regression estimators

7/28/2019 Regression Estimators

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Simple Linear Regression Maximum LikelihoodEstimation

January 20, 2010

Tiejun (Ty) Tong

Department of Applied Mathematics
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Simple Linear Regression

A simple linear regression model is defined as

Yi = 0 + 1xi + i,

where

Yi is the response values,xi is the predictor values,0 is the intercept,1 is the slope,i are i.i.d. random variables from N(0,

2).

For ease of notation, denote x = 1n

ni=1 xi, Y =

1

n

ni=1 Yi,

Sxx =n

i=1(xi x)

2, and Sxy =n

i=1(xi x)(Yi Y).


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Least Square Estimation

The LS estimates of0 and 1 are defined to be the values of0 and 1 such that the line 0 + 1x minimizes the RSS.

(0, 1) = argminc,d

n

i=1

(Yi (c + dxi))2.

The LS estimators of0 and 1 are

1 = Sxy/Sxx,

0 = Y 1x.

Given 0 and 1, the fitted linear regression model is

Y = 0 + 1x.
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Least Square Estimation

The difference between the observed value Yi and the fittedvalue Yi is called a residual. We denote it as

ei = Yi Yi = Yi (0 + 1xi), i = 1, . . . , n.

An unbiased estimator of2 is given as

2 =RSS

n 2=

1

n 2

ni=1

e2

i .

The coefficient of determination, denoted by r2, is given by

r2 = 1

RSS

SST= 1

ni=1(Yi Yi)

2

ni=1(Yi Y)

2.
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Maximum Likelihood Estimation

The lease squares method can be used to estimate 0 and 1regardless of the distribution form of the error term (eithernormal or non-normal errors).

For Inference problems such as hypothesis testing andconfidence interval construction, we need to assume that thedistribution of the errors are known.

For a simple linear regression model, we assume that

ii.i.d. N(0, 2), i = 1, . . . , n.

Thus for fixed design points xi, the observations Yi areindependently r.v.s with distribution

Yi N(0 + 1xi, 2), i = 1, . . . , n.
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Maximum Likelihood Estimation

Taking the first partial derivatives of the log-likelihood

function on 0, 1 and 2, we haven

i=1

(Yi 0 1xi) = 0,

ni=1

xi(Yi 0 1xi) = 0,

n

i=1(Yi 0 1xi)

2 = n2.

Solving the above equations leads to

1,ML = Sxy/Sxx, 0,ML = Y 1,MLx, and 2

ML =1

n

ni=1

e2

i .

Note that the ML estimators of0

and 1

areidentical

to theLS estimators of0 and 1.
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Properties of 0 and 1

First, 0 and 1 can be represented as linear combinationsof the observations Yi:

1 =1

Sxx

ni=1

(xi x)(Yi Y) =

ni=1

ciYi,

0 = 1n

ni=1

Yi

ni=1

cix Yi =

ni=1

( 1n cix)Yi,

where ci = (xi x)/Sxx.

Second, 0 and 1 are unbiased estimators of0 and 1,

respectively. For example,

E(1) =n

i=1

ci(0 + 1xi) = 0

ni=1

ci + 1

ni=1

cixi = 1,

where

ni=1 ci = 0 and

ni=1 cixi = 1.


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Properties of 0 and 1

The variances of 0 and 1 are

Var(1) =n

i=1c2

i Var(Yi) =2

Sxx,

Var(0) = Var(Y) + x2Var(1) =

2( 1n

+ x2

Sxx),

where

ni=1 c

2

i = 1/Sxx, and the covariance of Y and 1 iszero.

Lastly, it can be shown that 0 and 1 are the Best LinearUnbiased Estimators (BLUE) of0 and 1, where thebest implies the minimum variance. This result is called theGauss-Markov Theorem.


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Distributions of the Estimators

Theorem: Let Z1, . . . , Zn be mutually independent randomvariables with Zi N(i, 2i ). Let a1, . . . , an and b1, . . . , bnbe fixed constants. Then

Z =n

i=1

(aiZi + bi) Nn

i=1

(aii + bi),n

i=1

a2

i2

i .The distributions of 0 and 1 are

0 N(0, 2(

1

n+

x2

Sxx)) , 1 N(1,

2

Sxx) .Furthermore, (0, 1) and

2 (unbiased estimator) areindependent and

(n 2)2

2 2n2.
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