# Chapter 6: Point Estimation. Fall Probability & Statistics

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1 STAT355 Chapter 6: Point Estimation Fall 2011 Chapter Fall : Point1 Estimat / 18

2 Chap 6 - Point Estimation Some general Concepts of Point Estimation Point Estimate Unbiasedness Principle of Minimum Variance Unbiased Estimation Methods of Point Estimation Maximum Likelihood Estimation The Method of Moments Chapter Fall : Point2 Estimat / 18

3 Definitions Definition A point estimate of a parameter θ is a single number that can be regarded as a sensible value for θ. A point estimate is obtained by selecting a suitable statistic and computing its value from the given sample data. The selected statistic is called the point estimator of θ. Chapter Fall : Point3 Estimat / 18

4 Examples Suppose, for example, that the parameter of interest is µ, the true average lifetime of batteries of a certain type. A random sample of n = 3 batteries might yield observed lifetimes (hours) x 1 = 5.0, x 2 = 6.4, x 3 = 5.9. The computed value of the sample mean lifetime is x = It is reasonable to regard 5.77 as a very plausible value of µ our best guess for the value of µ based on the available sample information. Chapter Fall : Point4 Estimat / 18

5 Notations Suppose we want to estimate a parameter of a single population (e.g., µ or σ, or λ) based on a random sample of size n. When discussing general concepts and methods of inference, it is convenient to have a generic symbol for the parameter of interest. We will use the Greek letter θ for this purpose. The objective of point estimation is to select a single number, based on sample data, that represents a sensible value for θ. Chapter Fall : Point5 Estimat / 18

6 Some General Concepts of Point Estimation We know that before data is available, the sample observations must be considered random variables X 1, X 2,..., X n. It follows that any function of the X i s - that is, any statistic - such as the sample mean X or sample standard deviation S is also a random variable. Example: In the battery example just given, the estimator used to obtain the point estimate of µ was X, and the point estimate of µ was If the three observed lifetimes had instead been x 1 = 5.6, x 2 = 4.5, and x 3 = 6.1, use of the estimator X would have resulted in the estimate x = ( )/3 = The symbol ˆθ ( theta hat ) is customarily used to denote both the estimator of µ and the point estimate resulting from a given sample. Chapter Fall : Point6 Estimat / 18

7 Some General Concepts of Point Estimation Thus ˆθ = X is read as the point estimator of θ is the sample mean X. The statement the point estimate of θ is 5.77 can be written concisely as ˆθ = In the best of all possible worlds, we could find an estimator ˆθ for which ˆθ = θ always. However, ˆθ is a function of the sample X i s, so it is a random variable. For some samples, ˆθ will yield a value larger than θ, whereas for other samples ˆθ will underestimate θ. If we write ˆθ = θ + error of estimation then an accurate estimator would be one resulting in small estimation errors, so that estimated values will be near the true value. Chapter Fall : Point7 Estimat / 18

8 Some General Concepts of Point Estimation A sensible way to quantify the idea of ˆθ being close to θ is to consider the squared error (ˆθ θ) 2. For some samples, ˆθ will be quite close to θ and the resulting squared error will be near 0. Other samples may give values of ˆθ far from θ, corresponding to very large squared errors. A measure of accuracy is the expected or mean square error (MSE) MSE = E[(ˆθ θ) 2 ] If a first estimator has smaller MSE than does a second, it is natural to say that the first estimator is the better one. Chapter Fall : Point8 Estimat / 18

9 Some General Concepts of Point Estimation However, MSE will generally depend on the value of θ. What often happens is that one estimator will have a smaller MSE for some values of θ and a larger MSE for other values. Finding an estimator with the smallest MSE is typically not possible. One way out of this dilemma is to restrict attention just to estimators that have some specified desirable property and then find the best estimator in this restricted group. A popular property of this sort in the statistical community is unbiasedness. Chapter Fall : Point9 Estimat / 18

10 Unbiasedness Definition A point estimator ˆθ is said to be an unbiased estimator of θ if E(ˆθ) = θ for every possible value of θ. If ˆθ is not unbiased, the difference E(ˆθ θ) is called the bias of ˆθ. That is, ˆθ is unbiased if its probability (i.e., sampling) distribution is always centered at the true value of the parameter. Chapter Fall : Point 10 Estimat / 18

11 Unbiasedness - Example The sample proportion X /n can be used as an estimator of p, where X, the number of sample successes, has a binomial distribution with parameters n and p.thus E(ˆp) = E( X n ) = 1 n E(X ) = 1 n (np) = p Proposition When X is a binomial rv with parameters n and p, the sample proportion ˆp = X /n is an unbiased estimator of p. No matter what the true value of p is, the distribution of the estimator will be centered at the true value. Chapter Fall : Point 11 Estimat / 18

12 Principle of Minimum Variance Unbiased Estimation Among all estimators of θ that are unbiased, choose the one that has minimum variance. The resulting is called the minimum v ariance unbiased estimator (MVUE) of θ. The most important result of this type for our purposes concerns estimating the mean µ of normal distribution. Theorem Let X 1,..., X n be a random sample from a normal distribution with parameters µ and σ 2. Then the estimator ˆµ = X is the MVUE for µ. In some situations, it is possible to obtain an estimator with small bias that would be preferred to the best unbiased estimator. Chapter Fall : Point 12 Estimat / 18

13 Reporting a Point Estimate: The Standard Error Besides reporting the value of a point estimate, some indication of its precision should be given. The usual measure of precision is the standard error of the estimator used. Definition The standard error of an estimator ˆθ is its standard deviation σˆθ = V (ˆθ). Chapter Fall : Point 13 Estimat / 18

14 Maximum Likelihood Estimation Definition Let X 1,..., X n have a joint pmf or pdf f (x 1,..., x n ; θ 1,..., θ m ) (1) where the parameters θ 1,..., θ m have unknown values. When x 1,..., x n are observed samples values and (1)is regarded as a function of θ 1,..., θ m, it is called the likelihood function. Definition L(θ 1,..., θ m ) = f (x 1,..., x n ; θ 1,..., θ m ) The maximum likelihood estimates (mle s) ˆθ 1,...ˆθ m are those values of the θ i s that maximize the likelihood function, so that L(ˆθ 1,..., ˆθ m ) L(θ 1,..., θ m ) Chapter Fall : Point 14 Estimat / 18

15 Maximum Likelihood Estimation - Example Let X 1,...X n be a random sample from the normal distribution. n f (x 1,..., x n ; µ, σ 2 1 ) = 2πσ 2 e (x i µ) 2 /(2σ 2 ) i=1 1 = ( 2πσ 2 )n/2 e n i=1 (x i µ) 2 /(2σ 2). (3) The natural logarithm of the likelihood function is ln(f (x 1,..., x n ; µ, σ 2 )) = n 2 ln(2πσ2 ) 1 n 2σ 2 (x i µ) 2 To find the maximizing values of µ and σ 2, we must take the partial derivatives of ln(f ) with respect to µ and σ 2, equate them to zero, and solve the resulting two equations. Omitting the details, the resulting mles are i=1 (2) n ˆµ = X ; ˆσ 2 i=1 = (X i X ) 2 n Chapter Fall : Point 15 Estimat / 18

16 The Method of Moments The basic idea of this method is to equate certain sample characteristics, such as the mean, to the corresponding population expected values. Then solving these equations for unknown parameter values yields the estimators. Definition Let X 1,..., X n be a random sample from a pmf or pdf f (x). For k = 1, 2, 3,..., the kth population moment, or kth moment of the distribution f (x), is E(X k ). The kth sample moment is (1/n) n i=1 X k i. Thus the first population moment is E(X ) = µ, and the first sample moment is X i /n = X. The second population and sample moments are E(X 2 ) and X 2 i /n, respectively. The population moments will be functions of any unknown parameters θ 1, θ 2,... Chapter Fall : Point 16 Estimat / 18

17 The method of Moments Definition Let X 1, X 2,..., X n be a random sample from a distribution with pmf or pdf f (x; θ 1,..., θ m ), where θ 1,..., θ m are parameters whose values are unknown. Then the moment estimators ˆθ 1,..., ˆθ m are obtained by equating the first m sample moments to the corresponding first m population moments and solving for θ 1,..., θ m. If, for example, m = 2, E(X ) and E(X 2 ) will be functions of θ 1 and θ 2. Setting E(X ) = (1/n) X i (= X ) and E(X 2 ) = (1/n) Xi 2 gives two equations in θ 1 and θ 2. The solution then defines the estimators. Chapter Fall : Point 17 Estimat / 18

18 The method of Moments - Example Let X 1, X 2,..., X n represent a random sample of service times of n customers at a certain facility, where the underlying distribution is assumed exponential with parameter λ. Since there is only one parameter to be estimated, the estimator is obtained by equating E(X ) to λ. Since E(X ) = 1/λ for an exponential distribution, this gives λ = 1/ X. The moment estimator of λ is then ˆλ = 1/ X. Chapter Fall : Point 18 Estimat / 18

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