MJIS

Some Aspects on the Utility of Distance Measures in Comparing Two MROC Curves

Sameera G And Vishnu Vardhan R

KEYWORDS

Bhattacharya Distance, Mahalanobis Distance, Mean vectors and Dispersion matrices and Multivariate Receiver Operating Characteristic curve.

PUBLISHED DATE
PUBLISHER
ABSTRACT

Receiver Operating Characteristic (ROC) curve is a widely used and accepted tool to assess the performance of a classifier or procedure. Apart from this, comparing the procedures or ROC curves is also of interest. A multivariate extension of ROC (MROC) curve that considers a linear combination of several markers for classification was proposed by Sameera, Vishnu Vardhan and Sarma [13]. In the present paper, some inferential procedures are given to compare two MROC curves by means of distance measures based on scores of MROC curve and summary measures such as mean vectors and dispersion matrices. Real and Simulated data sets are used to demonstrate the above proposed inferential aspects.

Page(s)
URL http://dspace.chitkara.edu.in/jspui/bitstream/1/789/3/51006_MJIS_Vardhan.pdf
ISSN
DOI 10.15415/mjis.2016.51006
REFERENCES
  • Aiyi Liu, Schisterman, E. F. & Yan Zhu (2005). On linear combinations of biomarkers to improve diagnostic accuracy. Statistics in Medicine, 24, 37–47. http://dx.doi.org/10.1002/sim.1922
  • Bendi Venkata Ramana, Prof. M. Surendra Prasad Babu & Prof. N. B. Venkateswarlu (2012). ILPD (Indian Liver Patient Dataset) Data Set, https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+ Datset).
  • Feng Gao, Chengjie Xiong, Yan Yan &Zhengjun Zhang (2008). Estimating optimum linear combination of multiple correlated diagnostic tests at a fixed specificity with receiver operating characteristic curves. Journal of Data Science, 6, 1–13.
  • Gourevitch, V. & Galanter, E. (1967). A significance test for one parameter isosensitivity functions. Psychometrika, 32, 25–33. http://dx.doi.org/10.1007/BF02289402
  • Greenhouse, S.W. & Nathan Mantel (1950). The evaluation of diagnostic tests. Biometrics 6(4), 399–412. http://dx.doi.org/10.2307/3001784
  • Hanley, J. A. & McNeil, B. J. (1982). A meaning and use of the area under a receiver operating characteristics (roc) curves. Radiology, 143, 29–36. http://dx.doi.org/10.1148/radiology.143.1.7063747
  • Hanley, J. A. & McNeil, B. J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148, 839–843. http://dx.doi.org/10.1148/radiology.148.3.6878708
  • John, Q. Su & Jun, S. Liu (1993). Linear combinations of multiple daignostic markers. Journal of American Statistical Association, 88(424), 1350–1355. http://dx.doi.org/10.1080/01621459.1993.10476417
  • Johnson, R.A. & Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. Pearson Prentice Hall, 6/e.
  • Marascuilo, L. A. (1970). Extension of the significance test for one parameter signal detection hypotheses. Psychometrika, 35, 237–243. http://dx.doi.org/10.1007/BF02291265
  • McClish D. K. (1987). Comparing the areas under more than two independent roc curves. Medical Decision Making, 7, 149–155. http://dx.doi.org/10.1177/0272989X8700700305
  • Metz, C. E. & Kronman, H. B. (1980). Statistical significance tests for binormal roc curves. Journal of Mathematical Psychology, 22, 243–243. http://dx.doi.org/10.1016/0022-2496(80)90020-6
  • Sameera, G. Vishnu Vardhan, R & Sarma, KVS (2015). Binary Classification using Multivariate Receiver Operating Characteristic curve for Continuous Data. Journal of Biopharmaceutical Statistics, http://dx.doi.org/10.108010543406.2015.1052479.
  • Vishnu Vardhan, R. Sameera, G. Chandrasekharan, P.A. & Thulasi Beere (2015). Inferential Procedures for Comparing the Accuracy and Intrinsic Measures of Multivariate Receiver Operating Characteristic (MROC) Curve. International Journal of Statistics in Medical Research, 4, 87-93. http://dx.doi.org/10.6000/1929-6029.2015.04.01.10