An M-estimation bibliography


Boos, D. D., & Stefanski, L. A. (2013). Essential statistical inference theory and methods. New York, NY: Springer.

Stefanski, L. A., & Boos, D. D. (2002). The calculus of M-estimation. The American Statistician, 56(1), 29-38.

Theoretical development

Godambe, V. P. (1960). An optimum property of regular maximum likelihood estimation. The Annals of Mathematical Statistics, 1208-1211.

Huber, P. J., & Ronchetti, E. M. (2009). Robust statistics (2nd ed.). Hoboken: Wiley.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica: Journal of the Econometric Society, 817-838.

White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica: Journal of the Econometric Society, 1-25.


Generalized Estimating Equations

Liang, K., & Zeger, L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13-22.

Sullivan Pepe, M., & Anderson, G. L. (1994). A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in Statistics - Simulation and Computation, 23(4), 939-951.

Preisser, J. S., Lohman, K. K., & Rathouz, P. J. (2002). Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random. Statistics in Medicine, 21(20), 3035-3054.

Small sample corrections

Fay, M. P., & Graubard, B. I. (2001). Small-Sample adjustments for Wald-type tests using sandwich estimators. Biometrics, 57(4), 1198-1206.

Kauermann, G., & Carroll, R. J. (2001). A note on the efficiency of sandwich covariance matrix estimation. Journal of the American Statistical Association, 96(456), 1387-1396.

Lu, B., Preisser, J. S., Qaqish, B. F., Suchindran, C., Bangdiwala, S. I., & Wolfson, M. (2007). A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics, 63(3), 935-941.

Mancl, L. A., & DeRouen, T. A. (2001). A covariance estimator for GEE with improved small-sample properties. Biometrics, 57(1), 126-134.

Paul, S., & Zhang, X. (2014). Small sample GEE estimation of regression parameters for longitudinal data. Statistics in Medicine, 33(22), 3869-81.

Teerenstra, S., Lu, B., Preisser, J. S., Van Achterberg, T., & Borm, G. F. (2010). Sample size considerations for GEE analyses of three-level cluster randomized trials. Biometrics, 66(4), 1230-1237.

Software (mostly GEE specific)

Carey, V. J., Lumley, T., & Ripley, B. D. (2012). gee: Generalized estimation equation solver. R Package Version, 4-13.

Halekoh, U., Hojsgaard, S., & Yan, J. (2006). The R package geepack for generalized estimating equations. Journal of Statistical Software, 15(2), 1-11.

McDaniel, L. S., Henderson, N. C., and Rathouz, P. J. (2013). Fast pure R implementation of GEE: application of the Matrix package. The R Journal, 5/1:181--187

In SAS, see the proc gee and proc genmod procedures.

In STATA, see the xtgee function.

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geex documentation built on Feb. 17, 2020, 5:08 p.m.