2020

Volume 88, n° S1, December 2020

Special Issue:“Data Science versus Classical Inference: Prediction, Estimation, and Attribution”, honouring Prof. Brad Efron's International Prize in Statistics in 2019

  • Bühlmann, P. & Ćevid, D. - Deconfounding and Causal Regularisation for Stability and External Validity, pp. S114-S134.
  • Candès, E. & Sabatti, C. - Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron, pp. S60-S63.
  • Clark, S., Hyndman, R.J., Pagendam, D., & Ryan, L.M. - Modern Strategies for Time Series Regression, pp. S179-S204.
  • Cox, D.R. - Discussion of Paper by Brad Efron, pp. S64-S64.
  • Cressie, N. - Comment: When Is It Data Science and When Is It Data Engineering?, pp. S65-S69.
  • Davison, A.C. - Discussion, pp. S70-S72.
  • Dwivedi, R., Tan, Y.S., Park, B., Wei, M., Horgan, K., Madigan, D., & Yu, B. - Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies, pp. S135-S178.
  • Efron, B. (2020a) Prediction, Estimation, and Attribution, pp. S28-S59.
  • Efron, B. (2020b) Rejoinder, pp. S87-S90.
  • Friedman, J., Hastie, T., & Tibshirani, R. - Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron, pp. S73-S74.
  • Narasimhan, B. - An Interview with Bradley Efron, pp. S2-S27.
  • Tansey, W., Wang, Y., Rabadan, R., & Blei, D. - Double Empirical Bayes Testing, pp. S91-S113.
  • Tay, J.K. & Tibshirani, R. - Reluctant Generalised Additive Modelling, pp. S205-S224.
  • Xie, M. & Zheng, Z. - Discussion of Professor Bradley Efron’s Article on “Prediction, Estimation, and Attribution, pp. S75-S82.
  • Yu, B. & Barter, R. - The Data Science Process: One Culture, pp. S83-S86.
( résumés n° S1/2020)

Volume 88, n° 3, December 2020

  • Castelletti F. - Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures, pp. 752-775.
  • Flournoy N., Moler J., Plo, F. - Performance Measures in Dose-Finding Experiments, pp. 728-751.
  • García‐Pérez J., López‐Martín M., del M. García‐García, C., Salmerón‐Gómez R. - A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies, pp. 776-792.
  • Genest C., Nešlehová J.G. - A Conversation With Paul Embrechts, pp. 521-547.
  • Gijbels I., Karim, R., Verhasselt A. - Response to the Letter to the Editor on ‘On Quantile-based Asymmetric Family of Distributions: Properties and Inference, pp. 797-801.
  • Górecki T., Horváth L., Kokoszka P. - Tests of Normality of Functional Data, pp. 677-697.
  • Hedlin D. - Is there a “safe area” where the nonresponse rate has only a modest effect on bias despite non-ignorable nonresponse?, pp. 642-657.
  • Jasra A., Law K., Suciu C. - Advanced Multilevel Monte Carlo Methods, pp. 548-579.
  • Kang X., Deng X., Tsui K.-W., Pourahmadi M. - On variable ordination of modified Cholesky decomposition for estimating time-varying covariance matrices, pp. 616-641.
  • Kuk A.Y.C. - A Non-Proportional Hazards Model with Hazard Ratio Functions Free from Covariate Values, pp. 715-727.
  • Lee M., Su Z. - A Review of Envelope Models, pp. 658-676.
  • Modarres R. - Graphical Comparison of High-Dimensional Distributions, pp. 698-714.
  • Pace L., Salvan A. - Likelihood, Replicability and Robbins’ Confidence Sequences, pp. 599-615.
  • Rubio Alvarez F.J. - Letter to the Editor: ‘On Quantile-based Asymmetric Family of Distributions: Properties and Inference, pp. 793-796.
  • Steorts R.C., Schmid T., Tzavidis N. - Smoothing and Benchmarking for Small Area Estimation, pp. 580-598.

Book Review

  • Chakraborty B. - Statistical Remedies for Medical Researchers Peter F. Thall Springer, 2020, xi + 291 pages, £ 79.99/$109.99, hardcover ISBN: 978-3-030-43713-8, pp. 802-804.
  • Ghosh D. - Statistical Inference via Convex Optimization Nillas, Alice Anatoli Juditsky and Arkadi Nemirovski Princeton University Press, 2020, xiv + 656 pages, £ 70/$85, hardcover ISBN: 978-0-6911-9729-6, pp. 806-808.
  • Khattree R. - End-to-End Data Science with SAS: A Hands-On Programming Guide James Gearheart SAS Press, 2020, xiii+363 pages, £ 23.99$29.99, ebook ISBN: 978-1-64295-806-5, pp. 809-812.
  • O’Brien C.M. - Sampling and Estimation from Finite PopulationsYves TilléWiley, 2020, xxviii + 419 pages, £ 65/$100, hardcover ISBN: 978-0-470-68205-0, pp. 804-806.
  • Qiwei Y. - Time Series: A First Course With Bootstrap Starter Tucker S. McElroy and Dimitris N. Politis CRC Press, 2020, xix + 566 pages, £ 74.99/$99.95, hardcover ISBN 978-1-4398-7561-0, pp. 808-809.

 

(résumé n° 3/2020)

Volume 88, n° 2, August 2020

  • Bhadra A., Datta J., Li Y., Polson N. - Horseshoe Regularisation for Machine Learning in Complex and Deep Models1, pp. 302-320.
  • Billard L., Wallman K.K. - Women Trailblazers in the Statistical Profession, pp. 280-301.
  • Dellaportas P., Stephens D.A. - Interview with Professor Adrian FM Smith, pp. 265-279.
  • Dunson D., Papamarkou T. - Discussions, pp. 321-324.
  • Farr A.C., Mengersen K., Ruggeri F., Simpson D., Wu P., Yarlagadda P. - Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach, pp. 335-353.
  • Ghosh S., Doshi‐Velez F. - Discussion, pp. 324-326.
  • Gramacy R.B. - Discussion, pp. 326-329.
  • Holan S.H., Ravishanker N., Rao J.S. - Foreword: COVID-19 Mini-issue—Statistical Primers, pp. 396-397.
  • Hooten M., Wikle C., Schwob M. - Statistical Implementations of Agent-Based Demographic Models, pp. 441-461.
  • Loyal J.D., Chen Y. - Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic, pp. 419-440.
  • Nagaraja C.H., Nagaraja H.N. - Correction to ‘Distribution-free Approximate Methods for Constructing Confidence Intervals for Quantiles, pp. 519-519.
  • Narisetty N.N. - Discussion, pp. 330-334.
  • Tang L., Zhou Y., Wang L., Purkayastha S., Zhang L., He J., Wang F., Song P.X.-K. - A Review of Multi-Compartment Infectious Disease Models, pp. 462-513.
  • Wakefield J., Okonek T., Pedersen J. - Small Area Estimation for Disease Prevalence Mapping, pp. 398-418.
  • Youngjo L., Kim G. - Properties of h-Likelihood Estimators in Clustered Data, pp. 380-395.

Book Review

  • Blumberg C.J. - Bayesian Statistics for Beginners: A Step-by-Step Approach Therese M. Donovan and Ruth M. Mickey Oxford University Press, 2019, viii + 419 pages, $100, hardcover ISBN: 978-0-19-884129-6, pp. 514-515.
  • Merikoski J.K. - Book Review: Random Circulant Matrices, Arup Bose and Koushik Saha, CRC Press, 2019, xix + 192 pages, $174.95, hardcover ISBN: 978-1-1383-5109-7, pp. 520-520.
  • Liu S. - A Concise Introduction to Machine Learning A.C. FaulCRC Press, 2019, 314 pages, £46.99, paperback ISBN: 978-0-8153-8410-6, pp. 517-518.
  • Nummi T. - Semiparametric Regression with R Jaroslaw Harezlak, David Ruppert and Matt P. Wand Springer, 2018, xi + 331 pages, €85.39, ebook ISBN: 978-1-4939-8853-2, pp. 516-516.
  • Smith D. - Re-identification in the Absence of Common Variables for Matching, pp. 354-379.
  • Vardeman S.B. - Machine Learning: A Concise Introduction Steven W. Knox John Wiley & Sons, 2018, 352 pages, $99.95, hardcover ISBN: 978-1-119-43919-6, pp. 515-516.

(résumés n° 2/2020)

Volume 88, n°1, April 2020  

  • Berger Y. G., Kabzińska E. - Empirical Likelihood Approach for Aligning Information from Multiple Surveys, pp. 54‑74.
  • Brakel J. van den, Zhang X. (Mark), Tam S.-M. - Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys, pp. 155‑175.
  • Branson Z., Dasgupta T. - Sampling-based Randomised Designs for Causal Inference under the Potential Outcomes Framework, pp. 101‑121.
  • Chacón J. E. - The Modal Age of Statistics, pp. 122‑141.
  • Hobolth A., Guo Q., Kousholt A., Jensen J. L. - A Unifying Framework and Comparison of Algorithms for Non-negative Matrix Factorisation, pp. 29‑53.
  • Lian H. - Asymptotics of the Non-parametric Function for B-splines-based Estimation in Partially Linear Models, pp. 142‑154.
  • Moretti A., Shlomo N., Sakshaug J. W. - Multivariate Small Area Estimation of Multidimensional Latent Economic Well-being Indicators, pp. 1‑28.
  • NInternational Statistical Review, volume 88, n°1, April 2020  
  • Berger Y. G., Kabzińska E. - Empirical Likelihood Approach for Aligning Information from Multiple Surveys, pp. 54‑74.
  • Brakel J. van den, Zhang X. (Mark), Tam S.-M. - Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys, pp. 155‑175.
  • Branson Z., Dasgupta T. - Sampling-based Randomised Designs for Causal Inference under the Potential Outcomes Framework, pp. 101‑121.
  • Chacón J. E. - The Modal Age of Statistics, pp. 122‑141.
  • Hobolth A., Guo Q., Kousholt A., Jensen J. L. - A Unifying Framework and Comparison of Algorithms for Non-negative Matrix Factorisation, pp. 29‑53.
  • Lian H. - Asymptotics of the Non-parametric Function for B-splines-based Estimation in Partially Linear Models, pp. 142‑154.
  • Moretti A., Shlomo N., Sakshaug J. W. - Multivariate Small Area Estimation of Multidimensional Latent Economic Well-being Indicators, pp. 1‑28.
  • Nagaraja C. H., Nagaraja H. N. - Distribution-free Approximate Methods for Constructing Confidence Intervals for Quantiles, pp. 75‑100.
  • Waal T. de, Delden A. van, Scholtus S. - Multi-source Statistics: Basic Situations and Methods, pp. 203‑228.
  • Yu C., Yao W., Yang G. - A Selective Overview and Comparison of Robust Mixture Regression Estimators, pp. 176‑202.
  • Yüzbaşı B., Arashi M., Ahmed S. E. - Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime, pp. 229‑251.109-7, pp. 252‑254.
( résumés du n°1/2020)

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