2021

Volume 184, n° 2, April 2021

  • Smith PA. A distillation of the live chat during the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 448-448. doi:https://doi.org/10.1111/rssa.12671
  • McNeish D, Dumas D. A seasonal dynamic measurement model for summer learning loss, pp. 616-642. doi:https://doi.org/10.1111/rssa.12634
  • Ramdas A. Aaditya Ramdas’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 438-440. doi:https://doi.org/10.1111/rssa.12652
  • Pedersen AP. Arthur Paul Pedersen’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 443-444. doi:https://doi.org/10.1111/rssa.12655
  • Shafer G. Author’s reply to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 466-478. doi:https://doi.org/10.1111/rssa.12672
  • Osimani B. Barbara Osimani’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 437-438. doi:https://doi.org/10.1111/rssa.12651
  • Tipton E. Beyond generalization of the ATE: Designing randomized trials to understand treatment effect heterogeneity, pp. 504-521. doi:https://doi.org/10.1111/rssa.12629
  • Krakauer C, Rice K. Chloe Krakauer and Kenneth Rice’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 452-453. doi:https://doi.org/10.1111/rssa.12660
  • Hennig C. Christian Hennig’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 446-447. doi:https://doi.org/10.1111/rssa.12657
  • Chai CP. Christine P. Chai’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 449-450. doi:https://doi.org/10.1111/rssa.12658
  • Auer L von, Wengenroth J. Consistent aggregation with superlative and other price indices, pp. 589-615. doi:https://doi.org/10.1111/rssa.12633
  • Rougier J. Estimating event-rates from unreliable historical records, pp. 494-503. doi:https://doi.org/10.1111/rssa.12625
  • Livingston M, Pannullo F, Bowman AW, Scott EM, Bailey N. Exploiting new forms of data to study the private rented sector: Strengths and limitations of a database of rental listings, pp. 663-682. doi:https://doi.org/10.1111/rssa.12643
  • Gómez‐Rubio V. Handbook of Mixture Analysis S. Frühwirth-Schnatter, G. Celeux and C.P. Robert, 2019. Chapman and Hall/CRC Handbooks of Modern Statistical Methods Series, Boca Raton. 522 pp., 52.99 GBP. ISBN 978-0-367-732066, pp. 787-788. doi:https://doi.org/10.1111/rssa.12673
  • Crane H. Harry Crane’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 436-437. doi:https://doi.org/10.1111/rssa.12650
  • Doucouliagos C, Hennessy J, Mallick D. Health aid, governance and infant mortality, pp. 761-783. doi:https://doi.org/10.1111/rssa.12679
  • Mateu J. Jorge Mateu’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 458-458. doi:https://doi.org/10.1111/rssa.12664
  • Schure J ter. Judith ter Schure’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 460-461. doi:https://doi.org/10.1111/rssa.12667
  • Kumar K. Kuldeep Kumar’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 453-454. doi:https://doi.org/10.1111/rssa.12661
  • Akande O, Madson G, Hillygus DS, Reiter JP. Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse, pp. 643-662. doi:https://doi.org/10.1111/rssa.12635
  • Zhang L-C, Tuoto T. Linkage-data linear regression, pp. 522-547. doi:https://doi.org/10.1111/rssa.12630
  • Weidmann B, Miratrix L. Missing, presumed different: Quantifying the risk of attrition bias in education evaluations, pp. 732-760. doi:https://doi.org/10.1111/rssa.12677
  • Longford N. Nick Longford’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 455-456. doi:https://doi.org/10.1111/rssa.12663
  • Proietti T, Giovannelli A. Nowcasting monthly GDP with big data: A model averaging approach, pp. 683-706. doi:https://doi.org/10.1111/rssa.12645
  • Vos P. Paul Vos’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 461-462. doi:https://doi.org/10.1111/rssa.12668
  • Grünwald PD. Peter D. Grünwald’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 440-441. doi:https://doi.org/10.1111/rssa.12653
  • Wijayatunga P. Priyantha Wijayatunga’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 465-466. doi:https://doi.org/10.1111/rssa.12670
  • Dawid P. Proposer of the vote of thanks to Glenn Shafer and contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication, pp. 432-433. doi:https://doi.org/10.1111/rssa.12648
  • Zhang L-C. Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big-data statistics, pp. 571-588. doi:https://doi.org/10.1111/rssa.12632
  • Berkum F van, Antonio K, Vellekoop M. Quantifying longevity gaps using micro-level lifetime data, pp. 548-570. doi:https://doi.org/10.1111/rssa.12631
  • Wang R. Ruodu Wang’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 463-464. doi:https://doi.org/10.1111/rssa.12669
  • Martin R. Ryan Martin’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 456-457. doi:https://doi.org/10.1111/rssa.12665
  • Dellaportas P, Ioannidis E, Kotsogiannis C. Sample size determination for risk-based tax auditing, pp. 479-493. doi:https://doi.org/10.1111/rssa.12618
  • Greenland S. Sander Greenland’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 450-451. doi:https://doi.org/10.1111/rssa.12659
  • Coolen FPA. Seconder of the vote of thanks to Glenn Shafer and contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication.’, pp. 434-435. doi:https://doi.org/10.1111/rssa.12649
  • Senn S. Stephen Senn’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 459-460. doi:https://doi.org/10.1111/rssa.12666
  • Shafer G. Testing by betting: A strategy for statistical and scientific communication, pp. 407-431. doi:https://doi.org/10.1111/rssa.12647
  • Kavetsos G, Kawachi I, Kyriopoulos I, Vandoros S. The effect of the Brexit referendum result on subjective well-being*, pp. 707-731. doi:https://doi.org/10.1111/rssa.12676
  • Barnett V, McConway K. Tobias (Toby) Lewis 1918-2020, pp. 784-786. doi:https://doi.org/10.1111/rssa.12675
  • Lai TL, Choi A. Tze Leung Lai and Anna Choi’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 454-455. doi:https://doi.org/10.1111/rssa.12662
  • Vovk V. Vladimir Vovk’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 445-446. doi:https://doi.org/10.1111/rssa.12656
  • Meng X-L. Xiao-Li Meng’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, pp. 442-443. doi:https://doi.org/10.1111/rssa.12654

 (résumés n° 2/2021)

Volume 184, n° 1, January 2021

  • Dorsett R. A Bayesian structural time series analysis of the effect of basic income on crime: Evidence from the Alaska Permanent Fund*, pp. 179-200. doi:https://doi.org/10.1111/rssa.12619
  • Knaus MC. A double machine learning approach to estimate the effects of musical practice on student’s skills, pp. 282-300. doi:https://doi.org/10.1111/rssa.12623
  • Schiavoni C, Palm F, Smeekes S, Brakel J van den. A dynamic factor model approach to incorporate Big Data in state space models for official statistics, pp. 324-353. doi:https://doi.org/10.1111/rssa.12626
  • Lebacher M, Thurner PW, Kauermann G. A dynamic separable network model with actor heterogeneity: An application to global weapons transfers*, pp. 201-226. doi:https://doi.org/10.1111/rssa.12620
  • Hong G, Yang F, Qin X. Did you conduct a sensitivity analysis? A new weighting-based approach for evaluations of the average treatment effect for the treated, pp. 227-254. doi:https://doi.org/10.1111/rssa.12621
  • Moore JC, Durrant GB, Smith PWF. Do coefficients of variation of response propensities approximate non-response biases during survey data collection?, pp. 301-323. doi:https://doi.org/10.1111/rssa.12624
  • Tanner KT, Sharples LD, Daniel RM, Keogh RH. Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison, pp. 3-30. doi:https://doi.org/10.1111/rssa.12611
  • Bansak K. Estimating causal moderation effects with randomized treatments and non-randomized moderators, pp. 65-86. doi:https://doi.org/10.1111/rssa.12614
  • Crossley TF, Schmidt T, Tzamourani P, Winter JK. Interviewer effects and the measurement of financial literacy, pp. 150-178. doi:https://doi.org/10.1111/rssa.12617
  • Christensen BJ, Gupta ND, Magistris PS de. Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting*, pp. 118-149. doi:https://doi.org/10.1111/rssa.12616
  • Song Y, Wang N. On probability distributions of the time deviation law of container liner ships under interference uncertainty, pp. 354-367. doi:https://doi.org/10.1111/rssa.12627
  • Spearing H, Tawn J, Irons D, Paulden T, Bennett G. Ranking, and other properties, of elite swimmers using extreme value theory, pp. 368-395. doi:https://doi.org/10.1111/rssa.12628
  • Greene WH, Harris MN, Knott RJ, Rice N. Specification and testing of hierarchical ordered response models with anchoring vignettes, pp. 31-64. doi:https://doi.org/10.1111/rssa.12612
  • Kim HJ, Drechsler J, Thompson KJ. Synthetic microdata for establishment surveys under informative sampling, pp. 255-281. doi:https://doi.org/10.1111/rssa.12622
  • Cai L. The effects of health on the extensive and intensive margins of labour supply, pp. 87-117. doi:https://doi.org/10.1111/rssa.12615

OBITUARY

BOOK REVIEWS

  • Dietz S. Handbook of Financial Risk Management T. Roncalli, 2020. Chapman and Hall/CRC Financial Mathematics Series, Boca Raton. 1142 pp., 230.00 GBP. ISBN 978-1-138-50187-4, pp. 402-403. doi:https://doi.org/10.1111/rssa.12641
  • Dietz S. R Visualizations – Derive Meaning from DataD.W. Gerbing, 2020. Chapman and Hall/CRC Press, Boca Raton. 237 pp. 68,99 GBP. ISBN 978-1-138-59963-5, pp. 401-402. doi:https://doi.org/10.1111/rssa.12640
  • Kumar K. Statistics and Health Care Fraud: How to Save BillionsTahir Ekin (2019) Boca Raton, Chapman Hall/CRC Press 140+xvii pp $46.99 ISBN 9781138197428, pp. 401-401. doi:https://doi.org/10.1111/rssa.12637
  • Kumar K. Time series clustering and classificationMaharaj E. A., D’Urso P., Caiado J., 2019. Boca Raton, Chapman Hall/CRC Press. 227+xv pp $284.00. ISBN 978-1-4987-7321-8 (Hardback), pp. 400-400. doi:https://doi.org/10.1111/rssa.12636

(résumés n° 1/2021)

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