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Publisher: Oxford University Press   (Total: 396 journals)

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Showing 1 - 200 of 396 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: 46, SJR: 2.196, CiteScore: 5)
Aesthetic Surgery J.     Hybrid Journal   (Followers: 6, SJR: 1.434, CiteScore: 1)
African Affairs     Hybrid Journal   (Followers: 64, SJR: 1.869, CiteScore: 2)
Age and Ageing     Hybrid Journal   (Followers: 91, SJR: 1.989, CiteScore: 4)
Alcohol and Alcoholism     Hybrid Journal   (Followers: 18, SJR: 1.376, CiteScore: 3)
American Entomologist     Full-text available via subscription   (Followers: 7)
American Historical Review     Hybrid Journal   (Followers: 154, SJR: 0.467, CiteScore: 1)
American J. of Agricultural Economics     Hybrid Journal   (Followers: 41, SJR: 2.113, CiteScore: 3)
American J. of Clinical Nutrition     Hybrid Journal   (Followers: 147, SJR: 3.438, CiteScore: 6)
American J. of Epidemiology     Hybrid Journal   (Followers: 175, 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: 18, 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: 15, SJR: 0.391, CiteScore: 0)
Analysis     Hybrid Journal   (Followers: 21, SJR: 1.038, CiteScore: 1)
Animal Frontiers     Hybrid Journal  
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 15, SJR: 1.423, CiteScore: 3)
Annals of Botany     Hybrid Journal   (Followers: 36, SJR: 1.721, CiteScore: 4)
Annals of Oncology     Hybrid Journal   (Followers: 42, 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: 32, SJR: 0.728, CiteScore: 2)
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: 56, 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: 43, SJR: 0.146, CiteScore: 0)
Behavioral Ecology     Hybrid Journal   (Followers: 52, SJR: 1.871, CiteScore: 3)
Bioinformatics     Hybrid Journal   (Followers: 302, SJR: 6.14, CiteScore: 8)
Biology Methods and Protocols     Hybrid Journal  
Biology of Reproduction     Full-text available via subscription   (Followers: 10, 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: 166, SJR: 2.115, CiteScore: 3)
BJA Education     Hybrid Journal   (Followers: 64)
Brain     Hybrid Journal   (Followers: 68, SJR: 5.858, CiteScore: 7)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 48, 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: 35, SJR: 2.161, CiteScore: 2)
British J. of Aesthetics     Hybrid Journal   (Followers: 26, SJR: 0.508, CiteScore: 1)
British J. of Criminology     Hybrid Journal   (Followers: 585, SJR: 1.828, CiteScore: 3)
British J. of Social Work     Hybrid Journal   (Followers: 88, SJR: 1.019, CiteScore: 2)
British Medical Bulletin     Hybrid Journal   (Followers: 7, SJR: 1.355, CiteScore: 3)
British Yearbook of Intl. Law     Hybrid Journal   (Followers: 32)
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4, SJR: 1.376, CiteScore: 1)
Cambridge J. of Economics     Hybrid Journal   (Followers: 62, 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: 9, 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: 45, SJR: 3.892, CiteScore: 6)
CESifo Economic Studies     Hybrid Journal   (Followers: 17, SJR: 0.483, CiteScore: 1)
Chemical Senses     Hybrid Journal   (Followers: 1, SJR: 1.42, CiteScore: 3)
Children and Schools     Hybrid Journal   (Followers: 5, SJR: 0.246, CiteScore: 0)
Chinese J. of Comparative Law     Hybrid Journal   (Followers: 4, SJR: 0.412, CiteScore: 0)
Chinese J. of Intl. Law     Hybrid Journal   (Followers: 23, SJR: 0.329, CiteScore: 0)
Chinese J. of Intl. Politics     Hybrid Journal   (Followers: 9, 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: 26, SJR: 0.123, CiteScore: 0)
Clean Energy     Open Access   (Followers: 1)
Clinical Infectious Diseases     Hybrid Journal   (Followers: 65, SJR: 5.051, CiteScore: 5)
Clinical Kidney J.     Open Access   (Followers: 3, SJR: 1.163, CiteScore: 2)
Communication Theory     Hybrid Journal   (Followers: 22, SJR: 2.424, CiteScore: 3)
Communication, Culture & Critique     Hybrid Journal   (Followers: 26, 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: 2, 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: 1)
Current Legal Problems     Hybrid Journal   (Followers: 27)
Current Zoology     Full-text available via subscription   (Followers: 2, 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: 15, SJR: 0.139, CiteScore: 0)
Economic Policy     Hybrid Journal   (Followers: 39, SJR: 3.584, CiteScore: 3)
ELT J.     Hybrid Journal   (Followers: 24, SJR: 0.942, CiteScore: 1)
English Historical Review     Hybrid Journal   (Followers: 52, SJR: 0.612, CiteScore: 1)
English: J. of the English Association     Hybrid Journal   (Followers: 14, 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: 2, 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: 17, SJR: 0.113, CiteScore: 0)
European Heart J.     Hybrid Journal   (Followers: 57, 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: 1)
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: 186, 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: 29, SJR: 0.702, CiteScore: 1)
European Sociological Review     Hybrid Journal   (Followers: 40, SJR: 2.728, CiteScore: 3)
Evolution, Medicine, and Public Health     Open Access   (Followers: 11)
Family Practice     Hybrid Journal   (Followers: 15, SJR: 1.018, CiteScore: 2)
Fems Microbiology Ecology     Hybrid Journal   (Followers: 12, SJR: 1.492, CiteScore: 4)
Fems Microbiology Letters     Hybrid Journal   (Followers: 24, SJR: 0.79, CiteScore: 2)
Fems Microbiology Reviews     Hybrid Journal   (Followers: 30, SJR: 7.063, CiteScore: 13)
Fems Yeast Research     Hybrid Journal   (Followers: 14, SJR: 1.308, CiteScore: 3)
Food Quality and Safety     Open Access   (Followers: 1)
Foreign Policy Analysis     Hybrid Journal   (Followers: 23, 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: 20, 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: 12, SJR: 2.578, CiteScore: 4)
Geophysical J. Intl.     Hybrid Journal   (Followers: 35, SJR: 1.506, CiteScore: 3)
German History     Hybrid Journal   (Followers: 22, SJR: 0.161, CiteScore: 0)
GigaScience     Open Access   (Followers: 4, SJR: 5.022, CiteScore: 7)
Global Summitry     Hybrid Journal   (Followers: 1)
Glycobiology     Hybrid Journal   (Followers: 14, SJR: 1.493, CiteScore: 3)
Health and Social Work     Hybrid Journal   (Followers: 56, SJR: 0.388, CiteScore: 1)
Health Education Research     Hybrid Journal   (Followers: 15, 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: 13, SJR: 2.146, CiteScore: 3)
Human Molecular Genetics     Hybrid Journal   (Followers: 8, SJR: 3.555, CiteScore: 5)
Human Reproduction     Hybrid Journal   (Followers: 71, SJR: 2.643, CiteScore: 5)
Human Reproduction Open     Open Access  
Human Reproduction Update     Hybrid Journal   (Followers: 20, SJR: 5.317, CiteScore: 10)
Human Rights Law Review     Hybrid Journal   (Followers: 56, SJR: 0.756, CiteScore: 1)
ICES J. of Marine Science: J. du Conseil     Hybrid Journal   (Followers: 52, SJR: 1.591, CiteScore: 3)
ICSID Review     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: 35, SJR: 0.249, CiteScore: 1)
Inflammatory Bowel Diseases     Hybrid Journal   (Followers: 44, 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: 60, 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: 37, SJR: 1.348, CiteScore: 2)
Intl. J. of Constitutional Law     Hybrid Journal   (Followers: 63, SJR: 0.601, CiteScore: 1)
Intl. J. of Epidemiology     Hybrid Journal   (Followers: 225, 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: 26, SJR: 0.223, CiteScore: 1)
Intl. J. of Lexicography     Hybrid Journal   (Followers: 9, 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: 9, SJR: 1.545, CiteScore: 1)
Intl. J. of Refugee Law     Hybrid Journal   (Followers: 35, 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: 37, 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: 45, SJR: 2.581, CiteScore: 2)
Intl. Studies Review     Hybrid Journal   (Followers: 23, 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: 15, 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: 4, 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: 9, 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: 11, SJR: 0.139, CiteScore: 0)
J. of Communication     Hybrid Journal   (Followers: 53, SJR: 4.411, CiteScore: 5)
J. of Competition Law and Economics     Hybrid Journal   (Followers: 35, 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: 26, SJR: 2.961, CiteScore: 6)
J. of Conflict and Security Law     Hybrid Journal   (Followers: 12, SJR: 0.402, CiteScore: 0)
J. of Consumer Research     Full-text available via subscription   (Followers: 43, SJR: 5.856, CiteScore: 5)

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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  [396 journals]
  • Assessing replicability of findings across two studies of multiple
           features
    • Authors: Bogomolov M; Heller R.
      Pages: 505 - 516
      Abstract: SummaryReplicability analysis aims to identify the overlapping signals across independent studies that examine the same features. For this purpose we develop hypothesis testing procedures that first select the promising features from each of two studies separately. Only those features selected in both studies are then tested. The proposed procedures have theoretical guarantees regarding their control of the familywise error rate or false discovery rate on the replicability claims. They can also be used for signal discovery in each study separately, with the desired error control. Their power for detecting truly replicable findings is compared to alternatives. We illustrate the procedures on behavioural genetics data.
      PubDate: Mon, 11 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy029
      Issue No: Vol. 105, No. 3 (2018)
       
  • When is the first spurious variable selected by sequential regression
           procedures'
    • Authors: Su W.
      Pages: 517 - 527
      Abstract: SummaryApplied statisticians use sequential regression procedures to rank explanatory variables and, in settings of low correlations between variables and strong true effect sizes, expect that variables at the top of this ranking are truly relevant to the response. In a regime of certain sparsity levels, however, we show that the lasso, forward stepwise regression, and least angle regression include the first spurious variable unexpectedly early. We derive a sharp prediction of the rank of the first spurious variable for these three procedures, demonstrating that it occurs earlier and earlier as the regression coefficients become denser. This phenomenon persists for statistically independent Gaussian random designs and arbitrarily large true effects. We gain insight by identifying the underlying cause and then introduce a simple visualization tool termed the double-ranking diagram to improve on these methods. We obtain the first result establishing the exact equivalence between the lasso and least angle regression in the early stages of solution paths beyond orthogonal designs. This equivalence implies that many important model selection results concerning the lasso can be carried over to least angle regression.
      PubDate: Thu, 28 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy032
      Issue No: Vol. 105, No. 3 (2018)
       
  • Asymptotic normality of interpoint distances for high-dimensional data
           with applications to the two-sample problem
    • Authors: Li J.
      Pages: 529 - 546
      Abstract: SummaryInterpoint distances have applications in many areas of probability and statistics. Thanks to their simplicity of computation, interpoint distance-based procedures are particularly appealing for analysing small samples of high-dimensional data. In this paper, we first study the asymptotic distribution of interpoint distances in the high-dimension, low-sample-size setting and show that it is normal under regularity conditions. We then construct a powerful test for the two-sample problem, which is consistent for detecting location and scale differences. Simulations show that the test compares favourably with existing distance-based tests.
      PubDate: Fri, 15 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy020
      Issue No: Vol. 105, No. 3 (2018)
       
  • Symmetric rank covariances: a generalized framework for nonparametric
           measures of dependence
    • Authors: Weihs L; Drton M, Meinshausen N.
      Pages: 547 - 562
      Abstract: SummaryThe need to test whether two random vectors are independent has spawned many competing measures of dependence. We focus on nonparametric measures that are invariant under strictly increasing transformations, such as Kendall’s tau, Hoeffding’s $D$, and the Bergsma–Dassios sign covariance. Each exhibits symmetries that are not readily apparent from their definitions. Making these symmetries explicit, we define a new class of multivariate nonparametric measures of dependence that we call symmetric rank covariances. This new class generalizes the above measures and leads naturally to multivariate extensions of the Bergsma–Dassios sign covariance. Symmetric rank covariances may be estimated unbiasedly using U-statistics, for which we prove results on computational efficiency and large-sample behaviour. The algorithms we develop for their computation include, to the best of our knowledge, the first efficient algorithms for Hoeffding’s $D$ statistic in the multivariate setting.
      PubDate: Fri, 15 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy021
      Issue No: Vol. 105, No. 3 (2018)
       
  • Shrinking characteristics of precision matrix estimators
    • Authors: Molstad A; Rothman A.
      Pages: 563 - 574
      Abstract: SummaryWe propose a framework to shrink a user-specified characteristic of a precision matrix estimator that is needed to fit a predictive model. Estimators in our framework minimize the Gaussian negative loglikelihood plus an $L_1$ penalty on a linear or affine function evaluated at the optimization variable corresponding to the precision matrix. We establish convergence rate bounds for these estimators and propose an alternating direction method of multipliers algorithm for their computation. Our simulation studies show that our estimators can perform better than competitors when they are used to fit predictive models. In particular, we illustrate cases where our precision matrix estimators perform worse at estimating the population precision matrix but better at prediction.
      PubDate: Thu, 10 May 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy023
      Issue No: Vol. 105, No. 3 (2018)
       
  • High-dimensional peaks-over-threshold inference
    • Authors: de Fondeville R; Davison A.
      Pages: 575 - 592
      Abstract: SummaryMax-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. ${r}$-Pareto processes are mathematically simpler and have the potential advantage of incorporating all relevant extreme events, by generalizing the notion of a univariate exceedance. In this paper we investigate the use of proper scoring rules for high-dimensional peaks-over-threshold inference, focusing on extreme-value processes associated with log-Gaussian random functions, and compare gradient score estimators with the spectral and censored likelihood estimators for regularly varying distributions with normalized marginals, using data with several hundred locations. When simulating from the true model, the spectral estimator performs best, closely followed by the gradient score estimator, but censored likelihood estimation performs better with simulations from the domain of attraction, though it is outperformed by the gradient score in cases of weak extremal dependence. We illustrate the potential and flexibility of our ideas by modelling extreme rainfall on a grid with 3600 locations, based on exceedances for locally intense and for spatially accumulated rainfall, and discuss diagnostics of model fit. The differences between the two fitted models highlight how the definition of rare events affects the estimated dependence structure.
      PubDate: Thu, 21 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy026
      Issue No: Vol. 105, No. 3 (2018)
       
  • Asymptotic properties of approximate Bayesian computation
    • Authors: Frazier D; Martin G, Robert C, et al.
      Pages: 593 - 607
      Abstract: SummaryApproximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance used within the method, mild regularity conditions on the summary statistics, and a condition linked to identification of the true parameters. Implications for practitioners are discussed.
      PubDate: Wed, 06 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy027
      Issue No: Vol. 105, No. 3 (2018)
       
  • Variance estimation in the particle filter
    • Authors: Lee A; Whiteley N.
      Pages: 609 - 625
      Abstract: SummaryThis paper concerns numerical assessment of Monte Carlo error in particle filters. We show that by keeping track of certain key features of the genealogical structure arising from resampling operations, it is possible to estimate variances of a number of Monte Carlo approximations that particle filters deliver. All our estimators can be computed from a single run of a particle filter. We establish that, as the number of particles grows, our estimators are weakly consistent for asymptotic variances of the Monte Carlo approximations and some of them are also non-asymptotically unbiased. The asymptotic variances can be decomposed into terms corresponding to each time step of the algorithm, and we show how to estimate each of these terms consistently. When the number of particles may vary over time, this allows approximation of the asymptotically optimal allocation of particle numbers.
      PubDate: Tue, 26 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy028
      Issue No: Vol. 105, No. 3 (2018)
       
  • A structural break test for extremal dependence in β-mixing random
           vectors
    • Authors: Hoga Y.
      Pages: 627 - 643
      Abstract: SummaryWe derive a structural break test for extremal dependence in $\beta$-mixing, possibly high-dimensional random vectors with either asymptotically dependent or asymptotically independent components. Existing tests require serially independent observations with asymptotically dependent components. To avoid estimating a long-run variance, we use self-normalization, which obviates the need to estimate the coefficient of tail dependence when components are asymptotically independent. Simulations show favourable empirical size and power of the test, which we apply to S&P 500 and DAX log-returns. We find evidence for one break in the coefficient of tail dependence for the upper and lower joint tail at the beginning of the 2007–08 financial crisis, leading to more extremal co-movement.
      PubDate: Thu, 28 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy030
      Issue No: Vol. 105, No. 3 (2018)
       
  • Asymptotic post-selection inference for the Akaike information criterion
    • Authors: Charkhi A; Claeskens G.
      Pages: 645 - 664
      Abstract: SummaryIgnoring the model selection step in inference after selection is harmful. In this paper we study the asymptotic distribution of estimators after model selection using the Akaike information criterion. First, we consider the classical setting in which a true model exists and is included in the candidate set of models. We exploit the overselection property of this criterion in constructing a selection region, and we obtain the asymptotic distribution of estimators and linear combinations thereof conditional on the selected model. The limiting distribution depends on the set of competitive models and on the smallest overparameterized model. Second, we relax the assumption on the existence of a true model and obtain uniform asymptotic results. We use simulation to study the resulting post-selection distributions and to calculate confidence regions for the model parameters, and we also apply the method to a diabetes dataset.
      PubDate: Sun, 03 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy018
      Issue No: Vol. 105, No. 3 (2018)
       
  • A semiparametric extension of the stochastic block model for longitudinal
           networks
    • Authors: Matias C; Rebafka T, Villers F.
      Pages: 665 - 680
      Abstract: SummaryWe propose an extension of the stochastic block model for recurrent interaction events in continuous time, where every individual belongs to a latent group and conditional interactions between two individuals follow an inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. We show that the model is identifiable and estimate it with a semiparametric variational expectation-maximization algorithm. We develop two versions of the method, one using a nonparametric histogram approach with an adaptive choice of the partition size, and the other using kernel intensity estimators. We select the number of latent groups by an integrated classification likelihood criterion. We demonstrate the performance of our procedure on synthetic experiments, analyse two datasets to illustrate the utility of our approach, and comment on competing methods.
      PubDate: Sun, 03 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy016
      Issue No: Vol. 105, No. 3 (2018)
       
  • Local polynomial regression with correlated errors in random design and
           unknown correlation structure
    • Authors: De Brabanter K; Cao F, Gijbels I, et al.
      Pages: 681 - 690
      Abstract: SummaryAutomated or data-driven bandwidth selection methods tend to break down in the presence of correlated errors. While this problem has previously been studied in the fixed design setting for kernel regression, the results were applicable only when there is knowledge about the correlation structure. This article generalizes these results to the random design setting and addresses the problem in situations where no prior knowledge about the correlation structure is available. We establish the asymptotic optimality of our proposed bandwidth selection criterion based on kernels $K$ satisfying $K(0)=0$.
      PubDate: Wed, 06 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy025
      Issue No: Vol. 105, No. 3 (2018)
       
  • Bayesian spatial monotonic multiple regression
    • Authors: Rohrbeck C; Costain D, Frigessi A.
      Pages: 691 - 707
      Abstract: SummaryWe consider monotonic, multiple regression for contiguous regions. The regression functions vary regionally and may exhibit spatial structure. We develop Bayesian nonparametric methodology that permits estimation of both continuous and discontinuous functional shapes using marked point process and reversible jump Markov chain Monte Carlo techniques. Spatial dependence is incorporated by a flexible prior distribution which is tuned using crossvalidation and Bayesian optimization. We derive the mean and variance of the prior induced by the marked point process approach. Asymptotic results show consistency of the estimated functions. Posterior realizations enable variable selection, the detection of discontinuities and prediction. In simulations and in an application to a Norwegian insurance dataset, our method shows better performance than existing approaches.
      PubDate: Sun, 03 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy019
      Issue No: Vol. 105, No. 3 (2018)
       
  • Covariate association eliminating weights: a unified weighting framework
           for causal effect estimation
    • Authors: Yiu S; Su L.
      Pages: 709 - 722
      Abstract: SummaryWeighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimation by eliminating the associations between these covariates and treatment assignment characterized in a chosen treatment assignment model after weighting. The moment conditions in covariate balancing weight methods for binary, categorical and continuous treatments in cross-sectional settings are special cases of the conditions in our framework, which extends to longitudinal settings. Simulation shows that our method gives treatment effect estimates with smaller biases and variances than the maximum likelihood approach under treatment assignment model misspecification. We illustrate our method with an application to systemic lupus erythematosus data.
      PubDate: Wed, 25 Apr 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy015
      Issue No: Vol. 105, No. 3 (2018)
       
  • Targeted learning ensembles for optimal individualized treatment rules
           with time-to-event outcomes
    • Authors: Díaz I; Savenkov O, Ballman K.
      Pages: 723 - 738
      Abstract: SummaryWe consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying the expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which arise from considering two loss functions aimed at directly estimating the conditional treatment effect and recasting the problem in terms of weighted classification using the 0-1 loss function. Our estimated rules are ensembles that minimize the crossvalidated risk of a linear combination in a user-supplied library of candidate estimators. We prove oracle inequalities bounding the finite-sample excess risk of the estimator. The bounds depend on the excess risk of the oracle selector and a doubly robust term related to estimation of the nuisance parameters. We discuss the convergence rates of our estimator to the oracle selector, and illustrate our methods by analysis of a phase III randomized study testing the efficacy of a new therapy for the treatment of breast cancer.
      PubDate: Mon, 07 May 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy017
      Issue No: Vol. 105, No. 3 (2018)
       
  • On Bayes factors for the linear model
    • Authors: Shively T; Walker S.
      Pages: 739 - 744
      Abstract: SummaryWe show that the Bayes factor for testing whether a subset of coefficients are zero in the normal linear regression model gives the uniformly most powerful test amongst the class of invariant tests discussed in Lehmann & Romano (2005) if the prior distributions for the regression coefficients are in a specific class of distributions. The priors in this class can have any elliptical distribution, with a specific scale matrix, for the subset of coefficients that are being tested. We also show under mild conditions that the Bayes factor gives the uniformly most powerful invariant test only if the prior for the coefficients being tested is an elliptical distribution with this scale matrix. The implications are discussed.
      PubDate: Thu, 10 May 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy022
      Issue No: Vol. 105, No. 3 (2018)
       
  • Sequential rerandomization
    • Authors: Zhou Q; Ernst P, Morgan K, et al.
      Pages: 745 - 752
      Abstract: SummaryThe seminal work of Morgan & Rubin (2012) considers rerandomization for all the units at one time.In practice, however, experimenters may have to rerandomize units sequentially. For example, a clinician studying a rare disease may be unable to wait to perform an experiment until all the experimental units are recruited. Our work offers a mathematical framework for sequential rerandomization designs, where the experimental units are enrolled in groups. We formulate an adaptive rerandomization procedure for balancing treatment/control assignments over some continuous or binary covariates, using Mahalanobis distance as the imbalance measure. We prove in our key result that given the same number of rerandomizations, in expected value, under certain mild assumptions, sequential rerandomization achieves better covariate balance than rerandomization at one time.
      PubDate: Sun, 24 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy031
      Issue No: Vol. 105, No. 3 (2018)
       
  • Amendments and Corrections‘Information-theoretic optimality of
           observation-driven time series models for continuous responses’
    • Authors: Blasques F; Koopman S, Lucas A.
      Pages: 753 - 753
      Abstract: Biometrika (2015) 102, pp. 325–43.
      PubDate: Wed, 18 Jul 2018 00:00:00 GMT
      DOI: 10.1093/biomet/asy039
      Issue No: Vol. 105, No. 3 (2018)
       
 
 
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