Journal of Statistics: Advances in Theory and Applications[1] A. A. Adepoju and M. Adegbite, Application of ordinal logistic
regression model to occupational data, J. Sci. Ind. Stud. 7 (2009),
39-49.
[2] A. Agresti, Analysis of Ordinal Categorical Data, Wiley, 2nd
Edition, 2010.
[3] I. E. Allen and C. A. Seaman, Likert scales and data analysis,
Quality Progress 40(7) (2007), 64-65.
[4] M. Catalogna, E. Cohen, S. Fishman, Z. Halpern, U. Nevo and E.
Ben-Jacob, Artificial neural networks based controller for glucose
monitoring during clamp test, PloS One. 7: e44587, 2012.
[5] J. R. Chimka and H. Wolfe, History of ordinal variables before
1980, Scientific Research and Essay 4(9) (2009), 853-860.
[6] W. Deng, W. Chen and W. Pei, Back-propagation neural network based
importance-performance analysis for determining critical service
attributes, Expert Systems with Applications 34(2) (2008),
1115-1125.
[7] Y. Dong, Logistic Regression Models for Ordinal Response, Texas
Medical Center Dissertations, 2007,
http://digitalcommons.library.tmc.edu/dissertations
/AA11445931.
[8] P. Dey, A. Lamba, S. Kumari and N. Marwaha, Application of an
artificial neural network in the prognosis of chronic myeloid
leukemia, Anal. Quant. Cytol. Histol. 33 (2012), 335-339.
[9] R. C. Eberhart and R. W. Dobbins, Neural Network PC Tools: A
Practical Guide, Academic Press, San Diego, California, USA, 1990.
[10] E. Elveren and N. Yumuşak, Tuberculosis disease diagnosis using
artificial neural network trained with genetic algorithm, J. Med.
Syst. 35 (2011), 329-332.
[11] O. Er, F. Temurtas and A. Tanrýkulu, Tuberculosis disease
diagnosis using artificial neural networks, J. Med. Syst. 34 (2008),
299-302.
[12] Kevin L. Fox, Rhonda R. Henning and Jonathan H. Reed, A Neural
Network Approach Towards Intrusion Detection, In Proceedings of the
13th National Computer Security Conference (1990).
[13] P. Fedor, I. Malenovsky, J. Vanhara, W. Sierka and J. Havel,
Thrips (Thysanoptera) identification using artificial neural networks,
Bull. Entomol. Res. 98 (2008), 437-447.
[14] J. Fernandez de Canete, S. Gonzalez-Perez and J. C. Ramos-Diaz,
Artificial neural networks for closed loop control of in silico and ad
hoc type 1 diabetes, Comput. Meth. Progr. Biomed. 106 (2012),
55-66.
[15] S. I. Gallant, Neural Network Learning and Expert Systems, MIT
Press, Cambridge, MA, 1993.
[16] T. G. Gregoire and B. L. Driver, Analysis of ordinal data to
detect population differences, Psychological Bulletin 101(1) (1987),
159-165.
[17] T. J. Hastie, J. L. Botha and C. M. Schnitzler, Regression with
an ordered categorical response, Statistics in Medicine 8(7) (1989),
785-794.
[18] J. Havel, E. Peña, A. Rojas-Hernández, J. Doucet and A. Panaye,
Neural networks for optimization of highper formance capillary zone
electrophoresis methods, J. Chromatogr. A 793 (1998), 317-329.
[19] S. Jamieson, Likert scales: how to (ab)use them, Medical
Education 38(12) (2004), 1217-1218.
[20] M. G. Karlaftis and E. I. Vlahogianni, Statistics versus neural
networks in transportation research: Differences, similarities and
some insights, Submitted to Transportation Research Part C: Emerging
Technologies 19(3) (2011), 387-399.
[21] T. R. Knapp, Treating ordinal scales as interval scales: An
attempt to resolve the controversy, Nurs. Res. 39(2) (1990),
121-123.
[22] L. Mayer, A note on treating ordinal data as interval data,
American Sociological Review 36(3) (1971), 519-520.
[23] V. Michalkova, A. Valigurova, M. Dindo and J. Vanhara, Larval
morphology and anatomy of the parasitoid Exorista larvarum (Diptera:
Tachinidae), with an emphasis on cephalopharyngeal skeleton and
digestive tract, J. Parasitol. 95 (2009), 544-554.
[24] N. Murarikova, J. Vanhara, A. Tothova and J. Havel, Polyphasic
approach applying artificial neural networks, molecular analysis and
post abdomen morphology to West Palaearctic Tachina spp. (Diptera,
Tachinidae), Bull. Entomol. Res. 101 (2010), 165-175.
[25] National Kidney Foundation, K/DOQI clinical practice guidelines
for chronic kidney disease, Retrieved 2008-06-29 (2002).
[26] M. Nelson and W. T. Illingworth, A Practical Guide to Neural
Nets, Addison-Wesley, Reading, MA, 1990.
[27] M. Paliwal and U. A. Kumar, Neural networks and statistical
techniques: A review of applications, Expert Systems with Applications
36(1) (2009), 2-17.
[28] Paulo J. Lisboa and Azzam F. G. Taktak, The use of artificial
neural networks in decision support in cancer: A systematic review-
School of Computing and Mathematical Science, Liverpool John Moores
University, Liverpool, UK, Department of Clinical Engineering, Royal
Liverpool University Hospital, Liverpool, UK, Neural Networks-Volume
19(4) (2006); M. G. Penedo, M. J. Carreira, A. Mosquera and D.
Cabello, Computer-aided diagnosis: a neural-network-based approach to
lung nodule detection, IEEE Trans. Medical Imaging 17(6) (1998).
[29] Galdón B. Rodríguez, E. Pená-Méndez, J. Havel, Rodríguez E.
Rodríguez and Romero C. Díaz, Cluster analysis and artificial neural
networks multivariate classification of onion varieties, J. Agric.
Food. Chem. (2010), 11435-11440.
[30] Tutz, Regression for Categorical Data, Volume 34, Cambridge
University Press, 2012.
[31] P. F. Velleman and W. Leland, Nominal, ordinal, interval, and
ratio typologies are misleading, The American Statistician 47(1)
(1993), 65-72.
[32] H. White, Some asymptotic results for learning in single hidden
layer feed forward network models, Journal of American Statistical
Association 84 (1989).
