References
- Attia, M. A., E. A. Sallam, and M. M. Fahmy. 2012. A proposed generalized mean single multiplicative neuron model. Computer Engineering and Systems 73–8.
- Basu, A., I. Harris, L. Hjort, and M. Jones. 1998. Robust and efficient estimation by minimising a density power divergence. Biometrika 85 (3):549–59. doi: 10.1093/biomet/85.3.549.
- Berger, R. L., and G. Casella. 1992. Deriving generalized means as least squares and maximum likelihood estimates. The American Statistician 46 (4):279–82. doi: 10.2307/2685312.
- Bhattacharya, S., A. Gupta, and A. K. Chattopadhyay. 2009. Effect of migration on population growth under dynamical system. Journal of Applied Probability and Statistics 4 (2):239–53.
- Cassie, R. M. 1954. Some uses of probability paper in the analysis of size frequency distributions. Marine and Freshwater Research 5 (3):513–22. doi: 10.1071/MF9540513.
- Chakraborty, B., S. Bhattacharya, A. Basu, S. Bandyopadhyay, and A. Bhattacharjee. 2014. Goodness-of-fit testing for the Gompertz growth curve model. Metron 72 (1):45–64. doi: 10.1007/s40300-013-0030-z.
- Fleischman, S. J., and D. L. Burwen. 2003. Mixture models for the species apportionment of hydroacoustic data, with echo-envelope length as the discriminatory variable. ICES Journal of Marine Science 60 (3):592–8. doi: 10.1016/S1054-3139(03)00041-9.
- Georgi, B., I. G. Costa, and A. Schliep. 2010. PyMix – The python mixture package – A tool for clustering of heterogeneous biological data. BMC Bioinformatics 1:1–9. doi: 10.1186/1471-2105-11-9.
- Hampel, F. 1971. A general qualitative definition of robustness. The Annals of Mathematical Statistics 42 (6):1887–96. doi: 10.1214/aoms/1177693054.
- Huber, P. 1964. Robust estimation of a location parameter. The Annals of Mathematical Statistics 35 (1):73–101. doi: 10.1214/aoms/1177703732.
- Lindsay, B. 1994. Efficiency versus robustness: The case for minimum Hellinger distance and related methods. The Annals of Statistics 22 (2):1081–114. doi: 10.1214/aos/1176325512.
- Medrano-Soto, A., J. A. Christen, and J. Collado-Vides. 2005. BClass: A Bayesian approach based on mixture models for clustering and classification of heterogeneous biological data. Journal of Statistical Software 13 (2):1–18. doi: 10.18637/jss.v013.i02.
- Mukhopadhyay, S., A. Hazra, A. R. Bhowmick, and S. Bhattacharya. 2016. On comparison of relative growth rates under different environmental conditions with application to biological data. Metron 74 (3):311–37. doi: 10.1007/s40300-016-0102-y.
- Titterington, D. M., A. F. M. Smith, and U. E. Markov. 1985. Statistical analysis of finite mixture distributions. Chichester, UK: John Willey and Sons.
- Tripathi, B. K., and P. K. Kalra. 2011. On efficient learning machine with root-power mean neuron in complex domain. IEEE Transactions on Neural Networks 22 (5):727–38. doi: 10.1109/TNN.2011.2115251.