[1] Paolino, P. (2001). Maximum Likelihood E stimation of Models with Beta-Distributed Dependent Variables, Political Analysis, 9, 325-346.
[2] Kieschnik, R. and McCullough, B.D. (2003). Regression Analysis of Variates Observed on (0,1): Percentage, Proportions and Fractions. Statistical Modelling, 3,193-213.
[3] Ferrari, S. and Cribari, F. (2004). Beta Regression for ModellingR ates and Proportions, Journal of Applied Statistics, 31, 799-815.
[4] Cepeda, E.D. and Gamerman, D. (2005). Bayesian Methodology for Modeling Parameters in the Two ParameterE xponentialF amily, Revista Estadística, 57, 168-169.
[5] Smithson, M. and Verkuilen, J. (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables, Psychological Methods, 11, 54-71.
[6] Branscum, A.J., Johnson, W.O. and Thurmond, M. (2007). Baysian Beta Regression Applications to Houshold Expenditure Data and Genetic Distance Between Food and Mouth Dieseas Viruses, Australian & New Zealand Journal of Statistics, 49, 287-301.
[7] Ospina, R. and Ferrari, S. (2010). Inflated Beta Distributions. Statistical Paper, 23, 193-213.
[8] Ospina, R. and Ferrari, S. (2012). A General Class of Zeror-One Inflated Beta Regression Models. Computational Statistics & Data Analysis, 56, 1609-1623.
[9] Galvis, M.D., Dipankar, B. and Victor, H.L. (2014). Augmented Mixed Beta Regression Models for Periodontal Proportion Data, Preprinted, Statistics in Medicine, 33,3759-3771.
[0] Figueroa-Zúñiga, J.I., Arellano-Valle, R.B. and Ferrari, S.L. (2013). Mixed Beta Regression: A Bayesian Perspective, Computational Statistics & Data Analysis, 61, 137– 147.
[11] Sturtz, S., Ligges, U. and Gelman, A. (2005). R2winbugs: A Package for Running Winbugs from R, Journal of Statistical Software, 12, 1–16.
[12] نتایج آمارگیری نیروی کار (1392)، مرکز آمار ایران، تهران.
[13] Gelman, A. and Rubin, D.B. (1992). Inference from Iterative Simulation Using Multiple Sequences, Statistical Science 7, 457–511.
[14] Heidelberger, P. and Welch, P.D. (1981). A Spectral Method for Confidence Interval Generation and Run Length Contral in Simulations, Communications of the ACM, 24, 233-245.
[15] Brooks, S.P. and Gelman, A., (1998). General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics 7, 434–455.
[16] Plummer, M., Best, N., Cowles, K. and Vines, K., (2006). The coda Package. R Project. http://cran.r-project.org/doc/packages/coda.pdf.