Publisher: Oxford University Press   (Total: 412 journals)

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Showing 1 - 200 of 412 Journals sorted alphabetically
ACS Symposium Series     Full-text available via subscription   (Followers: 3, 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: 61, SJR: 2.196, CiteScore: 5)
Aesthetic Surgery J.     Hybrid Journal   (Followers: 8, SJR: 1.434, CiteScore: 1)
Aesthetic Surgery J. Open Forum     Open Access   (Followers: 1)
African Affairs     Hybrid Journal   (Followers: 74, SJR: 1.869, CiteScore: 2)
Age and Ageing     Hybrid Journal   (Followers: 96, SJR: 1.989, CiteScore: 4)
Alcohol and Alcoholism     Hybrid Journal   (Followers: 24, SJR: 1.376, CiteScore: 3)
American Entomologist     Hybrid Journal   (Followers: 8)
American Historical Review     Hybrid Journal   (Followers: 220, SJR: 0.467, CiteScore: 1)
American J. of Agricultural Economics     Hybrid Journal   (Followers: 54, SJR: 2.113, CiteScore: 3)
American J. of Clinical Nutrition     Hybrid Journal   (Followers: 232, SJR: 3.438, CiteScore: 6)
American J. of Epidemiology     Hybrid Journal   (Followers: 235, SJR: 2.713, CiteScore: 3)
American J. of Health-System Pharmacy     Full-text available via subscription   (Followers: 64, SJR: 0.595, CiteScore: 1)
American J. of Hypertension     Hybrid Journal   (Followers: 29, 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: 11, SJR: 0.116, CiteScore: 0)
American Law and Economics Review     Hybrid Journal   (Followers: 31, SJR: 1.053, CiteScore: 1)
American Literary History     Hybrid Journal   (Followers: 19, SJR: 0.391, CiteScore: 0)
Analysis     Hybrid Journal   (Followers: 25, SJR: 1.038, CiteScore: 1)
Animal Frontiers     Hybrid Journal   (Followers: 2)
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 15, SJR: 1.423, CiteScore: 3)
Annals of Botany     Hybrid Journal   (Followers: 38, SJR: 1.721, CiteScore: 4)
Annals of Oncology     Hybrid Journal   (Followers: 62, SJR: 5.599, CiteScore: 9)
Annals of the Entomological Society of America     Full-text available via subscription   (Followers: 11, SJR: 0.722, CiteScore: 1)
Annals of Work Exposures and Health     Hybrid Journal   (Followers: 11, SJR: 0.728, CiteScore: 2)
Antibody Therapeutics     Open Access   (Followers: 1)
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: 67, 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: 32, SJR: 0.731, CiteScore: 2)
Aristotelian Society Supplementary Volume     Hybrid Journal   (Followers: 2)
Arthropod Management Tests     Hybrid Journal   (Followers: 2)
Astronomy & Geophysics     Hybrid Journal   (Followers: 47, SJR: 0.146, CiteScore: 0)
Behavioral Ecology     Hybrid Journal   (Followers: 58, SJR: 1.871, CiteScore: 3)
Bioinformatics     Hybrid Journal   (Followers: 402, SJR: 6.14, CiteScore: 8)
Biology Methods and Protocols     Open Access   (Followers: 1)
Biology of Reproduction     Full-text available via subscription   (Followers: 11, SJR: 1.446, CiteScore: 3)
Biometrika     Hybrid Journal   (Followers: 20, SJR: 3.485, CiteScore: 2)
BioScience     Hybrid Journal   (Followers: 30, SJR: 2.754, CiteScore: 4)
Bioscience Horizons : The National Undergraduate Research J.     Open Access   (Followers: 3, SJR: 0.146, CiteScore: 0)
Biostatistics     Hybrid Journal   (Followers: 17, SJR: 1.553, CiteScore: 2)
BJA : British J. of Anaesthesia     Hybrid Journal   (Followers: 241, SJR: 2.115, CiteScore: 3)
BJA Education     Hybrid Journal   (Followers: 69)
Brain     Hybrid Journal   (Followers: 78, SJR: 5.858, CiteScore: 7)
Brain Communications     Open Access   (Followers: 3)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 54, 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: 42, SJR: 2.161, CiteScore: 2)
British J. of Aesthetics     Hybrid Journal   (Followers: 24, SJR: 0.508, CiteScore: 1)
British J. of Criminology     Hybrid Journal   (Followers: 623, SJR: 1.828, CiteScore: 3)
British J. of Social Work     Hybrid Journal   (Followers: 100, 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: 37)
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3, SJR: 1.376, CiteScore: 1)
Cambridge J. of Economics     Hybrid Journal   (Followers: 77, SJR: 0.764, CiteScore: 2)
Cambridge J. of Regions, Economy and Society     Hybrid Journal   (Followers: 13, SJR: 2.438, CiteScore: 4)
Cambridge Quarterly     Hybrid Journal   (Followers: 11, SJR: 0.104, CiteScore: 0)
Capital Markets Law J.     Hybrid Journal   (Followers: 4, SJR: 0.222, CiteScore: 0)
Carcinogenesis     Hybrid Journal   (Followers: 2, SJR: 2.135, CiteScore: 5)
Cardiovascular Research     Hybrid Journal   (Followers: 16, SJR: 3.002, CiteScore: 5)
Cerebral Cortex     Hybrid Journal   (Followers: 56, SJR: 3.892, CiteScore: 6)
CESifo Economic Studies     Hybrid Journal   (Followers: 24, SJR: 0.483, CiteScore: 1)
Chemical Senses     Hybrid Journal   (Followers: 1, SJR: 1.42, CiteScore: 3)
Children and Schools     Hybrid Journal   (Followers: 8, SJR: 0.246, CiteScore: 0)
Chinese J. of Comparative Law     Hybrid Journal   (Followers: 6, SJR: 0.412, CiteScore: 0)
Chinese J. of Intl. Law     Hybrid Journal   (Followers: 24, SJR: 0.329, CiteScore: 0)
Chinese J. of Intl. Politics     Hybrid Journal   (Followers: 11, 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: 30, SJR: 0.123, CiteScore: 0)
Clean Energy     Open Access   (Followers: 3)
Clinical Infectious Diseases     Hybrid Journal   (Followers: 81, SJR: 5.051, CiteScore: 5)
Communication Theory     Hybrid Journal   (Followers: 29, SJR: 2.424, CiteScore: 3)
Communication, Culture & Critique     Hybrid Journal   (Followers: 29, SJR: 0.222, CiteScore: 1)
Community Development J.     Hybrid Journal   (Followers: 28, 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: 12, SJR: 0.121, CiteScore: 0)
Contributions to Political Economy     Hybrid Journal   (Followers: 8, SJR: 0.906, CiteScore: 1)
Critical Values     Full-text available via subscription  
Current Developments in Nutrition     Open Access   (Followers: 5)
Current Legal Problems     Hybrid Journal   (Followers: 29)
Current Zoology     Full-text available via subscription   (Followers: 6, SJR: 1.164, CiteScore: 2)
Database : The J. of Biological Databases and Curation     Open Access   (Followers: 10, SJR: 1.791, CiteScore: 3)
Digital Scholarship in the Humanities     Hybrid Journal   (Followers: 15, SJR: 0.259, CiteScore: 1)
Diplomatic History     Hybrid Journal   (Followers: 25, SJR: 0.45, CiteScore: 1)
DNA Research     Open Access   (Followers: 6, SJR: 2.866, CiteScore: 6)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 5)
Early Music     Hybrid Journal   (Followers: 17, SJR: 0.139, CiteScore: 0)
Econometrics J.     Hybrid Journal   (Followers: 34, SJR: 2.926, CiteScore: 1)
Economic J.     Hybrid Journal   (Followers: 124, SJR: 5.161, CiteScore: 3)
Economic Policy     Hybrid Journal   (Followers: 51, SJR: 3.584, CiteScore: 3)
ELT J.     Hybrid Journal   (Followers: 27, SJR: 0.942, CiteScore: 1)
English Historical Review     Hybrid Journal   (Followers: 61, SJR: 0.612, CiteScore: 1)
English: J. of the English Association     Hybrid Journal   (Followers: 23, SJR: 0.1, CiteScore: 0)
Environmental Entomology     Full-text available via subscription   (Followers: 12, SJR: 0.818, CiteScore: 2)
Environmental Epigenetics     Open Access   (Followers: 2)
Environmental History     Hybrid Journal   (Followers: 28, 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: 24, SJR: 0.113, CiteScore: 0)
European Heart J.     Hybrid Journal   (Followers: 68, SJR: 9.315, CiteScore: 9)
European Heart J. - Cardiovascular Imaging     Hybrid Journal   (Followers: 10, 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   (Followers: 1)
European Heart J. Supplements     Hybrid Journal   (Followers: 7, 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: 242, SJR: 0.694, CiteScore: 1)
European J. of Orthodontics     Hybrid Journal   (Followers: 5, SJR: 1.279, CiteScore: 2)
European J. of Public Health     Hybrid Journal   (Followers: 23, SJR: 1.36, CiteScore: 2)
European Review of Agricultural Economics     Hybrid Journal   (Followers: 12, SJR: 1.172, CiteScore: 2)
European Review of Economic History     Hybrid Journal   (Followers: 31, SJR: 0.702, CiteScore: 1)
European Sociological Review     Hybrid Journal   (Followers: 46, 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: 19, SJR: 1.492, CiteScore: 4)
Fems Microbiology Letters     Hybrid Journal   (Followers: 29, SJR: 0.79, CiteScore: 2)
Fems Microbiology Reviews     Hybrid Journal   (Followers: 38, 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: 26, SJR: 1.425, CiteScore: 1)
Forest Science     Hybrid Journal   (Followers: 8, 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: 36, 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: 17, SJR: 2.578, CiteScore: 4)
Geophysical J. Intl.     Hybrid Journal   (Followers: 39, SJR: 1.506, CiteScore: 3)
German History     Hybrid Journal   (Followers: 27, SJR: 0.161, CiteScore: 0)
GigaScience     Open Access   (Followers: 6, SJR: 5.022, CiteScore: 7)
Global Summitry     Hybrid Journal   (Followers: 1)
Glycobiology     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Health and Social Work     Hybrid Journal   (Followers: 68, SJR: 0.388, CiteScore: 1)
Health Education Research     Hybrid Journal   (Followers: 20, SJR: 0.854, CiteScore: 2)
Health Policy and Planning     Hybrid Journal   (Followers: 26, SJR: 1.512, CiteScore: 2)
Health Promotion Intl.     Hybrid Journal   (Followers: 27, SJR: 0.812, CiteScore: 2)
History Workshop J.     Hybrid Journal   (Followers: 33, SJR: 1.278, CiteScore: 1)
Holocaust and Genocide Studies     Hybrid Journal   (Followers: 30, SJR: 0.105, CiteScore: 0)
Human Communication Research     Hybrid Journal   (Followers: 16, SJR: 2.146, CiteScore: 3)
Human Molecular Genetics     Hybrid Journal   (Followers: 11, SJR: 3.555, CiteScore: 5)
Human Reproduction     Hybrid Journal   (Followers: 76, SJR: 2.643, CiteScore: 5)
Human Reproduction Open     Open Access   (Followers: 1)
Human Reproduction Update     Hybrid Journal   (Followers: 18, SJR: 5.317, CiteScore: 10)
Human Rights Law Review     Hybrid Journal   (Followers: 69, SJR: 0.756, CiteScore: 1)
ICES J. of Marine Science: J. du Conseil     Hybrid Journal   (Followers: 59, SJR: 1.591, CiteScore: 3)
ICSID Review : Foreign Investment Law J.     Hybrid Journal   (Followers: 11)
ILAR J.     Hybrid Journal   (Followers: 3, 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: 12, SJR: 1.792, CiteScore: 2)
Industrial Law J.     Hybrid Journal   (Followers: 30, SJR: 0.249, CiteScore: 1)
Inflammatory Bowel Diseases     Hybrid Journal   (Followers: 45, SJR: 2.511, CiteScore: 4)
Information and Inference     Free  
Innovation in Aging     Open Access   (Followers: 1)
Insect Systematics and Diversity     Hybrid Journal  
Integrative and Comparative Biology     Hybrid Journal   (Followers: 10, SJR: 1.319, CiteScore: 2)
Integrative Biology     Full-text available via subscription   (Followers: 5, SJR: 1.36, CiteScore: 3)
Integrative Organismal Biology     Open Access  
Interacting with Computers     Hybrid Journal   (Followers: 10, SJR: 0.292, CiteScore: 1)
Interactive CardioVascular and Thoracic Surgery     Hybrid Journal   (Followers: 6, SJR: 0.762, CiteScore: 1)
Intl. Affairs     Hybrid Journal   (Followers: 73, SJR: 1.505, CiteScore: 3)
Intl. Data Privacy Law     Hybrid Journal   (Followers: 22)
Intl. Health     Hybrid Journal   (Followers: 7, SJR: 0.851, CiteScore: 2)
Intl. Immunology     Hybrid Journal   (Followers: 4, SJR: 2.167, CiteScore: 4)
Intl. J. for Quality in Health Care     Hybrid Journal   (Followers: 40, SJR: 1.348, CiteScore: 2)
Intl. J. of Constitutional Law     Hybrid Journal   (Followers: 59, SJR: 0.601, CiteScore: 1)
Intl. J. of Epidemiology     Hybrid Journal   (Followers: 296, 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: 21, 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: 11, SJR: 1.545, CiteScore: 1)
Intl. J. of Refugee Law     Hybrid Journal   (Followers: 39, SJR: 0.389, CiteScore: 1)
Intl. J. of Transitional Justice     Hybrid Journal   (Followers: 14, SJR: 0.724, CiteScore: 2)
Intl. Mathematics Research Notices     Hybrid Journal   (Followers: 1, SJR: 2.168, CiteScore: 1)
Intl. Political Sociology     Hybrid Journal   (Followers: 41, SJR: 1.465, CiteScore: 3)
Intl. Relations of the Asia-Pacific     Hybrid Journal   (Followers: 26, SJR: 0.401, CiteScore: 1)
Intl. Studies Perspectives     Hybrid Journal   (Followers: 9, SJR: 0.983, CiteScore: 1)
Intl. Studies Quarterly     Hybrid Journal   (Followers: 55, SJR: 2.581, CiteScore: 2)
Intl. Studies Review     Hybrid Journal   (Followers: 24, 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: 18, SJR: 0.533, CiteScore: 1)
J. of American History     Hybrid Journal   (Followers: 56, SJR: 0.297, CiteScore: 1)
J. of Analytical Toxicology     Hybrid Journal   (Followers: 15, SJR: 1.065, CiteScore: 2)
J. of Antimicrobial Chemotherapy     Hybrid Journal   (Followers: 17, SJR: 2.419, CiteScore: 4)
J. of Antitrust Enforcement     Hybrid Journal   (Followers: 2)
J. of Biochemistry     Hybrid Journal   (Followers: 46, SJR: 1.226, CiteScore: 2)
J. of Breast Imaging     Full-text available via subscription   (Followers: 2)

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Similar Journals
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  [412 journals]
  • Network cross-validation by edge sampling
    • Authors: Li T; Levina E, Zhu J.
      Pages: 257 - 276
      Abstract: SummaryWhile many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is applicable to cross-validation for a wide range of network model selection tasks. We provide theoretical justification for our method in a general setting and examples of how the method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a statisticians’ citation network show that the proposed cross-validation approach works well for model selection.
      PubDate: Sat, 04 Apr 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa006
      Issue No: Vol. 107, No. 2 (2020)
       
  • Discussion of ‘Network cross-validation by edge sampling’
    • Authors: Chang J; Kolaczyk E, Yao Q.
      Pages: 277 - 280
      PubDate: Fri, 15 May 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa017
      Issue No: Vol. 107, No. 2 (2020)
       
  • Discussion of ‘Network cross-validation by edge sampling’
    • Authors: Gao C; Ma Z.
      Pages: 281 - 284
      PubDate: Fri, 15 May 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa022
      Issue No: Vol. 107, No. 2 (2020)
       
  • Discussion of ‘Network cross-validation by edge sampling’
    • Authors: Lei J; Lin K.
      Pages: 285 - 287
      PubDate: Fri, 15 May 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa009
      Issue No: Vol. 107, No. 2 (2020)
       
  • Rejoinder: ‘Network cross-validation by edge sampling’
    • Authors: Li T; Levina E, Zhu J.
      Pages: 289 - 292
      PubDate: Fri, 15 May 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa021
      Issue No: Vol. 107, No. 2 (2020)
       
  • Adaptive nonparametric regression with the K-nearest neighbour fused lasso
    • Authors: Madrid Padilla O; Sharpnack J, Chen Y, et al.
      Pages: 293 - 310
      Abstract: SummaryThe fused lasso, also known as total-variation denoising, is a locally adaptive function estimator over a regular grid of design points. In this article, we extend the fused lasso to settings in which the points do not occur on a regular grid, leading to a method for nonparametric regression. This approach, which we call the $K$-nearest-neighbours fused lasso, involves computing the $K$-nearest-neighbours graph of the design points and then performing the fused lasso over this graph. We show that this procedure has a number of theoretical advantages over competing methods: specifically, it inherits local adaptivity from its connection to the fused lasso, and it inherits manifold adaptivity from its connection to the $K$-nearest-neighbours approach. In a simulation study and an application to flu data, we show that excellent results are obtained. For completeness, we also study an estimator that makes use of an $\epsilon$-graph rather than a $K$-nearest-neighbours graph and contrast it with the $K$-nearest-neighbours fused lasso.
      PubDate: Wed, 29 Jan 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz071
      Issue No: Vol. 107, No. 2 (2020)
       
  • Classification with imperfect training labels
    • Authors: Cannings T; Fan Y, Samworth R.
      Pages: 311 - 330
      Abstract: SummaryWe study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour, support vector machine and linear discriminant analysis classifiers. One consequence of these results is that the $k$-nearest neighbour and support vector machine classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risk of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods. The linear discriminant analysis classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of the classes are equal. Our theoretical results are supported by a simulation study.
      PubDate: Wed, 22 Apr 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa011
      Issue No: Vol. 107, No. 2 (2020)
       
  • Testing conditional mean independence for functional data
    • Authors: Lee C; Zhang X, Shao X.
      Pages: 331 - 346
      Abstract: SummaryWe propose a new nonparametric conditional mean independence test for a response variable $Y$ and a predictor variable $X$ where either or both can be function-valued. Our test is built on a new metric, the so-called functional martingale difference divergence, which fully characterizes the conditional mean dependence of $Y$ given $X$ and extends the martingale difference divergence proposed by Shao & Zhang (2014). We define an unbiased estimator of functional martingale difference divergence by using a $\mathcal{U}$-centring approach, and we obtain its limiting null distribution under mild assumptions. Since the limiting null distribution is not pivotal, we use the wild bootstrap method to estimate the critical value and show the consistency of the bootstrap test. Our test can detect the local alternative which approaches the null at the rate of $n^{-1/2}$ with a nontrivial power, where $n$ is the sample size. Unlike the three tests developed by Kokoszka et al. (2008), Lei (2014) and Patilea et al. (2016), our test does not require a finite-dimensional projection or assume a linear model, and it does not involve any tuning parameters. Promising finite-sample performance is demonstrated via simulations, and a real-data illustration is used to compare our test with existing ones.
      PubDate: Mon, 27 Jan 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz070
      Issue No: Vol. 107, No. 2 (2020)
       
  • The essential histogram
    • Authors: Li H; Munk A, Sieling H, et al.
      Pages: 347 - 364
      Abstract: SummaryThe histogram is widely used as a simple, exploratory way of displaying data, but it is usually not clear how to choose the number and size of the bins. We construct a confidence set of distribution functions that optimally deal with the two main tasks of the histogram: estimating probabilities and detecting features such as increases and modes in the distribution. We define the essential histogram as the histogram in the confidence set with the fewest bins. Thus the essential histogram is the simplest visualization of the data that optimally achieves the main tasks of the histogram. The only assumption we make is that the data are independent and identically distributed. We provide a fast algorithm for computing the essential histogram and illustrate our method with examples.
      PubDate: Tue, 11 Feb 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz081
      Issue No: Vol. 107, No. 2 (2020)
       
  • Discontinuous Hamiltonian Monte Carlo for discrete parameters and
           discontinuous likelihoods
    • Authors: Nishimura A; Dunson D, Lu J.
      Pages: 365 - 380
      Abstract: SummaryHamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article we present an extension that can efficiently explore target distributions with discontinuous densities. Our extension in particular enables efficient sampling from ordinal parameters through the embedding of probability mass functions into continuous spaces. We motivate our approach through a theory of discontinuous Hamiltonian dynamics and develop a corresponding numerical solver. The proposed solver is the first of its kind, with a remarkable ability to exactly preserve the Hamiltonian. We apply our algorithm to challenging posterior inference problems to demonstrate its wide applicability and competitive performance.
      PubDate: Sat, 07 Mar 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz083
      Issue No: Vol. 107, No. 2 (2020)
       
  • On the use of approximate Bayesian computation Markov chain Monte Carlo
           with inflated tolerance and post-correction
    • Authors: Vihola M; Franks J.
      Pages: 381 - 395
      Abstract: SummaryApproximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure sufficient mixing and post-processing the output, leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators and propose an adaptive approximate Bayesian computation Markov chain Monte Carlo algorithm, which finds a balanced tolerance level automatically based on acceptance rate optimization. Our experiments show that post-processing-based estimators can perform better than direct Markov chain Monte Carlo targeting a fine tolerance, that our confidence intervals are reliable, and that our adaptive algorithm leads to reliable inference with little user specification.
      PubDate: Mon, 03 Feb 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz078
      Issue No: Vol. 107, No. 2 (2020)
       
  • Lassoing eigenvalues
    • Authors: Tyler D; Yi M.
      Pages: 397 - 414
      Abstract: SummaryThe properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of nonsmooth penalty functions for the sample covariance matrix and demonstrate how their use results in a grouping of the estimated eigenvalues. We refer to the proposed method as lassoing eigenvalues, or the elasso.
      PubDate: Tue, 11 Feb 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz076
      Issue No: Vol. 107, No. 2 (2020)
       
  • Doubly functional graphical models in high dimensions
    • Authors: Qiao X; Qian C, James G, et al.
      Pages: 415 - 431
      Abstract: SummaryWe consider estimating a functional graphical model from multivariate functional observations. In functional data analysis, the classical assumption is that each function has been measured over a densely sampled grid. However, in practice the functions have often been observed, with measurement error, at a relatively small number of points. We propose a class of doubly functional graphical models to capture the evolving conditional dependence relationship among a large number of sparsely or densely sampled functions. Our approach first implements a nonparametric smoother to perform functional principal components analysis for each curve, then estimates a functional covariance matrix and finally computes sparse precision matrices, which in turn provide the doubly functional graphical model. We derive some novel concentration bounds, uniform convergence rates and model selection properties of our estimator for both sparsely and densely sampled functional data in the high-dimensional large-$p$, small-$n$ regime. We demonstrate via simulations that the proposed method significantly outperforms possible competitors. Our proposed method is applied to a brain imaging dataset.
      PubDate: Tue, 11 Feb 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz072
      Issue No: Vol. 107, No. 2 (2020)
       
  • Ensemble estimation and variable selection with semiparametric regression
           models
    • Authors: Shin S; Liu Y, Cole S, et al.
      Pages: 433 - 448
      Abstract: SummaryWe consider scenarios in which the likelihood function for a semiparametric regression model factors into separate components, with an efficient estimator of the regression parameter available for each component. An optimal weighted combination of the component estimators, named an ensemble estimator, may be employed as an overall estimate of the regression parameter, and may be fully efficient under uncorrelatedness conditions. This approach is useful when the full likelihood function may be difficult to maximize, but the components are easy to maximize. It covers settings where the nuisance parameter may be estimated at different rates in the component likelihoods. As a motivating example we consider proportional hazards regression with prospective doubly censored data, in which the likelihood factors into a current status data likelihood and a left-truncated right-censored data likelihood. Variable selection is important in such regression modelling, but the applicability of existing techniques is unclear in the ensemble approach. We propose ensemble variable selection using the least squares approximation technique on the unpenalized ensemble estimator, followed by ensemble re-estimation under the selected model. The resulting estimator has the oracle property such that the set of nonzero parameters is successfully recovered and the semiparametric efficiency bound is achieved for this parameter set. Simulations show that the proposed method performs well relative to alternative approaches. Analysis of an AIDS cohort study illustrates the practical utility of the method.
      PubDate: Wed, 15 Apr 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa012
      Issue No: Vol. 107, No. 2 (2020)
       
  • Estimation from cross-sectional data under a semiparametric truncation
           model
    • Authors: Heuchenne C; De Uña-Álvarez J, Laurent G.
      Pages: 449 - 465
      Abstract: SummaryCross-sectional sampling is often used when investigating inter-event times, resulting in left-truncated and right-censored data. In this paper, we consider a semiparametric truncation model in which the truncating variable is assumed to belong to a certain parametric family. We examine two methods of estimating both the truncation and the lifetime distributions. We obtain asymptotic representations of the estimators for the lifetime distribution and establish their weak convergence. Both of the proposed estimators perform better than Wang’s (1991) nonparametric maximum likelihood estimator in terms of the integrated mean squared error, when the parametric family for the truncation is sufficiently close to its true distribution. The full likelihood approach is preferable to the conditional likelihood approach in estimating the lifetime distribution, though not necessarily the truncation distribution. In an application to Alzheimer’s disease data, hypothesis tests reject the uniform truncation distribution, but several other parametric models lead to similar behaviour of the truncation and lifetime distributions after disease onset.
      PubDate: Sat, 11 Apr 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asaa002
      Issue No: Vol. 107, No. 2 (2020)
       
  • Robust empirical Bayes small area estimation with density power divergence
    • Authors: Sugasawa S.
      Pages: 467 - 480
      Abstract: SummaryA two-stage normal hierarchical model called the Fay–Herriot model and the empirical Bayes estimator are widely used to obtain indirect and model-based estimates of means in small areas. However, the performance of the empirical Bayes estimator can be poor when the assumed normal distribution is misspecified. This article presents a simple modification that makes use of density power divergence and proposes a new robust empirical Bayes small area estimator. The mean squared error and estimated mean squared error of the proposed estimator are derived based on the asymptotic properties of the robust estimator of the model parameters. We investigate the numerical performance of the proposed method through simulations and an application to survey data.
      PubDate: Wed, 29 Jan 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz075
      Issue No: Vol. 107, No. 2 (2020)
       
  • Estimation of error variance via ridge regression
    • Authors: Liu X; Zheng S, Feng X.
      Pages: 481 - 488
      Abstract: SummaryWe propose a novel estimator of error variance and establish its asymptotic properties based on ridge regression and random matrix theory. The proposed estimator is valid under both low- and high-dimensional models, and performs well not only in nonsparse cases, but also in sparse ones. The finite-sample performance of the proposed method is assessed through an intensive numerical study, which indicates that the method is promising compared with its competitors in many interesting scenarios.
      PubDate: Mon, 27 Jan 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz074
      Issue No: Vol. 107, No. 2 (2020)
       
  • On the marginal likelihood and cross-validation
    • Authors: Fong E; Holmes C.
      Pages: 489 - 496
      Abstract: SummaryIn Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through $k$-fold partitioning or leave-$p$-out subsampling. We show that the marginal likelihood is formally equivalent to exhaustive leave-$p$-out crossvalidation averaged over all values of $p$ and all held-out test sets when using the log posterior predictive probability as the scoring rule. Moreover, the log posterior predictive score is the only coherent scoring rule under data exchangeability. This offers new insight into the marginal likelihood and cross-validation, and highlights the potential sensitivity of the marginal likelihood to the choice of the prior. We suggest an alternative approach using cumulative cross-validation following a preparatory training phase. Our work has connections to prequential analysis and intrinsic Bayes factors, but is motivated in a different way.
      PubDate: Fri, 24 Jan 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz077
      Issue No: Vol. 107, No. 2 (2020)
       
  • Consistency for the tree bootstrap in respondent-driven sampling
    • Authors: Green A; McCormick T, Raftery A.
      Pages: 497 - 504
      Abstract: SummaryRespondent-driven sampling is an approach for estimating features of populations that are difficult to access using standard survey tools, e.g., the fraction of injection drug users who are HIV positive. Baraff et al. (2016) introduced an approach to estimating uncertainty in population proportion estimates from respondent-driven sampling using the tree bootstrap method. In this paper we establish the consistency of this tree bootstrap approach in the case of $m$-trees.
      PubDate: Fri, 24 Jan 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz067
      Issue No: Vol. 107, No. 2 (2020)
       
  • A random-perturbation-based rank estimator of the number of factors
    • Authors: Kong X.
      Pages: 505 - 511
      Abstract: SummaryWe introduce a random-perturbation-based rank estimator of the number of factors of a large-dimensional approximate factor model. An expansion of the rank estimator demonstrates that the random perturbation reduces the biases due to the persistence of the factor series and the dependence between the factor and error series. A central limit theorem for the rank estimator with convergence rate higher than root $n$ gives a new hypothesis-testing procedure for both one-sided and two-sided alternatives. Simulation studies verify the performance of the test.
      PubDate: Mon, 03 Feb 2020 00:00:00 GMT
      DOI: 10.1093/biomet/asz073
      Issue No: Vol. 107, No. 2 (2020)
       
 
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