In probability theory and statistics, empirical likelihood (EL) is a nonparametric method for estimating the parameters of statistical models. It requires fewer assumptions about the error distribution while retaining some of the merits in likelihood-based inference. The estimation method requires that the data are independent and identically distributed (iid). It performs well even when the distribution is asymmetric or censored.[1] EL methods can also handle constraints and prior information on parameters. Art Owen pioneered work in this area with his 1988 paper.[2]
- ^ Owen, Art B. (2001). Empirical likelihood. Boca Raton, Fla. ISBN 978-1-4200-3615-2. OCLC 71012491.
{{cite book}}: CS1 maint: location missing publisher (link) - ^ Owen, Art B. (1988). "Empirical likelihood ratio confidence intervals for a single functional". Biometrika. 75 (2): 237–249. doi:10.1093/biomet/75.2.237. ISSN 0006-3444.