Open AccessMaximum likelihood solution for inclination‐only data in paleomagnetism
Author(s) -
Arason P.,
Levi S.
Publication year - 2010
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.2010.04671.x
Subject(s) - likelihood function , mathematics , function (biology) , statistics , exponential function , maximum likelihood , dispersion (optics) , exponential family , marginal likelihood , algorithm , mathematical analysis , physics , evolutionary biology , optics , biology
SUMMARY We have developed a new robust maximum likelihood method for estimating the unbiased mean inclination from inclination‐only data. In paleomagnetic analysis, the arithmetic mean of inclination‐only data is known to introduce a shallowing bias. Several methods have been introduced to estimate the unbiased mean inclination of inclination‐only data together with measures of the dispersion. Some inclination‐only methods were designed to maximize the likelihood function of the marginal Fisher distribution. However, the exact analytical form of the maximum likelihood function is fairly complicated, and all the methods require various assumptions and approximations that are often inappropriate. For some steep and dispersed data sets, these methods provide estimates that are significantly displaced from the peak of the likelihood function to systematically shallower inclination. The problem locating the maximum of the likelihood function is partly due to difficulties in accurately evaluating the function for all values of interest, because some elements of the likelihood function increase exponentially as precision parameters increase, leading to numerical instabilities. In this study, we succeeded in analytically cancelling exponential elements from the log‐likelihood function, and we are now able to calculate its value anywhere in the parameter space and for any inclination‐only data set. Furthermore, we can now calculate the partial derivatives of the log‐likelihood function with desired accuracy, and locate the maximum likelihood without the assumptions required by previous methods. To assess the reliability and accuracy of our method, we generated large numbers of random Fisher‐distributed data sets, for which we calculated mean inclinations and precision parameters. The comparisons show that our new robust Arason–Levi maximum likelihood method is the most reliable, and the mean inclination estimates are the least biased towards shallow values.