Abstract
In this chapter, and throughout the book, one can assume there are n subjects/units yi : i = 1, 2, …, n. Let us assume these are independent events, and then from a distribution that belongs to the exponential family. A review of general linear model, generalized linear model, and Bayesian interval is given.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, F. (2009). Bayesian modeling using the MCMC procedure. In Proceedings of the SAS Global Forum 2009 Conference. SAS Global Forum 2009, Cary, NC. Retrieved from http://support.sas.com/resources/papers/proceedings09/257s009.pdf.
Chen, F. (2011). The RANDOM statement and more: Moving on with PROC MCMC. In Proceedings of the SAS Global Forum 2011 Conference. SAS Global Forum 2011, Cary, NC. Retrieved from http://support.sas.com/resources/papers/proceedings11/334-2011.pdf.
Chen, F. (2013). Missing no more: Using the MCMC procedure to model missing data. In Proceedings of the SAS Global Forum 2013 Conference. SAS Global Forum 2013, Cary, NC. Retrieved from https://support.sas.com/resources/papers/proceedings13/436-2013.pdf.
Dobson, A. J., & Barnett, A. G. (2008). An introduction to generalized linear models (3rd ed.). Boca Raton: Chapman and Hall/CRC.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054.
Hoff, P. D. (2009). A first course in bayesian statistical methods. New York: Springer.
Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd ed.). Boca Raton: Chapman and Hall.
Stokes, M., Chen, F., & Gures, F. (2014). An introduction to Bayesian analysis with SAS/STAT Software. In Proceedings of the SAS Global Forum 2014 Conference. SAS Global Forum 2014 Conference, Washington, DC. Retrieved from https://support.sas.com/resources/papers/proceedings14/SAS400-2014.pdf.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Wilson, J.R., Vazquez-Arreola, E., Chen, (.DG. (2020). Review of Estimators for Regression Models. In: Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-48904-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-48904-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48903-8
Online ISBN: 978-3-030-48904-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)