4 1. The Fisher Eﬃciency Example 1.2. Suppose n independent observations X1,...,Xn come from a distribution with density p(x, θ) = p 0 (x − θ), −∞ x, θ ∞, where p 0 is a fixed probability density function. Here θ determines the shift of the distribution, and therefore is termed the location parameter. The location parameter model can be written as Xi = θ + εi, i = 1,...,n, where ε1,...,εn are independent random variables with a given density p 0 , and θ ∈ Θ = R. The independence of observations implies that the joint density of Xi’s equals p (x1,...,xn, θ) = n i = 1 p (xi, θ). We denote the respective expectation by Eθ[ · ] and variance by Varθ[ · ]. In a statistical experiment, all observations are obtained under the same value of an unknown parameter θ. The goal of the parametric statistical estimation is to assess the true value of θ from the observations X1,...,Xn. An arbitrary function of observations, denoted by ˆ = ˆ n = ˆ n (X1,...,Xn), is called an estimator (or a point estimator) of θ. A random variable l(Xi , θ) = ln p(Xi , θ) is referred to as a log-likelihood function related to the observation Xi. The joint log-likelihood function of a sample of size n (or, simply, the log- likelihood function) is the sum Ln(θ) = Ln(θ | X1 , . . . , Xn) = n i = 1 l(Xi , θ) = n i = 1 ln p(Xi , θ). In the above notation, we emphasize the dependence of the log-likelihood function on the parameter θ, keeping in mind that it is actually a random function that depends on the entire set of observations X1,...,Xn. The parameter θ may be evaluated by the method of maximum likelihood estimation. An estimator θ ∗ n is called the maximum likelihood estimator (MLE), if for any θ ∈ Θ the following inequality holds: Ln(θn) ∗ ≥ Ln(θ). If the log-likelihood function attains its unique maximum, then the MLE reduces to θ ∗ n = argmax θ∈Θ Ln(θ).

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