3,006
Views
1
CrossRef citations to date
0
Altmetric
General

Distribution-Free Location-Scale Regression

ORCID Icon, ORCID Icon & ORCID Icon
Pages 345-356 | Received 23 Aug 2022, Accepted 07 Apr 2023, Published online: 01 Jun 2023

References

  • Burke, K., and MacKenzie, G. (2017), “Multi-Parameter Regression Survival Modeling: An Alternative to Proportional Hazards,” Biometrics, 73, 678–686. DOI: 10.1111/biom.12625.
  • Burke, K., Eriksson, F., and Pipper, C. B. (2020), “Semiparametric Multiparameter Regression Survival Modeling,” Scandinavian Journal of Statistics, 47, 555–571. DOI: 10.1111/sjos.12416.
  • Burke, K., Jones, M. C., and Noufaily, A. (2020), “A Flexible Parametric Modelling Framework for Survival Analysis,” Journal of the Royal Statistical Society, Series C, 69, 429–457. DOI: 10.1111/rssc.12398.
  • Cox, C. (1995), “Location-Scale Cumulative Odds Models for Ordinal Data: A General Non-linear Model Approach,” Statistics in Medicine, 14, 1191–1203. DOI: 10.1002/sim.4780141105.
  • Deb, P., and Trivedi, P. K. (1997), “Demand for Medical Care by the Elderly: A Finite Mixture Approach,” Journal of Applied Econometrics, 12, 313–336. DOI: 10.1002/(sici)1099-1255(199705)12:3¡313::aid-jae440¿3.0.co;2-g.
  • Farouki, R. T. (2012), “The Bernstein Polynomial Basis: A Centennial Retrospective,” Computer Aided Geometric Design, 29, 379–419. DOI: 10.1016/j.cagd.2012.03.001.
  • Fredriks, A. M., van Buuren, S., Burgmeijer, R. J. F., Meulmeester, J. F., Beuker, R. J., Brugman, E., Roede, M. J., Verloove-Vanhorick, S. P., and Wit, J. (2000), “Continuing Positive Secular Growth Change in The Netherlands 1955–1997,” Pediatric Research, 47, 316–323. DOI: 10.1203/00006450-200003000-00006.
  • Haslinger, C., Korte, W., Hothorn, T., Brun, R., Greenberg, C., and Zimmermann, R. (2020), “The Impact of Prepartum Factor XIII Activity on Postpartum Blood Loss,” Journal of Thrombosis and Haemostasis, 18, 1310–1319. DOI: 10.1111/jth.14795.
  • Hothorn, T. (2020), “Transformation Boosting Machines,” Statistics and Computing, 30, 141–152. DOI: 10.1007/s11222-019-09870-4.
  • Hothorn, T., and Zeileis, A. (2021), “Predictive Distribution Modelling Using Transformation Forests,” Journal of Computational and Graphical Statistics, 30, 144–148. DOI: 10.1080/10618600.2021.1872581.
  • Hothorn, T., Hornik, K., van de Wiel, M. A., and Zeileis, A. (2006), “A Lego System for Conditional Inference,” The American Statistician, 60, 257–263. DOI: 10.1198/000313006X118430.
  • Hothorn, T., Müller, J., Held, L., Möst, L., and Mysterud, A. (2015), “Temporal Patterns of Deer-Vehicle Collisions Consistent with Deer Activity Pattern and Density Increase but not General Accident Risk,” Accident Analysis & Prevention, 81, 143–152. DOI: 10.1016/j.aap.2015.04.037.
  • Hothorn, T., Möst, L., and Bühlmann, P. (2018), “Most Likely Transformations,” Scandinavian Journal of Statistics, 45, 110–134. DOI: 10.1111/sjos.12291.
  • Hothorn, T., Barbanti, L., and Siegfried, S. (2023), tram: Transformation Models. R package version 0.8-3, https://CRAN.R-project.org/package=tram.
  • Kneib, T., Silbersdorff, A., and Säfken, B. (2023), “Rage against the Mean – A Review of Distributional Regression Approaches,” Econometrics and Statistics, 26, 99–123. DOI: 10.1016/j.ecosta.2021.07.006.
  • Kook, L. (2023), tramvs: Optimal Subset Selection for Transformation Models. R package version 0.0-4, available at https://CRAN.R-project.org/package=tramvs.
  • Lepage, Y. (1971), “A Combination of Wilcoxon’s and Ansari-Bradley’s Statistics,” Biometrika, 58, 213–217. DOI: 10.2307/2334333.
  • Madsen, K., Nielsen, H. B., and Tingleff, O. (2004), Optimization with Constraints (2nd ed.), Technical University of Denmark. Available at http://www2.imm.dtu.dk/pubdb/p.php?4213.
  • Mayr, A., Fenske, N., Hofner, B., Kneib, T., and Schmid, M. (2012), “Generalized Additive Models for Location, Scale and Shape for High Dimensional Data – A Flexible Approach based on Boosting,” Journal of the Royal Statistical Society, Series C, 61, 403–427. DOI: 10.1111/j.1467-9876.2011.01033.x.
  • McCullagh, P. (1980), “Regression Models for Ordinal Data,” Journal of the Royal Statistical Society, Series B, 42, 109–127. DOI: 10.1111/j.2517-6161.1980.tb01109.x.
  • McLain, A. C., and Ghosh, S. K. (2013), “Efficient Sieve Maximum Likelihood Estimation of Time-Transformation Models,” Journal of Statistical Theory and Practice, 7, 285–303. DOI: 10.1080/15598608.2013.772835.
  • Peng, D., MacKenzie, G., and Burke, K. (2020), “A Multiparameter Regression Model for Interval-Censored Survival Data” Statistics in Medicine, 39, 1903–1918. DOI: 10.1002/sim.8508.
  • Peterson, B., and Harrell, F. E. (1990), “Partial Proportional Odds Models for Ordinal Response Variables,” Journal of the Royal Statistical Society, Series C, 39, 205–217. DOI: 10.2307/2347760.
  • Pollet, T. V., and Nettle, D. (2009), “Partner Wealth Predicts Self-Reported Orgasm Frequency in a Sample of Chinese Women,” Evolution and Human Behavior, 30, 146–151. DOI: 10.1016/j.evolhumbehav.2008.11.002.
  • Rigby, R., Stasinopoulos, D. M., Heller, G., and De Bastiani, F. (2019), Distributions for Modeling Location, Scale, and Shape: Using GAMLSS in R, Boca Raton, FL: Chapman & Hall/CRC Press. DOI: 10.1201/9780429298547.
  • Rigby, R. A., and Stasinopoulos, D. M. (2005), “Generalized Additive Models for Location, Scale and Shape,” Journal of the Royal Statistical Society, Series C, 54, 507–554. DOI: 10.1111/j.1467-9876.2005.00510.x.
  • Schein, P. S., and Gastrointestinal Tumor Study Group. (1982), “A Comparison of Combination Chemotherapy and Combined Modality Therapy for Locally Advanced Gastric Carcinoma,” Cancer, 49, 1771–1777. DOI: 10.1002/1097-0142(19820501)49:9¡1771::aid-cncr2820490907¿3.0.co;2-m.
  • Sewak, A., and Hothorn, T. (2023), “Estimating Transformations for Evaluating Diagnostic Tests with Covariate Adjustment,” Statistical Methods in Medical Research. Accepted for publication. DOI: 10.1177/09622802231176030.
  • Siegfried, S., and Hothorn, T. (2020), “Count Transformation Models,” Methods in Ecology and Evolution, 11, 818–827. DOI: 10.1111/2041-210X.13383.
  • Stasinopoulos, D. M., and Rigby, R. A. (2007), “Generalized Additive Models for Location Scale and Shape (GAMLSS) in R,” Journal of Statistical Software, 23, 1–46. DOI: 10.18637/jss.v023.i07.
  • Thas, O., De Neve, J., Clement, L., and Ottoy, J.-P. (2012), “Probabilistic Index Models,” Journal of the Royal Statistical Society, Series B, 74, 623–671. DOI: 10.1111/j.1467-9868.2011.01020.x.
  • Tosteson, A. N. A., and Begg, C. B. (1988), “A General Regression Methodology for ROC Curve Estimation,” Medical Decision Making, 8, 204–215. DOI: 10.1177/0272989x8800800309.
  • Tutz, G., and Berger, M. (2017), “Separating Location and Dispersion in Ordinal Regression Models,” Econometrics and Statistics, 2, 131–148. DOI: 10.1016/j.ecosta.2016.10.002.
  • Tutz, G., and Berger, M. (2020), “Non Proportional Odds Models are Widely Dispensable – Sparser Modeling based on Parametric and Additive Location-Shift Approaches,” arXiv 2006.03914, arXiv.org E-Print Archive.
  • Zeng, D., and Lin, D. Y. (2007), “Maximum Likelihood Estimation in Semiparametric Regression Models with Censored Data,” Journal of the Royal Statistical Society, Series B, 69, 507–564. DOI: 10.1111/j.1369-7412.2007.00606.x.
  • Zhu, J., Wen, C., Zhu, J., Zhang, H., and Wang, X. (2020), “A Polynomial Algorithm for Best-Subset Selection Problem,” Proceedings of the National Academy of Sciences, 117, 33117–33123. DOI: 10.1073/pnas.2014241117.