 13, SJR: 1.308, CiteScore: 3)
Food Quality and Safety     Open Access   (Followers: 1)
Foreign Policy Analysis     Hybrid Journal   (Followers: 25, SJR: 1.425, CiteScore: 1)
Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.89, CiteScore: 2)
Forestry: An Intl. J. of Forest Research     Hybrid Journal   (Followers: 16, SJR: 1.133, CiteScore: 3)
Forum for Modern Language Studies     Hybrid Journal   (Followers: 6, SJR: 0.104, CiteScore: 0)
French History     Hybrid Journal   (Followers: 33, SJR: 0.118, CiteScore: 0)
French Studies     Hybrid Journal   (Followers: 21, SJR: 0.148, CiteScore: 0)
French Studies Bulletin     Hybrid Journal   (Followers: 10, SJR: 0.152, CiteScore: 0)
Gastroenterology Report     Open Access   (Followers: 2)
Genome Biology and Evolution     Open Access   (Followers: 15, SJR: 2.578, CiteScore: 4)
Geophysical J. Intl.     Hybrid Journal   (Followers: 36, SJR: 1.506, CiteScore: 3)
German History     Hybrid Journal   (Followers: 23, SJR: 0.161, CiteScore: 0)
GigaScience     Open Access   (Followers: 5, SJR: 5.022, CiteScore: 7)
Global Summitry     Hybrid Journal   (Followers: 1)
Glycobiology     Hybrid Journal   (Followers: 13, SJR: 1.493, CiteScore: 3)
Health and Social Work     Hybrid Journal   (Followers: 57, SJR: 0.388, CiteScore: 1)
Health Education Research     Hybrid Journal   (Followers: 16, SJR: 0.854, CiteScore: 2)
Health Policy and Planning     Hybrid Journal   (Followers: 24, SJR: 1.512, CiteScore: 2)
Health Promotion Intl.     Hybrid Journal   (Followers: 22, SJR: 0.812, CiteScore: 2)
History Workshop J.     Hybrid Journal   (Followers: 31, SJR: 1.278, CiteScore: 1)
Holocaust and Genocide Studies     Hybrid Journal   (Followers: 28, SJR: 0.105, CiteScore: 0)
Human Communication Research     Hybrid Journal   (Followers: 15, SJR: 2.146, CiteScore: 3)
Human Molecular Genetics     Hybrid Journal   (Followers: 9, SJR: 3.555, CiteScore: 5)
Human Reproduction     Hybrid Journal   (Followers: 72, SJR: 2.643, CiteScore: 5)
Human Reproduction Open     Open Access   (Followers: 1)
Human Reproduction Update     Hybrid Journal   (Followers: 20, SJR: 5.317, CiteScore: 10)
Human Rights Law Review     Hybrid Journal   (Followers: 62, SJR: 0.756, CiteScore: 1)
ICES J. of Marine Science: J. du Conseil     Hybrid Journal   (Followers: 56, SJR: 1.591, CiteScore: 3)
ICSID Review : Foreign Investment Law J.     Hybrid Journal   (Followers: 10)
ILAR J.     Hybrid Journal   (Followers: 2, SJR: 1.732, CiteScore: 4)
IMA J. of Applied Mathematics     Hybrid Journal   (SJR: 0.679, CiteScore: 1)
IMA J. of Management Mathematics     Hybrid Journal   (SJR: 0.538, CiteScore: 1)
IMA J. of Mathematical Control and Information     Hybrid Journal   (Followers: 2, SJR: 0.496, CiteScore: 1)
IMA J. of Numerical Analysis - advance access     Hybrid Journal   (SJR: 1.987, CiteScore: 2)
Industrial and Corporate Change     Hybrid Journal   (Followers: 10, SJR: 1.792, CiteScore: 2)
Industrial Law J.     Hybrid Journal   (Followers: 39, SJR: 0.249, CiteScore: 1)
Inflammatory Bowel Diseases     Hybrid Journal   (Followers: 48, SJR: 2.511, CiteScore: 4)
Information and Inference     Free  
Integrative and Comparative Biology     Hybrid Journal   (Followers: 8, SJR: 1.319, CiteScore: 2)
Interacting with Computers     Hybrid Journal   (Followers: 11, SJR: 0.292, CiteScore: 1)
Interactive CardioVascular and Thoracic Surgery     Hybrid Journal   (Followers: 7, SJR: 0.762, CiteScore: 1)
Intl. Affairs     Hybrid Journal   (Followers: 65, SJR: 1.505, CiteScore: 3)
Intl. Data Privacy Law     Hybrid Journal   (Followers: 25)
Intl. Health     Hybrid Journal   (Followers: 6, SJR: 0.851, CiteScore: 2)
Intl. Immunology     Hybrid Journal   (Followers: 3, SJR: 2.167, CiteScore: 4)
Intl. J. for Quality in Health Care     Hybrid Journal   (Followers: 36, SJR: 1.348, CiteScore: 2)
Intl. J. of Constitutional Law     Hybrid Journal   (Followers: 64, SJR: 0.601, CiteScore: 1)
Intl. J. of Epidemiology     Hybrid Journal   (Followers: 241, SJR: 3.969, CiteScore: 5)
Intl. J. of Law and Information Technology     Hybrid Journal   (Followers: 5, SJR: 0.202, CiteScore: 1)
Intl. J. of Law, Policy and the Family     Hybrid Journal   (Followers: 28, SJR: 0.223, CiteScore: 1)
Intl. J. of Lexicography     Hybrid Journal   (Followers: 10, SJR: 0.285, CiteScore: 1)
Intl. J. of Low-Carbon Technologies     Open Access   (Followers: 1, SJR: 0.403, CiteScore: 1)
Intl. J. of Neuropsychopharmacology     Open Access   (Followers: 3, SJR: 1.808, CiteScore: 4)
Intl. J. of Public Opinion Research     Hybrid Journal   (Followers: 11, SJR: 1.545, CiteScore: 1)
Intl. J. of Refugee Law     Hybrid Journal   (Followers: 38, SJR: 0.389, CiteScore: 1)
Intl. J. of Transitional Justice     Hybrid Journal   (Followers: 11, SJR: 0.724, CiteScore: 2)
Intl. Mathematics Research Notices     Hybrid Journal   (Followers: 1, SJR: 2.168, CiteScore: 1)
Intl. Political Sociology     Hybrid Journal   (Followers: 40, SJR: 1.465, CiteScore: 3)
Intl. Relations of the Asia-Pacific     Hybrid Journal   (Followers: 23, SJR: 0.401, CiteScore: 1)
Intl. Studies Perspectives     Hybrid Journal   (Followers: 9, SJR: 0.983, CiteScore: 1)
Intl. Studies Quarterly     Hybrid Journal   (Followers: 48, SJR: 2.581, CiteScore: 2)
Intl. Studies Review     Hybrid Journal   (Followers: 25, SJR: 1.201, CiteScore: 1)
ISLE: Interdisciplinary Studies in Literature and Environment     Hybrid Journal   (Followers: 2, SJR: 0.15, CiteScore: 0)
ITNOW     Hybrid Journal   (Followers: 1, SJR: 0.103, CiteScore: 0)
J. of African Economies     Hybrid Journal   (Followers: 16, SJR: 0.533, CiteScore: 1)
J. of American History     Hybrid Journal   (Followers: 46, SJR: 0.297, CiteScore: 1)
J. of Analytical Toxicology     Hybrid Journal   (Followers: 14, SJR: 1.065, CiteScore: 2)
J. of Antimicrobial Chemotherapy     Hybrid Journal   (Followers: 15, SJR: 2.419, CiteScore: 4)
J. of Antitrust Enforcement     Hybrid Journal   (Followers: 1)
J. of Applied Poultry Research     Hybrid Journal   (Followers: 5, SJR: 0.585, CiteScore: 1)
J. of Biochemistry     Hybrid Journal   (Followers: 40, SJR: 1.226, CiteScore: 2)
J. of Burn Care & Research     Hybrid Journal   (Followers: 10, SJR: 0.768, CiteScore: 2)
J. of Chromatographic Science     Hybrid Journal   (Followers: 18, SJR: 0.36, CiteScore: 1)
J. of Church and State     Hybrid Journal   (Followers: 12, SJR: 0.139, CiteScore: 0)
J. of Communication     Hybrid Journal   (Followers: 55, SJR: 4.411, CiteScore: 5)
J. of Competition Law and Economics     Hybrid Journal   (Followers: 37, SJR: 0.33, CiteScore: 0)
J. of Complex Networks     Hybrid Journal   (Followers: 2, SJR: 1.05, CiteScore: 4)
J. of Computer-Mediated Communication     Open Access   (Followers: 29, SJR: 2.961, CiteScore: 6)
J. of Conflict and Security Law     Hybrid Journal   (Followers: 13, SJR: 0.402, CiteScore: 0)
J. of Consumer Research     Full-text available via subscription   (Followers: 47, SJR: 5.856, CiteScore: 5)

        1 2 | Last   [Sort by number of followers]   [Restore default list]

Journal Cover
Biometrika
Journal Prestige (SJR): 3.485
Citation Impact (citeScore): 2
Number of Followers: 20  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0006-3444 - ISSN (Online) 1464-3510
Published by Oxford University Press Homepage  [397 journals]
  • Discussion of ‘Gene hunting with hidden Markov model
           knockoffs’
    • Authors: Bottolo L; Richardson S.
      Pages: 19 - 22
      PubDate: Wed, 13 Feb 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy063
      Issue No: Vol. 106, No. 1 (2019)
       
  • Discussion of ‘Gene hunting with hidden Markov model
           knockoffs’
    • Authors: Jewell S; Witten D.
      Pages: 23 - 26
      PubDate: Wed, 13 Feb 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy061
      Issue No: Vol. 106, No. 1 (2019)
       
  • Discussion of ‘Gene hunting with hidden Markov model
           knockoffs’
    • Authors: Marchini J.
      Pages: 27 - 28
      PubDate: Wed, 13 Feb 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy067
      Issue No: Vol. 106, No. 1 (2019)
       
  • Discussion of ‘Gene hunting with hidden Markov model
           knockoffs’
    • Authors: Rosenblatt J; Ritov Y, Goeman J.
      Pages: 29 - 33
      PubDate: Wed, 13 Feb 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy062
      Issue No: Vol. 106, No. 1 (2019)
       
  • Rejoinder: ‘Gene hunting with hidden Markov model knockoffs’
    • Authors: Sesia M; Sabatti C, Candès E.
      Pages: 35 - 45
      PubDate: Wed, 13 Feb 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy075
      Issue No: Vol. 106, No. 1 (2019)
       
  • Testing for independence in arbitrary distributions
    • Authors: Genest C; Nešlehová J, Rémillard B, et al.
      Pages: 47 - 68
      Abstract: SUMMARYStatistics are proposed for testing the hypothesis that arbitrary random variables are mutually independent. The tests are consistent and well behaved for any marginal distributions; they can be used, for example, for contingency tables which are sparse or whose dimension depends on the sample size, as well as for mixed data. No regularity conditions, data jittering, or binning mechanisms are required. The statistics are rank-based functionals of Cramér–von Mises type whose asymptotic behaviour derives from the empirical multilinear copula process. Approximate $p$-values are computed using a wild bootstrap. The procedures are simple to implement and computationally efficient, and maintain their level well in moderate to large samples. Simulations suggest that the tests are robust with respect to the number of ties in the data, can easily detect a broad range of alternatives, and outperform existing procedures in many settings. Additional insight into their performance is provided through asymptotic local power calculations under contiguous alternatives. The procedures are illustrated on traumatic brain injury data.
      PubDate: Mon, 07 Jan 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy059
      Issue No: Vol. 106, No. 1 (2019)
       
  • A sequential algorithm for false discovery rate control on directed
           acyclic graphs
    • Authors: Ramdas A; Chen J, Wainwright M, et al.
      Pages: 69 - 86
      Abstract: SUMMARYWe propose a linear-time, single-pass, top-down algorithm for multiple testing on directed acyclic graphs, where nodes represent hypotheses and edges specify a partial ordering in which the hypotheses must be tested. The procedure is guaranteed to reject a sub-directed acyclic graph with bounded false discovery rate while satisfying the logical constraint that a rejected node’s parents must also be rejected. It is designed for sequential testing settings where the directed acyclic graph structure is known a priori but the $p$-values are obtained selectively, such as in a sequence of experiments; however, the algorithm is also applicable in nonsequential settings where all $p$-values can be calculated in advance, such as in model selection. Our algorithm provably controls the false discovery rate under independence, positive dependence or arbitrary dependence of the $p$-values and specializes to known algorithms in the special cases of trees and line graphs; it simplifies to the classical Benjamini–Hochberg procedure when the directed acyclic graph has no edges. We explore the empirical performance of our algorithm through simulations and analysis of a real dataset corresponding to a gene ontology, and we demonstrate its favourable performance in terms of computational time and power.
      PubDate: Tue, 29 Jan 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy066
      Issue No: Vol. 106, No. 1 (2019)
       
  • Counting process-based dimension reduction methods for censored outcomes
    • Authors: Sun Q; Zhu R, Wang T, et al.
      Pages: 181 - 196
      Abstract: SUMMARYWe propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate for the bias in estimating the dimension reduction subspace. Second, the nonparametric estimation involved adapts to the structural dimension, so our methods circumvent the curse of dimensionality. Asymptotic normality is established for the estimators. We propose a computationally efficient approach that requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance. The methods are implemented in the $\texttt{R}$ package $\texttt{orthoDr}$.
      PubDate: Mon, 07 Jan 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy064
      Issue No: Vol. 106, No. 1 (2019)
       
  • Low-risk population size estimates in the presence of capture
           heterogeneity
    • Authors: Johndrow J; Lum K, Manrique-Vallier D.
      Pages: 197 - 210
      Abstract: SUMMARYPopulation estimation methods are used for estimating the size of a population from samples of individuals. In many applications, the probability of being observed in the sample varies across individuals, resulting in sampling bias. We show that in this setting, estimators of the population size have high and sometimes infinite risk, leading to large uncertainty in the population size. As an alternative, we propose estimating the population of individuals with observation probability exceeding a small threshold. We show that estimators of this quantity have lower risk than estimators of the total population size. The proposed approach is shown empirically to result in large reductions in mean squared error in a common model for capture-recapture population estimation with heterogeneous capture probabilities.
      PubDate: Tue, 22 Jan 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy065
      Issue No: Vol. 106, No. 1 (2019)
       
  • Semiparametric inference for the dominance index under the density ratio
           model
    • Authors: Zhuang W; Hu B, Chen J.
      Pages: 229 - 241
      Abstract: SUMMARYAn important and often discussed research problem in statistics is how to compare several populations; examples arise in medical science, engineering, finance and other fields. Often population means or medians are compared. However, one population may have a higher mean income, for example, because of a small number of super-rich individuals; the mean therefore may not reflect the wealth of the general population. Instead, an index of the degree of stochastic dominance of one population over another would better reflect their relative wealth. Currently, we can estimate such an index under restrictive conditions, but there is no generic estimator with a known asymptotic distribution. In this paper, we suggest linking the populations via the density ratio model. Under this model, we develop an empirical likelihood estimator and establish its asymptotic normality. In addition, we improve the estimation efficiency by examining the similarities between the populations. Furthermore, we provide a valid bootstrap method for hypothesis testing and the construction of confidence intervals. Simulation experiments show that the proposed estimator substantially improves the estimation efficiency and power of the test, and leads to confidence intervals with satisfactorily precise coverage probabilities. It is also robust with respect to mild model misspecification. Two examples are given to demonstrate the usefulness of both the method and the concept.
      PubDate: Tue, 22 Jan 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy068
      Issue No: Vol. 106, No. 1 (2019)
       
  • Signal-plus-noise matrix models: eigenvector deviations and fluctuations
    • Authors: Cape J; Tang M, Priebe C.
      Pages: 243 - 250
      Abstract: SummaryEstimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science and applied mathematics. In this paper we characterize the behaviour of perturbed eigenvectors for a range of signal-plus-noise matrix models encountered in statistical and random-matrix-theoretic settings. We establish both first-order approximation results, i.e., sharp deviations, and second-order distributional limit theory, i.e., fluctuations. The concise methodology presented in this paper synthesizes tools rooted in two core concepts, namely deterministic decompositions of matrix perturbations and probabilistic matrix concentration phenomena. We illustrate our theoretical results with simulation examples involving stochastic block model random graphs.
      PubDate: Mon, 07 Jan 2019 00:00:00 GMT
      DOI: 10.1093/biomet/asy070
      Issue No: Vol. 106, No. 1 (2019)
       
  • Gene hunting with hidden Markov model knockoffs
    • Authors: Sesia M; Sabatti C, Candès E.
      Pages: 1 - 18
      Abstract: SUMMARYModern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for inference in genome-wide association studies with guaranteed false discovery rate control. We apply our method to datasets on Crohn’s disease and some continuous phenotypes.
      PubDate: Sat, 04 Aug 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy033
      Issue No: Vol. 106, No. 1 (2018)
       
  • Nonparametric regression with adaptive truncation via a convex
           hierarchical penalty
    • Authors: Haris A; Shojaie A, Simon N.
      Pages: 87 - 107
      Abstract: SUMMARYWe consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and smoothing penalties. In the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions possess different levels of complexity. In contrast, a smoothing spline-type penalty on the component functions is adaptive but does not provide a parsimonious representation. Our proposal simultaneously achieves parsimony and adaptivity in a computationally efficient way. We demonstrate these properties through empirical studies and show that our estimator converges at the minimax rate for functions within a hierarchical class. We further establish minimax rates for a large class of sparse additive models. We also develop an efficient algorithm that scales similarly to the lasso with the number of covariates and sample size.
      PubDate: Thu, 13 Dec 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy056
      Issue No: Vol. 106, No. 1 (2018)
       
  • Constrained likelihood for reconstructing a directed acyclic Gaussian
           graph
    • Authors: Yuan Y; Shen X, Pan W, et al.
      Pages: 109 - 125
      Abstract: SUMMARYDirected acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph’s structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially when a local or sequential approach is used to enumerate edge directions by testing or optimizing a criterion locally, as a local method may break down even for moderately sized graphs. We propose a novel approach to simultaneously identifying all estimable directed edges and model parameters, using constrained maximum likelihood with nonconvex constraints. We develop a constraint reduction method that constructs a set of active constraints from super-exponentially many constraints. This, coupled with an alternating direction method of multipliers and a difference convex method, permits efficient computation for large-graph learning. We show that the proposed method consistently reconstructs identifiable directions of the true graph and achieves the optimal performance in terms of parameter estimation. Numerically, the method compares favourably with competitors. A protein network is analysed to demonstrate that the proposed method can make a difference in identifying the network’s structure.
      PubDate: Thu, 13 Dec 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy057
      Issue No: Vol. 106, No. 1 (2018)
       
  • Extremal behaviour of aggregated data with an application to downscaling
    • Authors: Engelke S; De Fondeville R, Oesting M.
      Pages: 127 - 144
      Abstract: SUMMARYThe distribution of spatially aggregated data from a stochastic process $X$ may exhibit tail behaviour different from that of its marginal distributions. For a large class of aggregating functionals $\ell$ we introduce the $\ell$-extremal coefficient, which quantifies this difference as a function of the extremal spatial dependence in $X$. We also obtain the joint extremal dependence for multiple aggregation functionals applied to the same process. Formulae for the $\ell$-extremal coefficients and multivariate dependence structures are derived in important special cases. The results provide a theoretical link between the extremal distribution of the aggregated data and the corresponding underlying process, which we exploit to develop a method for statistical downscaling. We apply our framework to downscale daily temperature maxima in the south of France from a gridded dataset and use our model to generate high-resolution maps of the warmest day during the $2003$ heatwave.
      PubDate: Fri, 28 Dec 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy052
      Issue No: Vol. 106, No. 1 (2018)
       
  • Recovering covariance from functional fragments
    • Authors: Descary M; Panaretos V.
      Pages: 145 - 160
      Abstract: SUMMARYWe consider nonparametric estimation of a covariance function on the unit square, given a sample of discretely observed fragments of functional data. When each sample path is observed only on a subinterval of length $\delta<1$, one has no statistical information on the unknown covariance outside a $\delta$-band around the diagonal. The problem seems unidentifiable without parametric assumptions, but we show that nonparametric estimation is feasible under suitable smoothness and rank conditions on the unknown covariance. This remains true even when the observations are discrete, and we give precise deterministic conditions on how fine the observation grid needs to be relative to the rank and fragment length for identifiability to hold true. We show that our conditions translate the estimation problem to a low-rank matrix completion problem, construct a nonparametric estimator in this vein, and study its asymptotic properties. We illustrate the numerical performance of our method on real and simulated data.
      PubDate: Mon, 17 Dec 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy055
      Issue No: Vol. 106, No. 1 (2018)
       
  • Classification of functional fragments by regularized linear classifiers
           with domain selection
    • Authors: Kraus D; Stefanucci M.
      Pages: 161 - 180
      Abstract: SUMMARYWe consider classification of functional data into two groups by linear classifiers based on one-dimensional projections of functions. We reformulate the task of finding the best classifier as an optimization problem and solve it by the conjugate gradient method with early stopping, the principal component method, and the ridge method. We study the empirical version with finite training samples consisting of incomplete functions observed on different subsets of the domain and show that the optimal, possibly zero, misclassification probability can be achieved in the limit along a possibly nonconvergent empirical regularization path. We propose a domain extension and selection procedure that finds the best domain beyond the common observation domain of all curves. In a simulation study we compare the different regularization methods and investigate the performance of domain selection. Our method is illustrated on a medical dataset, where we observe a substantial improvement of classification accuracy due to domain extension.
      PubDate: Mon, 17 Dec 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy060
      Issue No: Vol. 106, No. 1 (2018)
       
  • Goodness-of-fit tests for the cure rate in a mixture cure model
    • Authors: Müller U; Van Keilegom I.
      Pages: 211 - 227
      Abstract: SUMMARYWe consider models for time-to-event data that allow that an event, e.g., a relapse of a disease, never occurs for a certain percentage $p$ of the population, called the cure rate. We suppose that these data are subject to random right censoring and we model the data using a mixture cure model, in which the survival function of the uncured subjects is left unspecified. The aim is to test whether the cure rate $p$, as a function of the covariates, satisfies a certain parametric model. To do so, we propose a test statistic that is inspired by a goodness-of-fit test for a regression function due to Härdle & Mammen (1993). We show that the statistic is asymptotically normally distributed under the null hypothesis, that the model is correctly specified, and under local alternatives. A bootstrap procedure is proposed to implement the test. The good performance of the approach is confirmed with simulations. For illustration we apply the test to data on the times between first and second births.
      PubDate: Mon, 17 Dec 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy058
      Issue No: Vol. 106, No. 1 (2018)
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 18.212.206.217
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-