[Balasco, M., Lapenna, V., Lovallo, M., Romano, G., Siniscalchi, A. and Telesca, L. (2008). Fisher information measure analysis of earth’s apparent resistivity. International Journal of Nonlinear Science 5(3) 230-236.]Search in Google Scholar
[Dulek, B. and Gezici, S. (2014). Average Fisher information maximisation in presence of cost-constrained measurements. Electronics Letters 47(11) 654-656.]Search in Google Scholar
[Frank, S.A. (2009). Natural selection maximizes Fisher information. Journal of Evolutionary Biology 22(2) 231-244.10.1111/j.1420-9101.2008.01647.x19032501]Search in Google Scholar
[Frieden, B.R. (2009). Fisher information, disorder, and the equilibrium distributions of physics. Physical Review A-41(8) 4265-4276.]Search in Google Scholar
[Hussin, A.G., Abuzaid, A., Zulkifili, F. and Mohamed I. (2010). Asymptotic covariance and detection of influential observations in a linear functional relationship model for circular data with application to the measurements of wind directions. Science Asia, 36, 249-253.10.2306/scienceasia1513-1874.2010.36.249]Search in Google Scholar
[Khoolenjani, N.B. and Alamatsaz, M.H. (2016). A De Bruijn’s identity for dependent random variables based on copula theory. Probability in the Engineering and Informational Sciences, 30(1), 125-140.10.1017/S0269964815000315]Search in Google Scholar
[Lehmann, E.L. and Casella, G. (1998). Theory of Point Estimation, 2nd ed, New York: Springer.]Search in Google Scholar
[Mamun, S.M.A., Hussin, G.A., Zubairi, Y.Z. and Imon, R.A.H.M. (2013). Maximum likelihood estimation of linear structural relationship model parameters assuming the slope is known. Science Asia, 39, 561-565.10.2306/scienceasia1513-1874.2013.39.561]Search in Google Scholar
[Martin, M.T., Pennini, F. and Plastino, A. (2009). Fisher’s information and the analysis of complex signals. Physics Letters A-256(2-3) 173-180.]Search in Google Scholar
[Nagy, A. (2003). Fisher information in density functional theory. The Journal of Chemical Physics 119(18) 9401-9405.10.1063/1.1615765]Search in Google Scholar
[Neri, A., Carli, M. and Battisti, F. (2013). Maximum likelihood estimation of depth field for trinocular images. Electronics Letters 49(6) 394-396.10.1049/el.2012.2978]Search in Google Scholar
[Park, S., Serpedin, E. and Qaraqe, K. (2013). Gaussian assumption: The least favorable but the most useful. IEEE Signal Processing Magazine 30 183-186.10.1109/MSP.2013.2238691]Search in Google Scholar
[Shao, J. (1999). Mathematical statistics, New York: Springer-Verlag.]Search in Google Scholar
[Stoica, P. and Babu, P. (2011). The Gaussian data assumption leads to the largest Cramer-Rao bound. IEEE Signal Processing Magazine 28 132-133.10.1109/MSP.2011.940411]Search in Google Scholar
[Vignat, C. and Bercher, J.F. (2003). On Fisher information inequalities and score functions in non-invertible linear systems. Journal of Inequalities in Pure and Applied Mathematics 4(4) 71.]Search in Google Scholar
[Xu, B., Chen, Q., Wu, Z. and Wang, Z. (2008). Analysis and approximation of performance bound for two-observer bearings-only tracking. Information Sciences 178(8) 2059-2078.10.1016/j.ins.2007.12.004]Search in Google Scholar
[Zivojnovic, V. and Noll, D. (1997). Minimum Fisher information spectral analysis. ICASSP-97, IEEE International Conference on Acoustics, Speech, and Signal Processing 5.10.1109/ICASSP.1997.604786]Search in Google Scholar