What is robust maximum likelihood estimation?
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable.
Are maximum likelihood estimators consistent?
The maximum likelihood estimator (MLE) is one of the backbones of statistics, and common wisdom has it that the MLE should be, except in “atypical” cases, consistent in the sense that it converges to the true parameter value as the number of observations tends to infinity.
Which is an example of a robust estimator?
Many estimators have ‘robust’ variants, meaning that they provide robust standard errors and a scaled test statistic. For example, for the maximum likelihood estimator, lavaan provides the following robust variants: “MLM”: maximum likelihood estimation with robust standard errors and a Satorra-Bentler scaled test statistic.
Which is the maximum likelihood estimator in lavaan?
“MLR”: maximum likelihood estimation with robust (Huber-White) standard errors and a scaled test statistic that is (asymptotically) equal to the Yuan-Bentler test statistic. For both complete and incomplete data. For the DWLS and ULS estimators, lavaan also provides ‘robust’ variants: WLSM, WLSMVS, WLSMV, ULSM, ULSMVS, ULSMV.
Which is the maximum likelihood estimator used in Mplus?
The value of the test statistic will be closer to the value reported by programs like EQS, LISREL or AMOS, since they all use the ‘Wishart’ approach when using the maximum likelihood estimator. The program Mplus, on the other hand, uses the ‘normal’ approach to maximum likelihood estimation.
Which is the robust variant of the DWLS estimator?
For both complete and incomplete data. For the DWLS and ULS estimators, lavaan also provides ‘robust’ variants: WLSM, WLSMVS, WLSMV, ULSM, ULSMVS, ULSMV. Note that for the robust WLS variants, we use the diagonal of the weight matrix for estimation, but we use the full weight matrix to correct the standard errors and to compute the test statistic.