References
- Kendall MG, Stuart A. The advanced theory of statistics. 4th ed. New York: Hafner; 1979.
- Cheng CL, Van Ness JW. Statistical regression with measurement error. Arnold: London; 1999.
- Fuller WA. Measurement error models. New York: John Wiley & Sons; 1987.
- Dunn G. Design and analysis of reliability: the statistical evaluation of measurement errors. New York: Edward Arnold; 1992.
- Carroll R, Ruppert D, Stefanski L, et al. Measurement error in nonlinear models: a modern perspective. 2nd ed. New York: Chapman & Hall/CRC; 2006.
- Buonaccorsi JP. Measurement error: models, methods and applications. London: CRC Press; 2010.
- Yi GY. Statistical analysis with measurement error or misclassication: strategy, method and application. New York: Springer; 2017.
- Chan LK, Mak TK. Maximum likelihood estimation in multivariate structural relationships. Scand J Stat. 1984;11:45–50.
- Gleser LJ. The importance of assessing measurement reliability in multivariate regression. J Am Stat Assoc. 1992;87:696–707.
- Barnett V. Simultaneous pairwise linear structural relationships. Biometrics. 1969;25:129–142.
- Anderson TW. Estimating linear statistical relationships. Ann Stat. 1984;12:1–45.
- Gleser LJ. Estimation in a multivariate errors-in-variables regression model: large sample results. Ann Stat. 1981;9:24–44.
- Kelly G. The influence function in the errors in variables problem. Ann Stat. 1984;12:87–100.
- Brown P, Fuller W, Statistical analysis of measurement error models and applications: Proceedings of the AMS-IMS-SIAM joint summer research conference. Contemporary Mathematics. Vol 112. American Mathematical Soc; 1990.
- Arellano R, Bolfarine H. Elliptical structural models. Commun Stat. 1996;25:2319–2341.
- Theobald CM, Mallison JR. Comparative calibration, linear structural relationship and congeneric measurements. Biometrics. 1978;34:39–45.
- Shyr JY, Gleser LJ. Inference about comparative precision in linear structural relationships. J Stat Plan Inference. 1986;14:339–358.
- Bolfarine H, Galea M. Structural comparative calibration using the EM algorithm. J Appl Stat. 1995;22:277–292.
- Bolfarine H, Galea M. Maximum likelihood estimation of simultaneous pairwise linear structural relationships. Biometr J. 1995;37:673–689.
- Lin L, Hedayat AS, Wu W. Statistical tools for measuring agreement. New York: Springer; 2012.
- Choudhary PK, Nagaraja HN. Measuring agreement: models, methods, and applications. New York: Wiley; 2017.
- Rogers G. Matrix derivatives. New York: Marcel Dekker; 1980.
- McLachlan GJ, Krishnan T. The EM algorithm and extensions. New York: John Wiley; 2008.
- Lindley DV. Regression lines and the linear functional relationship. J R Stat Soc. 1947;9:218–244.
- Boos DD, Stefanski LA. Essential statistical inference. New York: Springer; 2013.
- Terrell GR. The gradient statistic. Comput Sci Stat. 2002;34:206–215.
- Chen F, Zhu HT, Lee SY. Perturbation selection and local influence analysis for nonlinear structural equation model. Psychometrika. 2009;74(3):493–516.
- Zhu HT, Lee SY. Local influence for incomplete-data models. J R Stat Soc Ser B. 2001;63:111–126.
- Demidenko E, Stukel TA. Influence analysis for linear mixed-effects models. Stat Med. 2005;24:893–909.
- Cook RD. Assessment of local influence (with discussion). J R Stat Soc Ser B. 1986;48:133–169.
- Poon W, Poon YS. Conformal normal curvature and assessment of local influence. J R Stat Soc Ser B. 1999;61:51–61.
- Zhu H, Ibrahim JG, Lee S, et al. Perturbation selection and influence measures in local influence analysis. Ann Stat. 2007;35(6):2565–2588.
- Fung WK, Kwan CW. A note on local influence based on normal curvature. J R Stat Soc Ser B. 1997;59:839–843.
- Giménez P, Galea M. Influence measures on corrected score estimators in functional heteroscedastic measurement error models. J Multivar Anal. 2013;114:1–15.
- Galea M, Giménez P. Local influence diagnostics for the test of mean-variance efficiency and systematic risks in the capital asset pricing model. Stat Pap. 2019;60:293–312.
- Cox DR, Reid N. Parameter orthogonality and approximate conditional inference. J R Stat Soc Ser B. 1987;49:1–39.
- R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2021.
- Harville DA. Matrix algebra from a statistician's perspective. New York: Springer; 1997.