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Julio1
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Find maximum likelihood estimators of an sample of size $n$ if $X\sim U(0,\theta].$
Hello MHB :)! Can any user help me please :)! I don't how follow...
Hello MHB :)! Can any user help me please :)! I don't how follow...
Julio said:Find maximum likelihood estimators of an sample of size $n$ if $X\sim U(0,\theta].$
Hello MHB :)! Can any user help me please :)! I don't how follow...
A Maximum Likelihood Estimator (MLE) is a statistical method used to estimate the parameters of a probability distribution. It is based on the principle of maximum likelihood, which states that the most likely values of the parameters are those that make the observed data most probable.
A Maximum Likelihood Estimator is calculated by finding the values of the parameters that maximize the likelihood function, which is a function of the parameters and the observed data. This can be done analytically or numerically through optimization methods.
The main assumptions of Maximum Likelihood Estimators are that the data is independent and identically distributed, and that the probability distribution being used to model the data is the true distribution.
The advantages of using Maximum Likelihood Estimators include their consistency, efficiency, and asymptotic normality. They also have good statistical properties, such as being unbiased and having a lower variance compared to other estimation methods.
Maximum Likelihood Estimation may not be appropriate when the assumptions of the method are violated, such as when the data is not normally distributed or when there are outliers present. In these cases, alternative estimation methods may be more suitable.