References
- Annderson, T.W., Sawa, T. (1982). Exact and apoproximate distributions of the maximum likelihood estimator of a slope coefficient. J. Royal Stat. Soc. Ser. B. 44:52–62.
- Balakrishnan, N., Davies, K.F., Keating, J.P., Mason, R.L. (2011). Pitman closeness, monotonicity and consistency of best linear unbiased and invariant estimators for exponential distribution under Type-II censoring. J. Stat. Comput. Simul. 81:985–999.
- Balakrishnan, N., Iliopoulos, G., Keating, J.P., Mason, R.L. (2009). Pitman closeness of sample median to population median. Stat. Probab. Lett. 79:1759–1766.
- Cheng, C.L., Van Ness, J.W. (1991). On the unreplicated ultra-structural model. Biometrika 78:442–452.
- Cheng, C.L., Van Ness, J.W. (1994). On estimating linear relationships when both variables are subject to errors. J. Royal Stat. Soc. Ser. B 56:167–183.
- Cheng, C.L., Van Ness, J.W. (1999). Statistical Regression with Measurement Error.New York: Oxford University Press.
- Dolby, G.R. (1976). The ultrastructural relation: a synthesis of the functional and structural relations. Biometrika. 63:39–50.
- Fuller, W.A. (1987). Measurement Error Models.New York: Wiley.
- Gleser, L.J. (1992). The importance of assessing measurement reliability in multivariate regression. J. Am. Stat. Assoc. 87:696–707.
- Gleser, L.J. (1993). Estimators of slopes in linear errors-in-variables regression models when the predictors have known reliability matrix. Stat. Probab. Lett. 17:113–121.
- Jozani, M.J., Davies, K.F., Balakrishnan, N. (2012). Pitman closeness results concerning ranked set sampling. Stat. Probab. Lett. 82:2260–2269.
- Keating, J.P., Mason, R.L., Sen, P.K. (1993). Pitman Measure of Closeness: Comparison of Statistical Estimators.Philadelphia: SIAM.
- Li, W., Yang, H. (2013). Weighted stochastic restricted estimation in linear measurement error models. Commun. Stat.: Simul. Comput. 42:932–968.
- Mariano, R.A. (1969). On distribution and moments of single-equation estimators in a set of simultaneous linear stochastic equations. Technical report, 27, Institute of Mathematics Studies Sciences, Stanford university.
- Rao, C.R., Toutenburg, H., Shalabh, Heumann, C., (2008). Linear models and Generalizations: Least Squares and Alternatives. Berlin: Spring.
- Schneeweiss, H. (1991). Note on a linear model with errors in the variables and with trend. Stat. Pap. 32:261–264.
- Schneeweissa, H., Shalabhb. (2007). On the estimation of the linear relation when the error variances are known. Comput. Stat. Data Anal. 52:1143–1148.
- Shalabh, Garg, G., Misra, N. (2007). Restricted regression estimation in measurement error models. Comput. Stat. Data Anal. 52:1149–1166.
- Shalabh, Garg, G., Misra, N. (2009). Use of prior information in the consistent estimation of regression coefficients in measurement error models. J. Multivariate Anal. 100:1498–1520.
- Srivastava, A.K., Shalabh., (1997a). Asymptotic efficiency properties of least squares in an ultrastructural model. Test. 6:419–431.
- Srivastava, A.K., Shalabh., (1997b). Consistent estimation for the nonnormal ultrastructural model. Stat. Probab. Lett. 34:67–73.
- Singh, S., Jain, K., Sharma, S. (2014). Replicated measurement error model under exact linear restrictions. Stat. Pap. 55:253–274.
- Toutenburg, H. (1982). Prior Information in Linear Models.New York: Wiley.
- Wong, M.Y. (1989). Likelihood estimation of a simple linear regression model when both variables have error. Biometrika 76:141–148.