[amp_mcq option1=”they are biased” option2=”they have high variance” option3=”they are not consistent estimators” option4=”none of the above” correct=”option2″]<\/p>\n
The correct answer is: B. they have high variance<\/strong>.<\/p>\n Maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. It is based on the principle of maximizing the likelihood function, which is a function of the parameters and the data.<\/p>\n MLE estimates are often desirable because they are unbiased and efficient. However, they can have high variance, which means that they can be unstable and fluctuate widely from sample to sample.<\/p>\n Option A is incorrect because MLE estimates are unbiased. This means that the expected value of the MLE is equal to the true value of the parameter.<\/p>\n Option C is incorrect because MLE estimates are consistent estimators. This means that the MLE converges to the true value of the parameter as the sample size increases.<\/p>\n Option D is incorrect because MLE estimates are often desirable. They are unbiased and efficient, but they can have high variance.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”they are biased” option2=”they have high variance” option3=”they are not consistent estimators” option4=”none of the above” correct=”option2″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-46552","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n