참고문헌
- Atkinson AC (1986). [Influential observations, high leverage points, and outliers in linear regression]: comment: aspects of diagnostic regression analysis, Statistical Science, 1, 397-402. https://doi.org/10.1214/ss/1177013624
- Barbieri MM and Berger JO (2004). Optimal predictive model selection, The Annals of Statistics, 32, 870-897. https://doi.org/10.1214/009053604000000238
- Bayarri MJ, Berger JO, Forte A, and Donato GG (2012). Criteria for Bayesian model choice with application to variable selection, The Annals of Statistics, 40, 1550-1577. https://doi.org/10.1214/12-AOS1013
- Belsley DA, Kuh E, and Welsch RE (1980). Regression Diagnostics, Wiley, New York.
- Choi IH, Park CG, and Lee KE (2018). Outlier detection and variable selection via difference based regression model and penalized regression, Journal of the Korean Data & Information Science Society, 29, 815-825. https://doi.org/10.7465/jkdi.2018.29.3.815
- Donato GG and Forte A (2017). BayesVarSel : Bayes factors, model choice and variable selection in linear models, R package version 1.8.0 Available on line access from https://cran.rproject.org/web/packages/BayesVarSel/BayesVarSel.pdf
- George EI and McCulloch RE (1993). Variable selection via Gibbs sampling, Journal of the American Statistical Association, 88, 881-889. https://doi.org/10.1080/01621459.1993.10476353
- George EI and McCulloch RE (1997). Approaches for Bayesian variable selection, Statistica Sinica, 7, 339-373.
- Gelman A and Rubin DB (1992). Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511. https://doi.org/10.1214/ss/1177011136
- Hoeting J, Raftery AE, and Madigan D (1996). A method for simultaneous variable selection and outlier identification in linear regression, Computational Statistics and Data Analysis, 22, 251-270. https://doi.org/10.1016/0167-9473(95)00053-4
- Kahng MW, Kim YI, Ahn CH, and Lee YG (2016). Regression Analysis (2nd ed), Yulgok, Seoul.
- Kim S, Park SH, and Krzanowski WJ (2008). Simultaneous variable selection and outlier identification in linear regression using the mean-shift outlier model, Journal of Applied Statistics, 35, 283-291. https://doi.org/10.1080/02664760701833040
- Menjoge RS and Welsch RE (2010). A diagnostic method for simultaneous feature selection and outlier identification in linear regression, Computational Statistics and Data Analysis, 54, 3181-3193. https://doi.org/10.1016/j.csda.2010.02.014
- Park CG (2018). A study on robust regression estimators in heteroscedastic error models, Journal of the Korean Data & Information Science Society, 29, 339-350. https://doi.org/10.7465/jkdi.2018.29.2.339
- Park CG and Kim I (2018a). Outlier detection using difference-based variance estimators in multiple regression, Communications in Statistics - Theory and Methods, 47, 5986-6001. https://doi.org/10.1080/03610926.2017.1404101
- Park CG and Kim I (2018b). Outlier detection using difference based regression Model, Communications in Statistics - Theory Methods, under review.
- Park CG, Kim I, and Lee Y (2012). Error variance estimation in nonparametric regression under Lipschitz condition and small sample size, Journal of Statistical Planning and Inference, 142, 2369-2385. https://doi.org/10.1016/j.jspi.2012.02.050
- Rousseeuw PJ (1984). Least median of squares regression, Journal of the American Statistical Association, 79, 871-888. https://doi.org/10.1080/01621459.1984.10477105
- Weisberg S (2004). Applied Linear Regression (3rd ed.), Wiley,