Which of the following is the sensitive disadvantage of maximum likelihood?

Disadvantages of Maximum Likelihood Estimation Like other optimization problems, maximum likelihood estimation can be sensitive to the choice of starting values. Depending on the complexity of the likelihood function, the numerical estimation can be computationally expensive. Estimates can be biased in small samples.

Why is maximum likelihood estimator biased?

It is well known that maximum likelihood estimators are often biased, and it is of use to estimate the expected bias so that we can reduce the mean square errors of our parameter estimates. In both problems, the first-order bias is found to be linear in the parameter and the sample size.

Is maximum likelihood unbiased?

Therefore, the maximum likelihood estimator is an unbiased estimator of .

Which of the following does not hold true for maximum likelihood sequence estimation?

Which of the following does not hold true for MLSE? Explanation: Matched filter operates on the continuous time signal, whereas maximum likelihood sequence estimation (MLSE) equalizer and channel estimator rely on discretized samples. MLSE is optimal in the sense that it minimizes the probability of sequence error. 10.

What do you understand by 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.