[1] Madsen, H. and Thyregod, P. 2010. Introduction to general and generalized linear models. CRC Press.
[2] Ramsey, G. B. (1969). Test for specification error in classical linear least square regression analysis. Journal of the Royal Statistical Society, 31, 350-71.
[3] Griffith, D. A. and Chun, Y. (2016). Evaluating eigenvector spatial filter corrections for omitted georeference variables. Econometrics, 21, 1-12.
[4] Shukur, G. and Mantalos, P. (2004). Size and power of the RESET test as applied to systems of equations. A Bootstrap Approach, Journal of Modern Applied Statistical Methods, 3, 370-385.
[5] Sapra, S. (2005). A regression error specification test (RESET) for generalized linear model. Economics Bulletin, 3, 1-6.
[6] Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
[7] Little, R. J. A. and Rubin, D. B. (2002). Statistical analysis with missing data. Second Edition, Wiley-Interscience, New York.
[8] Basilevsky, A., Sabourin, D., Hum, D. and Anderson, A. (1985). Missing data estimators in the general linear model: an evaluation of simulated data as an experimental design. Communications in Statistics-Simulation and Computation, 14(2), 371-394.
[9] Little, R. J. A. (1992). Regression with missing X's: A review. Journal of the American Statistical Association, 87, 1227-1237.
[10] Wang, S. and Wang, C.Y. (2001). A note on kernel assisted estimators in missing covariate regression. Statistics and Probability letters, 55, 439-449.
[11] Hardle, W. and Mammen, E. (1993). Comparing nonparametric versus parametric regression fits. Annals of Statistics, 21, 1921-1947.
[12] Hardle, W., Mammen, E. and Muller, M. (1998). Testing parametric versus semiparametric modeling in generalized linear models. Journal of the American Statistical Association, 93(444), 1461-1474.
[13] Zhu, L.X. and Cui, H.J. (2005). Testing lack-of-fit for general linear errors in variables models. Statistica Sinica, 15, 1049--1068.
[14] Guo, X. and Xu, W. (2012). Goodness-of-fit tests for general linear models with covariates missed at random. Journal of Statistical Planning and Inference, 142, 2047-2058.
[15] Li, X. (2012). Lack-of-fit testing of a regression model with response missing at random. Journal of Statistical Planning and Inference, 142(1), 155-170.
[16] Zhao, L. P. and Lipsitz, S. (1992). Design and analysis of two-stage studies. Statistics in Medicine, 11, 769-782.
[17] Carpenter, J. R. and Kenward, M. G. (2006). A comparison of multiple imputation and doubly robust estimation for analyses with missing data. Journal of the Royal statistical Society, 169, 571-584.
[18] Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical methods for data analysis. Belmont, CA: Wadsworth.
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