128
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

Sensitivity analysis for the identifiability with application to latent random effect model for the mixed data

Pages 2761-2776 | Received 29 Oct 2013, Accepted 27 May 2014, Published online: 26 Jun 2014

References

  • A. Agresti, Categorical Data and Analysis, Wiley, New York, 2002.
  • J.A. Anderson and J.D. Pemberton, The grouped continuous model for multivariate ordered categorical variables and covariate adjustment, Biometrics 41 (1985), pp. 875–885. doi: 10.2307/2530960
  • E. Bahrami Samani and M. Ganjali, A multivariate latent variable model for mixed-data from continuous and ordinal responses with possibility of missing responses, Appl. Appl. Math.: Int. J. 5(10) (2010), pp. 1564–1584.
  • E. Bahrami Samani, M. Ganjali, and A. Khodadadi, A latent variable model for mixed continuous and ordinal responses, J. Stat. Theory Appl. 7 (2008), pp. 337–348.
  • E. Bahrami Samani and Zh. Thamasebinejad, Joint modelling of mixed correlated nominal, ordinal and continuous responses, J. Stat. Res. 45(1) (2011), pp. 37–47.
  • P. Catalano and L.M. Ryan, Bivariate latent variable models for clustered discrete and continuous outcomes, J. Am. Stat. Assoc. 50(3) (1992), pp. 1078–1095.
  • R.D. Cook, Assessment of local influence (with discussion), J. R. Stat. Soc. Ser. B. 48 (1986), pp. 133–169.
  • D.R. Cox and N. Wermuth, Response models for mixed binary and quantitative variables, Biometrika 79(3) (1992), pp. 441–461. doi: 10.1093/biomet/79.3.441
  • A.R. De Leon and K.C. Carri'ere, General mixed-data model: Extension of general location and grouped continuous models, Can. J. Stat. 35 (2007), pp. 533–548. doi: 10.1002/cjs.5550350405
  • A.R. De Leon and B. Wu, Copula-based regression models for a bivariate mixed discrete and continuous outcome, Stat. Med. 30(2) (2011), pp. 175–185. doi: 10.1002/sim.4087
  • P.J. Diggle, P. Heagerty, K.Y. Liang, and S.L. Zeger, Analysis of Longitudinal Data, Oxford University Press, Oxford, 2002.
  • A.A. Ding, Identifiability conditions for covariate effects model on survival times under informative censoring, Stat. Probab. Lett. 80 (2010), pp. 911–915. doi: 10.1016/j.spl.2010.01.027
  • G.M. Fitzmaurice and N.M. Laird, Regression models for bivariate discrete and continuous outcome with clustering, J. Am. Stat. Assoc. 90 (1995), pp. 845–852. doi: 10.1080/01621459.1995.10476583
  • D.A. Harvile and R.W. Mee, A mixed model procedure for analyzing ordered categorical data, Biometrics 40 (1984), pp. 393–408. doi: 10.2307/2531393
  • J.J. Heckman, Dummy endogenous variable in a simultaneous equation system, Econometrica 46(6) (1978), pp. 931–959. doi: 10.2307/1909757
  • M. Iannario and D. Piccolo, A new statistical model for the analysis of customer satisfaction, Qual. Technol. Quant. Manage. 7 (2010), pp. 149–168.
  • N.A. Kaciroti, T.E. Raghunathan, M.A. Schork, N.M. Clark, and M. Gong, A Bayesian approach for clustered longitudinal ordinal outcome with nonignorable missing data: Evaluation of an asthma education program, J. Am. Stat. Assoc. 474 (2006), pp. 435–446. doi: 10.1198/016214505000001221
  • W.J. Krzanowski, Distance between populations using mixed continuous and categorical variables, Biometrika 70 (1983), pp. 235–243. doi: 10.1093/biomet/70.1.235
  • P. McCullagh, Regression models for ordinal data (with discussion), J. R. Stat. Soc. B 42 (1980), pp. 109–142.
  • G. Molenberghs, H. Geys, and M. Buyse, Evaluation of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes, Stat. Med. 20 (2001), pp. 3023–3038. doi: 10.1002/sim.923
  • I. Olkin and R.F. Tate, Multivariate correlation models with mixed discrete and continuous variables, Ann. Math. Stat. 32 (1961), pp. 448–465. doi: 10.1214/aoms/1177705052
  • A.T. Pinto and S.L.T. Normand, Correlated bivariate continuous and binary outcomes: Issues and applications, Stat. Med. 28 (2009), pp. 1753–1773. doi: 10.1002/sim.3588
  • W.Y. Poon and S.Y. Lee, Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients, Psychometrika 52(3) (1987), pp. 409–430. doi: 10.1007/BF02294364
  • M.M. Regan and P.J. Catalano, Combined continuous and discrete outcomes, in Topics in Modelling of Clustered Data, M. Aerts, H. Geys, G. Molenberghs, and L.M. Ryan, eds., Chapman and Hall, London, 2002, pp. 233–261.
  • M.D. Sammel, L.M. Ryan, and J.M. Legler, Latent variable models for mixed discrete and continuous outcomes, J. R. Stat. Soc. Ser. B: Methodol. 59 (1997), pp. 667–678. doi: 10.1111/1467-9868.00090
  • T.K. Sengul, D.S. Stoffer, and N.L. Day, A residuals-based transition model for longitudinal analysis with estimation in the presence of missing data, Stat. Med. 26 (2007), pp. 3330–3341. doi: 10.1002/sim.2757
  • G. Tutz, Modeling of repeated ordered measurements by isotonic sequential regression, Stat. Model. 5 (2005), pp. 269–287. doi: 10.1191/1471082X05st101oa
  • G. Verbeke and G. Molenberghs, Linear Mixed Models in Practice: A SAS Oriented Approach, Springer, New York, 1997.
  • W. Wang, Identifiability of linear mixed effects models, Electron. J. Stat. 7 (2013), pp. 244–263. doi: 10.1214/13-EJS770

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.