Suppose that W1 is a random variable with mean μ and variance σ21 and W2 is a random variable with mean μ and variance σ2. From
Example 5.4.3, we know that cW1 + (1 − c)W2 is an unbiased estimator of μ for any constant c > 0. If W1 and W2 are independent, for what value of c is the estimator cW1 + (1 − c)W2 most efficient?
1 answer:
Answer:
Step-by-step explanation:
The concept of variance in random variable is applied in solving for the value of c for the estimator cW1 + (1 − c)W2 to be most efficient. Appropriate differentiation of the estimator with respect to c will give the value of c when the result is at minimum.
The detailed analysis and step by step approach is as shown in the attachment.
You might be interested in
Answer:
Original Price = $ 60.00
Step-by-step explanation:
This attachment has the formula of how I calculated it.
Answer:
y = 3x - 5
Step-by-step explanation:
We know that the equation 'y = 3x + ?' intersects the point (1, -2). This means that when x = 1, y = -2 in out equation above. To solve this just plug in the x and y values to get '?'.
Now that we know '?' is -5, we write it back into slope intercept form, so our final answer is y = 3x - 5