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http://dx.doi.org/10.29220/CSAM.2019.26.5.463

A robust method for response variable transformations using dynamic plots  

Seo, Han Son (Department of Applied Statistics, Konkuk University)
Publication Information
Communications for Statistical Applications and Methods / v.26, no.5, 2019 , pp. 463-471 More about this Journal
Abstract
The variable transformations are useful ways to guarantee the functional relationships in the model. However, the presence of outliers may undermine the accuracy of transformation. This paper deals with response transformations in the partial linear models under the existence of outliers. A new procedure for response transformation and outliers detection is proposed. The procedure uses a sequential method for identifying outliers and dynamic graphical methods for an appropriate transformation. The graphical tools make it possible to catch diagnostic information by monitoring the movement of points in the data. The procedure is illustrated with several examples. Examples show that visual clues regarding the optimal transformation, the fittness of the model and the outlyness of the observations can be checked from the series of plots.
Keywords
diagnostics; dynamic plots; outliers; partial linear models; variable transformations;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Atkinson AC (1994). Fast very robust methods for the detection of multiple outliers, Journal of the American Statistical Association, 89, 1329-1339.   DOI
2 Atkinson AC and Riani M (2000). Robust Diagnostic Regression Analysis, Springer, New York.
3 Cheng T (2005). Robust regression diagnostics with data transformations, Computational Statistics and Data Analysis, 49, 875-891.   DOI
4 Cook RD (1993). Exploring partial residual plots, Technometrics, 35, 351-362.   DOI
5 Cook RD and Weisberg S (1994). Transforming a response variable for linearity, Biometrika, 81, 731-737.   DOI
6 Hadi AS and Simonoff JS (1993). Procedures for the identification of multiple outliers in linear models, Journal of the American Statistical Association, 88, 1264-1272.   DOI
7 Hartigan JA (1981). Consistency of single linkage for high-density clusters, Journal of the American Statistical Association, 76, 388-394.   DOI
8 Larsen WA and McCleary SJ (1972). The use of partial residual plots in regression analysis, Technometrics, 14, 781-790.   DOI
9 Mallows CL (1986). Augmented partial residual plots, Technometrics, 28, 313-320.   DOI
10 Rosner B (1975). On the Detection of Many Outliers, Technometrics, 17, 217-227.
11 Rousseeuw PJ (1984). Least median of squares regression, Journal of American Statistical Association, 79, 871-880.   DOI
12 Seo HS (2009). A visual procedure for optimal response transformation and curvature specifications, Optimization and Engineering, 10, 301-312.   DOI
13 Seo HS, Lee GY, and Yoon M (2012). Robust response transformation using outlier detection in regression model, The Korean Journal of Applied Statistics, 25, 205-213.   DOI
14 Seo HS and Yoon M (2009). A dynamic graphical method for transformations and curvature specifications in regression, The Korean Journal of Applied Statistics, 22, 189-195.   DOI
15 Seo HS and Yoon M (2013). Regression diagnostics for response transformations in a partial linear model, Journal of the Korean Data & Information Science Society, 24, 33-39.   DOI
16 Simonoff JS (1984). The calculation of outlier detection statistics, Communications in Statistics, Part B-Simulation and Computation, 13, 275-285.   DOI
17 Weisberg S (2005). Applied Linear Regression, Wiley, New York.
18 Simonoff JS (1988). Detecting outlying cells in two-way contingency tables via backwards-stepping, Technometrics, 30, 339-345.   DOI
19 Stromberg AJ (1993). Computation of high breakdown nonlinear regression parameters, Journal of the American Statistical Association, 88, 237-244.   DOI
20 Tierney L (1990). LISP-STAT: An Object-Oriented Environment for Statistical Computing and Dynamic Graphics, John Wiley & Sons, New York.
21 Yohai VJ (1987). High breakdown-point and high efficiency robust estimate for regression, The Annals of Statistics, 15, 642-656.   DOI
22 Chambers JM, Cleveland WS, Kleiner B, and Tukey P (1983). Graphical Methods for Data Analysis,Duxbury Press, Boston.