References
- Basu, A., and B. G. Lindsay. 1994. Minimum disparity estimation for continuous models: Efficiency, distributions and robustness. Annals of the Institute of Statistical Mathematics 46 (4):683–705. doi:https://doi.org/10.1007/BF00773476.
- Brabanter, K. D., J. D. Brabanter, J. A. K. Suykens, and B. D. Moor. 2011. Kernel regression in the presence of correlated errors. Journal of Machine Learning Research 12:1955–76.
- Choi, K. L., and J. Shim. 2013. Variable selection in censored kernel regression. Journal of the Korean Data and Information Science Society 24:201–9. doi:https://doi.org/10.7465/jkdi.2013.24.1.201.
- Cochrane, D., and G. H. Orcutt. 1949. Application of least squares regression to relationships containing auto-correlated error terms. Journal of the American Statistical Association 44:32–61. doi:https://doi.org/10.2307/2280349.
- Draper, N. R., and H. Smith. 1998. Applied regression analysis. USA: Wiley.
- Holland, P. W., and R. E. Welsch. 1977. Robust regression using iteratively reweighted least squares. Communications in Statistics - Theory and Methods 6 (9):813–27. doi:https://doi.org/10.1080/03610927708827533.
- Kim, J. 2017. Generalized minimum distance estimators in linear regression with dependent errors. Cornell University arXiv.org, math, arXiv:1701.01199.
- Kim, J. 2019. Minimum distance estimation in linear regression with strong mixing errors. Communications in Statistics- Theory and Method 49 (6):1475–94.
- Kolmogorov, A. N. 1957. On the representation of continuous functions of several variables by superpositions of continuous functions of one variable and addition. Doklady 114:679–81.
- Lorentz, G. G. 1966. Approximation of functions. New York, NY: Holt, Rinehart and Winston.
- Magee, L. 1987. A note on Cochran-Orcutt estimation. Journal of Econometrics 35 (2–3):211–18. doi:https://doi.org/10.1016/0304-4076(87)90024-8.
- Markatou, M. 1996. Robust statistical inference: Weighted likelihood or usual M-estimation. Communications in Statistics - Theory and Methods 25 (11):2597–613. doi:https://doi.org/10.1080/03610929608831858.
- Matusita, K. 1953. On the estimation by the minimum distance method. Annals of the Institute of Statistical Mathematics 5 (2):59–65. doi:https://doi.org/10.1007/BF02949801.
- Matusita, K. 1955. Decision rules, based on the distance for problems of fit, two samples, and estimation. The Annals of Mathematical Statistics 26 (4):631–40. doi:https://doi.org/10.1214/aoms/1177728422.
- Matusita, K. 1964. Distance and decision rules. Annals of the Institute of Statistical Mathematics 16 (1):305–20. doi:https://doi.org/10.1007/BF02868578.
- Nadaraya, E. A. 1964. On estimating regression. Theory of Probability & Its Applications 9 (1):141–2. doi:https://doi.org/10.1137/1109020.
- Nahar, J., and S. Purwani. 2017. Application of robust M-estimator regression in handling data outliers. In 4th ICRIEMS Proceedings, 53–60. Yogyakarta: Faculty of Mathematics and Natural Sciences, Yogyakarta State University.
- Smadi, A. A., and N. H. Abu-Afouna. 2012. An extension of Cochran-Orcutt procedure for generalized linear regression models with periodically correlated errors. Journal of Modern Applied Statistical Methods 11 (2):407–15. doi:https://doi.org/10.22237/jmasm/1351743120.
- Quackenbush, G. G., and J. D. Shaffer . 1955. Factors affecting purchases of ice cream for home use. Department of Agricultural Economics, Michigan State University, Technical Bulletin 249: 28 p.
- Watson, G. S. 1964. Smooth regression analysis. Sankhya Series, A 26:141–2.