References
- Tobin J. Estimation of relationships for limited dependent variables. Econometrica. 1958;26:24–36.
- Nelson FD. Censored regression models with unobserved, stochastic censoring thresholds. J Econometrics. 1977;6:309–327.
- Maddala G. Limited-dependent and qualitative variables in econometrics. Econometric Society Monographs in Quantitative Econometrics. Econometric Society Publication No. 3. Cambridge University Press; 1983.
- Stapleton DC, Young JD. Censored normal regression with measurement error on the dependent variable. Econometrica. 1984;52:737–760.
- Amemiya T. Tobit models: a survey. J Econometrics. 1984;24:3–61.
- Chib S. Bayes inference in the Tobit censored regression model. J Econometrics. 1992;51:79–99.
- Thompson ML, Nelson KP. Linear regression with Type I interval and left-censored response data. Environ Ecol Statist. 2003;10:221–230.
- Park JW, Genton MG, Ghosh SK. Censored time series analysis with autoregressive moving average models. Canad J Statist. 2007;35:151–168.
- Vaida F, Liu L. Fast implementation for normal mixed effects models with censored response. J Comput Graph Statist. 2009;18:797–817.
- Kim H-G. Moments of truncated Student-t distribution. J Korean Statist Soc. 2008;37:81–87.
- Arellano-Valle RB, Castro LM, González-Farías G, Muñoz-Gajardo KA. Student-t censored regression model: properties and inference. Stat Methods Appl. 2012;21:453–473.
- Massuia MB, Cabral CRB, Matos LA, Lachos VH. Influence diagnostics for Student-t censored regression models. Statistics. 2014;85:1–21.
- Matos LA, Prates MO, Chen MH, Lachos VA. Likelihood-based inference for mixed-effects models with censored response using the multivariate-t distribution. Statist Sinica. 2013;23:1323–1342.
- Garay AM, Castro LM, Leskow J, Lachos VH. Censored linear regression models for irregularly observed longitudinal data using the multivariate-t distribution. Stat Methods Med Res. 2014. DOI: 10.1177/0962280214551191.
- Rocha GHMA, Arellano-Valle RB, Loschi RH. Maximum likelihood methods in a robust censored errors-in-variables model. Test. 2015;24:857–877.
- Garay AW, Lachos VH, Bolfarine H, Cabral CR. Linear censored regression models with scale mixtures of normal distributions. Statist Papers. 2015. DOI: 10.1007/s00362-015-0696-9.
- Garay AW, Bolfarine H, Lachos VH, Cabral CR. Bayesian analysis of censored linear regression models with scale mixtures of normal distributions. J Appl Stat. 2015;42:2694–2714.
- Garay AM, Massuia MB, Lachos VH. BayesCR: Bayesian analysis of censored regression models under scale mixture of skew normal distributions. R package version 2.0. 2015. Available from: http://cran.r-project.org/package=BayesCR
- Garay AM, Massuia MB, Lachos VH. SMNCensReg: fitting univariate censored regression model under the family of scale mixture of normal distributions. R package version 3.0. 2015. Available from: http://cran.r-project.org/package=SMNCensReg
- Castro LM, Lachos VH, Ferreira GP, Arellano-Valle RB. Partially linear censored regression models using heavy-tailed distributions: a Bayesian approach. Stat Model. 2014;18:14–31.
- Gelfand AE. Gibbs sampling. In: Raftery AE, Tanner MA, Wells MT, editors. Statistics in the 21-st century. Boca Raton: Chapman&Hall/CRC; 2002. p. 341–349.
- Ng KW. Explict formulas for unconditional pdf. Hong Kong: Department of Statistics, University of Hong Kong; 1995. Research report No. 82.
- Ng KW. One the inversion of Bayes theorem. Presentation in the 3rd ICSA statistical conference, 1995 August 17–20; Beijing, P. R. China; 1995.
- Tan MT, Tian GL, Ng. KW. Bayesian missing data problems: EM, data augmentation and noniterative computation. Biostatistics Series. New York: Chapman & Hall/CRC; 2010.
- Geweke J. Bayesian treatment of the independent Student-t linear model. J Appl Econometrics. 1993;8:S19–S40.
- Zellner A. Bayesian and non-Bayesian analysis of the regression model with multivariate Student-t error terms. J Amer Statist Assoc. 1976;71:400–405.
- Lachos VH, Cabral CRB, Abanto-Valle CA. A non-iterative sampling Bayesian method for linear mixed models with normal independent distributions. J Appl Stat. 2012;39:531–549.
- Peng FC, Dey DK. Bayesian analysis of outlier problems using divergence measures. Canad J Statist. 1995;23:199–213.
- R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015. Available from: http://www.R-project.org/