This is a sample problem from ISI MStat PSB 2013 Problem 4. It is based on the simple linear regression model, finding the estimates, and MSEs. But think over the "Food for Thought" any kind of discussion will be appreciated. Give it a try!
Consider n independent observation \( { (x_i,y_i) :1 \le i \le n} \) from the model
\( Y= \alpha + \beta x + \epsilon \) ,
where \(\epsilon\) is normal with mean 0 and variance \( \sigma^2\) . Let \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \) be the maximum likelihood estimators of \( \alpha , \beta \) and \( \sigma^2\) , respectively. Let \( v_{11}, v_{22} \) and \(v_{12}\) be the estimated values of \( Var(\hat{\alpha}), Var(\hat{\beta} \) and \( Cov ( \hat{\alpha}, \hat{\beta}) \), respectively.
(a) What is the estimated mean of Y, when when \( x=x_o\) ? Estimate the mean squared error of this estimator .
(b) What is the predicted value of Y, when when \( x=x_o\) ? Estimate the mean squared error of this predictor .
Linear Regression
Method of Least Squares
Maximum likelihood Estimators.
Mean Squared Error.
Here for the given model,
we have , the random errors, \( \epsilon \sim n(0, \sigma^2) \), and the maximum likelihood estimators (MLE), of the model parameters are given by \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \). The interesting thing about this model is, since the random errors \(\epsilon\) are Gaussian Random variables, the Ordinary Least Square Estimates of the model parameters \( \alpha, \beta \) and \( \sigma^2\), are identical to their Maximum Likelihood Estimators, ( which are already given!). How ?? Verify it yourself and once and remember it henceforth.
So, here \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \) there also the OLS estimates of the model parameters respectively.
And By Gauss-Markov Theorem, the OLS estimates of the model parameters are the BLUE (Best Linear Unbiased Estimator), for the model parameters. So, here \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \) are also the unbiased estimators of \( \alpha, \beta \) and \(\sigma^2\) respectively.
(a) Now we need to find the estimated mean Y given \(x=x_o\) ,
\( \hat{ E( Y| x=x_o)}= \hat{\alpha} + \hat{\beta} x_o \) is the estimated mean of Y given \( x=x_o\).
Now since, the given MLEs ( OLSEs) are also unbiased for their respective parameters,
\( MSE( \hat{ E( Y| x=x_o)})=MSE(\hat{\alpha} + \hat{\beta} x_o)=E(\hat{\alpha} + \hat{\beta} x_o-(\alpha + \beta x_o))^2 \)
=\( E(\hat{\alpha} + \hat{\beta} x_o-E(\hat{\alpha} + \hat{\beta} x_o))^2 \)
=\( Var( \hat{\alpha} + \hat{\beta} x_o) \)
= \( Var(\hat{\alpha}+2x_o Cov(\hat{\alpha}, \hat{\beta})+ {x_o}^2Var(\hat{\beta}) \)
So, \( .MSE( \hat{ E( Y| x=x_o)})= v_{11} +2x_o v_{12} + {x_o}^2 {v_{22}} \).
(b) Similarly, when \(x=x_o \) , the predicted value of Y would be,
\( \hat{Y} = \hat{\alpha} + \hat{\beta} x_o +\epsilon \) is the predicted value of Y when \(x=x_o\) is given.
Using similar arguments, as in (a) Properties of independence between the model parameters , verify that,
\(MSE(\hat{Y})= v_{11}+ 2x_o v_{12} + {x_o}^2{ v_{22}}+{\hat{\sigma}^2} \). Hence we are done !
Now, can you explain Why, the Maximum Likelihood Estimators and Ordinary Least Square Estimates are identical, when the model assumes Gaussian errors ??
Wait!! Not done yet. The main course is served below !!
In a game of dart, a thrower throws a dart randomly and uniformly in a unit circle. Let \(\theta\) be the angle between the line segment joining the dart and the center and the horizontal axis, now consider Z be a random variable. When the thrower is lefty , Z=-1 and when the thrower is right-handed , Z=1 . Assume that getting a Left-handed and Right-handed thrower is equally likely ( is it really equally likely, in real scenario ?? ). Can you construct a regression model, for regressing \(\theta\) on Z.
Think over it, if you want to discuss, we can do that too !!
This is a sample problem from ISI MStat PSB 2013 Problem 4. It is based on the simple linear regression model, finding the estimates, and MSEs. But think over the "Food for Thought" any kind of discussion will be appreciated. Give it a try!
Consider n independent observation \( { (x_i,y_i) :1 \le i \le n} \) from the model
\( Y= \alpha + \beta x + \epsilon \) ,
where \(\epsilon\) is normal with mean 0 and variance \( \sigma^2\) . Let \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \) be the maximum likelihood estimators of \( \alpha , \beta \) and \( \sigma^2\) , respectively. Let \( v_{11}, v_{22} \) and \(v_{12}\) be the estimated values of \( Var(\hat{\alpha}), Var(\hat{\beta} \) and \( Cov ( \hat{\alpha}, \hat{\beta}) \), respectively.
(a) What is the estimated mean of Y, when when \( x=x_o\) ? Estimate the mean squared error of this estimator .
(b) What is the predicted value of Y, when when \( x=x_o\) ? Estimate the mean squared error of this predictor .
Linear Regression
Method of Least Squares
Maximum likelihood Estimators.
Mean Squared Error.
Here for the given model,
we have , the random errors, \( \epsilon \sim n(0, \sigma^2) \), and the maximum likelihood estimators (MLE), of the model parameters are given by \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \). The interesting thing about this model is, since the random errors \(\epsilon\) are Gaussian Random variables, the Ordinary Least Square Estimates of the model parameters \( \alpha, \beta \) and \( \sigma^2\), are identical to their Maximum Likelihood Estimators, ( which are already given!). How ?? Verify it yourself and once and remember it henceforth.
So, here \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \) there also the OLS estimates of the model parameters respectively.
And By Gauss-Markov Theorem, the OLS estimates of the model parameters are the BLUE (Best Linear Unbiased Estimator), for the model parameters. So, here \( \hat{\alpha}, \hat{\beta} \) and \( \hat{\sigma}^2 \) are also the unbiased estimators of \( \alpha, \beta \) and \(\sigma^2\) respectively.
(a) Now we need to find the estimated mean Y given \(x=x_o\) ,
\( \hat{ E( Y| x=x_o)}= \hat{\alpha} + \hat{\beta} x_o \) is the estimated mean of Y given \( x=x_o\).
Now since, the given MLEs ( OLSEs) are also unbiased for their respective parameters,
\( MSE( \hat{ E( Y| x=x_o)})=MSE(\hat{\alpha} + \hat{\beta} x_o)=E(\hat{\alpha} + \hat{\beta} x_o-(\alpha + \beta x_o))^2 \)
=\( E(\hat{\alpha} + \hat{\beta} x_o-E(\hat{\alpha} + \hat{\beta} x_o))^2 \)
=\( Var( \hat{\alpha} + \hat{\beta} x_o) \)
= \( Var(\hat{\alpha}+2x_o Cov(\hat{\alpha}, \hat{\beta})+ {x_o}^2Var(\hat{\beta}) \)
So, \( .MSE( \hat{ E( Y| x=x_o)})= v_{11} +2x_o v_{12} + {x_o}^2 {v_{22}} \).
(b) Similarly, when \(x=x_o \) , the predicted value of Y would be,
\( \hat{Y} = \hat{\alpha} + \hat{\beta} x_o +\epsilon \) is the predicted value of Y when \(x=x_o\) is given.
Using similar arguments, as in (a) Properties of independence between the model parameters , verify that,
\(MSE(\hat{Y})= v_{11}+ 2x_o v_{12} + {x_o}^2{ v_{22}}+{\hat{\sigma}^2} \). Hence we are done !
Now, can you explain Why, the Maximum Likelihood Estimators and Ordinary Least Square Estimates are identical, when the model assumes Gaussian errors ??
Wait!! Not done yet. The main course is served below !!
In a game of dart, a thrower throws a dart randomly and uniformly in a unit circle. Let \(\theta\) be the angle between the line segment joining the dart and the center and the horizontal axis, now consider Z be a random variable. When the thrower is lefty , Z=-1 and when the thrower is right-handed , Z=1 . Assume that getting a Left-handed and Right-handed thrower is equally likely ( is it really equally likely, in real scenario ?? ). Can you construct a regression model, for regressing \(\theta\) on Z.
Think over it, if you want to discuss, we can do that too !!
