A Comparison Of Maximum-Likelihood And Least-Squares For The Estimation Of A Cumulative Distribution
Abbreviated Journal Title
J. Stat. Comput. Simul.
Computer Science; Interdisciplinary Applications; Statistics; Probability
Monte Carlo methods are used to compare the methods of maximum likelihood and least squares to estimate a cumulative distribution function. When the probabilistic model used is correct or nearly correct, the two methods produce similar results with the MLE usually slightly superior When an incorrect model is used, or when the data is contaminated, the least squares technique often gives substantially superior results.
Journal of Statistical Computation and Simulation
Somerville, Paul N. and Bean, S. J., "A Comparison Of Maximum-Likelihood And Least-Squares For The Estimation Of A Cumulative Distribution" (1982). Faculty Bibliography 1980s. 200.