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### Brief Review

### Probability distributions

### Statistical Inference variables)

### The general statistical model variables)Most data fits this situation

### Estimation Theory and Sufficiency Principle)

### Methods for Finding Estimators and Sufficiency Principle)

### Application and Sufficiency Principle)

### Hypothesis Testing and Sufficiency Principle)

Probability and Statistics

Continuous distributions

Defn (density function)

Let x denote a continuous random variable then f(x) is called the density function of x

1) f(x) ≥ 0

2)

3)

Defn (Joint density function)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables then

f(x) = f(x1 ,x2 ,x3 , ... , xn)

is called the joint density function of x = (x1 ,x2 ,x3 , ... , xn)

if

1) f(x) ≥ 0

2)

3)

Defn (Marginal density function)

The marginal density of x1 = (x1 ,x2 ,x3 , ... , xp) (p < n) is defined by:

f1(x1) = =

where x2 = (xp+1 ,xp+2 ,xp+3 , ... , xn)

The marginal density of x2 = (xp+1 ,xp+2 ,xp+3 , ... , xn) is defined by:

f2(x2) = =

where x1 = (x1 ,x2 ,x3 , ... , xp)

Defn (Conditional density function)

The conditional density of x1 given x2 (defined in previous slide) (p < n) is defined by:

f1|2(x1 |x2) =

conditional density of x2 given x1 is defined by:

f2|1(x2 |x1)=

Marginal densities describe how the subvector xi behaves ignoring xj

Conditional densities describe how the subvector xi behaves when the subvector xj is held fixed

Defn (Independence)

The two sub-vectors (x1 and x2) are called independent if:

f(x) = f(x1, x2) = f1(x1)f2(x2)

= product of marginals

or

the conditional density of xi given xj :

fi|j(xi |xj) = fi(xi) = marginal density of xi

Example (p-variate Normal)

The random vector x (p× 1) is said to have the

p-variate Normal distribution with

mean vector m(p× 1) and

covariance matrix S(p×p)

(written x ~ Np(m,S)) if:

Example (bivariate Normal)

The random vector is said to have the bivariate

Normal distribution with mean vector

and

covariance matrix

Theorem (Transformations)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables with joint density function f(x1 ,x2 ,x3 , ... , xn) = f(x). Let

y1 =f1(x1 ,x2 ,x3 , ... , xn)

y2 =f2(x1 ,x2 ,x3 , ... , xn)

...

yn =fn(x1 ,x2 ,x3 , ... , xn)

define a 1-1 transformation of x into y.

Then the joint density of y is g(y) given by:

g(y) = f(x)|J| where

= the Jacobian of the transformation

Corollary (Linear Transformations)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables with joint density function f(x1 ,x2 ,x3 , ... , xn) = f(x). Let

y1 = a11x1 + a12x2 + a13x3 , ... + a1nxn

y2 = a21x1 + a22x2 + a23x3 , ... + a2nxn

...

yn = an1x1 + an2x2 + an3x3 , ... + annxn

define a 1-1 transformation of x into y.

Then the joint density of y is g(y) given by:

Corollary (Linear Transformations for Normal Random variables)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables having an n-variate Normal distribution with mean vector m and covariance matrix S.

i.e. x ~ Nn(m, S)

Let

y1 = a11x1 + a12x2 + a13x3 , ... + a1nxn

y2 = a21x1 + a22x2 + a23x3 , ... + a2nxn

...

yn = an1x1 + an2x2 + an3x3 , ... + annxn

define a 1-1 transformation of x into y.

Then y = (y1 ,y2 ,y3 , ... , yn) ~ Nn(Am,ASA')

Defn (Expectation) variables)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables with joint density function

f(x) = f(x1 ,x2 ,x3 , ... , xn).

Let U = h(x)= h(x1 ,x2 ,x3 , ... , xn)

Then

Defn (Conditional Expectation) variables)

Let x = (x1 ,x2 ,x3 , ... , xn) = (x1 , x2 ) denote a vector of continuous random variables with joint density function

f(x) = f(x1 ,x2 ,x3 , ... , xn) = f(x1 , x2 ).

Let U = h(x1)= h(x1 ,x2 ,x3 , ... , xp)

Then the conditional expectation of U given x2

Defn (Variance) variables)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables with joint density function

f(x) = f(x1 ,x2 ,x3 , ... , xn).

Let U = h(x)= h(x1 ,x2 ,x3 , ... , xn)

Then

Defn (Conditional Variance) variables)

Let x = (x1 ,x2 ,x3 , ... , xn) = (x1 , x2 ) denote a vector of continuous random variables with joint density function

f(x) = f(x1 ,x2 ,x3 , ... , xn) = f(x1 , x2 ).

Let U = h(x1)= h(x1 ,x2 ,x3 , ... , xp)

Then the conditional variance of U given x2

Defn (Covariance, Correlation) variables)

Let x = (x1 ,x2 ,x3 , ... , xn) denote a vector of continuous random variables with joint density function

f(x) = f(x1 ,x2 ,x3 , ... , xn).

Let U = h(x)= h(x1 ,x2 ,x3 , ... , xn) and

V = g(x)=g(x1 ,x2 ,x3 , ... , xn)

Then the covariance of U and V.

Properties variables)

- Expectation
- Variance
- Covariance
- Correlation

- E variables)[a1x1 + a2x2 + a3x3 + ... + anxn]
= a1E[x1] + a2E[x2] + a3E[x3] + ... + anE[xn]

or E[a'x] = a'E[x]

- E[UV] = E[h( variables)x1)g(x2)]
= E[U]E[V] = E[h(x1)]E[g(x2)]

if x1 and x2 are independent

- Var variables)[a1x1 + a2x2 + a3x3 + ... + anxn]
or Var[a'x] = a′Sa

- Cov variables)[a1x1 + a2x2 + ... + anxn ,
b1x1 + b2x2 + ... + bnxn]

or Cov[a'x, b'x] = a′Sb

Making decisions from data

There are two main areas of Statistical Inference variables)

- Estimation – deciding on the value of a parameter
- Point estimation
- Confidence Interval, Confidence region Estimation

- Hypothesis testing
- Deciding if a statement (hypotheisis) about a parameter is True or False

Defn (The Classical Statistical Model) variables)

The data vector

x = (x1 ,x2 ,x3 , ... , xn)

The model

Let f(x|q) = f(x1 ,x2 , ... , xn|q1 , q2 ,... , qp) denote the joint density of the data vector x = (x1 ,x2 ,x3 , ... , xn) of observations where the unknown parameter vector qW (a subset of p-dimensional space).

An Example variables)

The data vector

x = (x1 ,x2 ,x3 , ... , xn) a sample from the normal distribution with mean m and variance s2

The model

Then f(x|m , s2) = f(x1 ,x2 , ... , xn|m , s2), the joint density of x = (x1 ,x2 ,x3 , ... , xn) takes on the form:

where the unknown parameter vector q = (m , s2) W ={(x,y)|-∞ < x < ∞ , 0 ≤ y < ∞}.

Defn (Sufficient Statistics) variables)

Let x have joint density f(x|q) where the unknown parameter vector qW.

Then S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is called a set of sufficient statistics for the parameter vector q if the conditional distribution of x given S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is not functionally dependent on the parameter vector q.

A set of sufficient statistics contains all of the information concerning the unknown parameter vector

A Simple Example illustrating Sufficiency variables)

Suppose that we observe a Success-Failure experiment n = 3 times. Let q denote the probability of Success. Suppose that the data that is collected is x1, x2, x3 where xi takes on the value 1 is the ith trial is a Success and 0 if the ith trial is a Failure.

The following table gives possible values of ( variables)x1, x2, x3).

The data can be generated in two equivalent ways:

- Generating (x1, x2, x3) directly from f (x1, x2, x3|q) or

- Generating S from g(S|q) then generating(x1, x2, x3) from f (x1, x2, x3|S). Since the second step does involve q, no additional information will be obtained by knowing (x1, x2, x3)once S is determined

The Sufficiency Principle variables)

Any decision regarding the parameter qshould be based on a set of Sufficient statistics S1(x), S2(x), ...,Sk(x) and not otherwise on the value of x.

A useful approach in developing a statistical procedure variables)

- Find sufficient statistics
- Develop estimators , tests of hypotheses etc. using only these statistics

Defn (Minimal Sufficient Statistics) variables)

Let x have joint density f(x|q) where the unknown parameter vector qW.

Then S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is a set of Minimal Sufficient statistics for the parameter vector qif S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is a set of Sufficient statistics and can be calculated from any other set of Sufficient statistics.

Theorem (The Factorization Criterion) variables)

Let x have joint density f(x|q) where the unknown parameter vector qW.

Then S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is a set of Sufficient statistics for the parameter vector qif

f(x|q) = h(x)g(S,q)

= h(x)g(S1(x) ,S2(x) ,S3(x) , ... , Sk(x),q).

This is useful for finding Sufficient statistics

i.e. If you can factor out q-dependence with a set of statistics then these statistics are a set of Sufficient statistics

Defn (Completeness) variables)

Let x have joint density f(x|q) where the unknown parameter vector qW.

Then S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is a set of Complete Sufficient statistics for the parameter vector qif S = (S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) is a set of Sufficient statistics and whenever

E[f(S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) ] = 0

then

P[f(S1(x) ,S2(x) ,S3(x) , ... , Sk(x)) = 0] = 1

Defn (The Exponential Family) variables)

Let x have joint density f(x|q)| where the unknown parameter vector qW. Then f(x|q) is said to be a member of the exponential family of distributions if:

qW,where

- - ∞ < variables)ai < bi < ∞ are not dependent on q.
2) W contains a nondegenerate k-dimensional rectangle.

3) g(q), ai,bi and pi(q) are not dependent on x.

4) h(x), ai ,bi and Si(x) are not dependent on q.

If in addition. variables)

5) The Si(x) are functionally independent for i = 1, 2,..., k.

6) [Si(x)]/ xj exists and is continuous for all i = 1, 2,..., k j = 1, 2,..., n.

7) pi(q) is a continuous function of qfor all i = 1, 2,..., k.

8) R = {[p1(q),p2(q), ...,pK(q)] | qW,} contains nondegenerate k-dimensional rectangle.

Then

the set of statistics S1(x), S2(x), ...,Sk(x) form a Minimal Complete set of Sufficient statistics.

Defn (The Likelihood function) variables)

Let x have joint density f(x|q) where the unkown parameter vector qW. Then for a

given value of the observation vector x ,the Likelihood function, Lx(q), is defined by:

Lx(q) = f(x|q) with qW

The log Likelihood functionlx(q) is defined by:

lx(q) =lnLx(q) = lnf(x|q) with qW

The Likelihood Principle variables)

Any decision regarding the parameter qshould be based on the likelihood function Lx(q) and not otherwise on the value of x.

If two data sets result in the same likelihood function the decision regarding q should be the same.

Some statisticians find it useful to variables)plot the likelihood function Lx(q) given the value of x.

It summarizes the information contained in x regarding the parameter vector q.

An Example variables)

The data vector

x = (x1 ,x2 ,x3 , ... , xn) a sample from the normal distribution with mean m and variance s2

The joint distribution of x

Then f(x|m , s2) = f(x1 ,x2 , ... , xn|m , s2), the joint density of x = (x1 ,x2 ,x3 , ... , xn) takes on the form:

where the unknown parameter vector q = (m , s2) W ={(x,y)|-∞ < x < ∞ , 0 ≤ y < ∞}.

The Likelihood function variables)

Assume data vector is known

x = (x1 ,x2 ,x3 , ... , xn)

The Likelihood function

Then L(m , s)= f(x|m , s) = f(x1 ,x2 , ... , xn|m , s2),

or variables)

hence variables)

Now consider the following data: (n = 10)

Now consider the following data: ( variables)n = 100)

The Sufficiency Principle variables)

Any decision regarding the parameter qshould be based on a set of Sufficient statistics S1(x), S2(x), ...,Sk(x) and not otherwise on the value of x.

If two data sets result in the same values for the set of Sufficient statistics the decision regarding q should be the same.

Theorem (Birnbaum - Equivalency of the Likelihood Principle and Sufficiency Principle)

Lx1(q) Lx2(q)

if and only if

S1(x1) = S1(x2),..., and Sk(x1) = Sk(x2)

The following table gives possible values of ( and Sufficiency Principle)x1, x2, x3).

The Likelihood function

Point Estimation

Defn (Estimator) and Sufficiency Principle)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector qW.

Then an estimatorof the parameter f(q) = f(q1 ,q2 , ... , qk) is any function T(x)=T(x1 ,x2 ,x3 , ... , xn) of the observation vector.

Defn (Mean Square Error) and Sufficiency Principle)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector q W.

Let T(x) be an estimator of the parameter

f(q). Then the Mean Square Error of T(x) is defined to be:

Defn (Uniformly Better) and Sufficiency Principle)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector q W.

Let T(x) and T*(x) be estimators of the parameter f(q). Then T(x) is said to be uniformly better than T*(x) if:

Defn (Unbiased and Sufficiency Principle))

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector q W.

Let T(x) be an estimator of the parameter f(q). Then T(x) is said to be an unbiased estimator of the parameter f(q) if:

Theorem (Cramer Rao Lower bound) and Sufficiency Principle)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector q W. Suppose that:

i) exists for all x and for all .

ii)

iii)

iv)

Let and Sufficiency Principle)M denote the p x p matrix with ijth element.

Then V = M-1 is the lower bound for the covariance matrix of unbiased estimators of q.

That is, var(c' ) = c'var( )c ≥ c'M-1c = c'Vc where is a vector of unbiased estimators of q.

Defn (Uniformly Minimum Variance Unbiased Estimator) and Sufficiency Principle)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector qW. Then T*(x) is said to be theUMVU (Uniformly minimum variance unbiased)estimator off(q) if:

1) E[T*(x)] = f(q) for all qW.

2) Var[T*(x)] ≤ Var[T(x)] for all qW whenever E[T(x)] = f(q).

Theorem (Rao-Blackwell) and Sufficiency Principle) Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector q W.

Let S1(x), S2(x), ...,SK(x) denote a set of sufficient statistics.

Let T(x) be any unbiased estimator of f(q).

Then T*[S1(x), S2(x), ...,Sk(x)] = E[T(x)|S1(x), S2(x), ...,Sk(x)] is an unbiased estimator of f(q) such that:

Var[T*(S1(x), S2(x), ...,Sk(x))] ≤ Var[T(x)]

for all qW.

Theorem (Lehmann-Scheffe') and Sufficiency Principle) Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector q W.

Let S1(x), S2(x), ...,SK(x) denote a set of completesufficient statistics.

Let T*[S1(x), S2(x), ...,Sk(x)] be an unbiased estimator of f(q). Then:

T*(S1(x), S2(x), ...,Sk(x)) )] is the UMVU estimator of f(q).

Defn ( and Sufficiency Principle)Consistency)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector qW. Let Tn(x) be anestimator off(q). Then Tn(x) is called a consistentestimator of f(q) if for any e > 0:

Defn (M. S. E. and Sufficiency Principle)Consistency)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vector qW. Let Tn(x) be anestimator off(q). Then Tn(x) is called a M. S. E. consistentestimator of f(q) if for any e > 0:

The Method of Moments

Maximum Likelihood Estimation

Methods for finding estimators and Sufficiency Principle)

- Method of Moments
- Maximum Likelihood Estimation

Method of Moments and Sufficiency Principle)

Let x1, … , xndenote a sample from the density function

f(x; q1, … , qp) = f(x; q)

The kth moment of the distribution being sampled is defined to be:

The and Sufficiency Principle)kth sample moment is defined to be:

To find the method of moments estimator of q1, … , qpwe set up the equations:

We then solve the equations and Sufficiency Principle)

for q1, … , qp.

The solutions

are called the method of moments estimators

The Method of Maximum Likelihood and Sufficiency Principle)

Suppose that the data x1, … , xnhas joint density function

f(x1, … , xn; q1, … , qp)

where q = (q1, … , qp) are unknown parameters assumed to lie in W(a subset of p-dimensional space).

We want to estimate the parametersq1, … , qp

Definition: and Sufficiency Principle)Maximum Likelihood Estimation

Suppose that the data x1, … , xnhas joint density function

f(x1, … , xn; q1, … , qp)

Then the Likelihood function is defined to be

L(q) = L(q1, … , qp)

= f(x1, … , xn; q1, … , qp)

the Maximum Likelihood estimators of the parameters q1, … , qp are the values that maximize

L(q) = L(q1, … , qp)

the and Sufficiency Principle)Maximum Likelihood estimators of the parameters q1, … , qp are the values

Such that

Note:

is equivalent to maximizing

the log-likelihood function

The General Linear Model

Consider the random variable and Sufficiency Principle)Y with

1. E[Y] = g(U1 ,U2 , ... , Uk)

= b1f1(U1 ,U2 , ... , Uk) + b2f2(U1 ,U2 , ... , Uk) + ... + bpfp(U1 ,U2 , ... , Uk)

=

and

2. var(Y) = s2

- where b1, b2 , ... ,bp are unknown parameters
- and f1 ,f2 , ... , fp are known functions of the nonrandom variables U1 ,U2 , ... , Uk.
- Assume further that Y is normally distributed.

Thus the density of and Sufficiency Principle)Y is:

f(Y|b1, b2 , ... ,bp, s2) = f(Y| b, s2)

i = 1,2, … , p

Now suppose that n independent observations of and Sufficiency Principle)Y,

(y1, y2, ..., yn) are made

corresponding to n sets of values of (U1 ,U2 , ... , Uk) - (u11 ,u12 , ... , u1k),

(u21 ,u22 , ... , u2k),

...

(un1 ,un2 , ... , unk).

Let xij = fj(ui1 ,ui2 , ... , uik) j =1, 2, ..., p; i =1, 2, ..., n.

Then the joint density of y = (y1, y2, ... yn) is:

f(y1, y2, ..., yn|b1, b2 , ... ,bp, s2) = f(y|b, s2)

Thus and Sufficiency Principle)f(y|b,s2) is a member of the exponential family of distributions

and S = (y'y, X'y) is a Minimal Complete set of Sufficient Statistics.

Defn (Test of size and Sufficiency Principle)a)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q)where the unknown parameter vectorq W.

Letw be any subset ofW.

Consider testing the the Null Hypothesis

H0:q w

against the alternative hypothesis

H1:q w.

Let and Sufficiency Principle)A denote the acceptance region for the test. (all values x = (x1 ,x2 ,x3 , ... , xn) of such that the decision to accept H0 is made.)

and let C denote the critical region for the test (all values x = (x1 ,x2 ,x3 , ... , xn) of such that the decision to reject H0 is made.).

Then the test is said to be of size a if

Defn (Power) and Sufficiency Principle)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vectorq W.

Consider testing the the Null Hypothesis

H0:q w

against the alternative hypothesis

H1:qw.

wherew is any subset ofW. Then the Power of the test forqwis defined to be:

Defn (Uniformly Most Powerful (UMP) test of size and Sufficiency Principle)a)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q) where the unknown parameter vectorq W.

Consider testing the the Null Hypothesis

H0:q w

against the alternative hypothesis

H1:q w.

wherew is any subset ofW.

Let C denote the critical region for the test . Then the test is called the UMP test of sizeaif:

Let and Sufficiency Principle)x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q)where the unknown parameter vectorq W.

Consider testing the the Null Hypothesis

H0:q w

against the alternative hypothesis

H1:q w.

wherew is any subset ofW.

Let C denote the critical region for the test . Then the test is called the UMP test of sizeaif:

and for any other critical region C* such that: and Sufficiency Principle)

then

Theorem and Sufficiency Principle)(Neymann-Pearson Lemma)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q)where the unknown parameter vectorq W = (q0, q1).

Consider testing the the Null Hypothesis

H0:q=q0

against the alternative hypothesis

H1:q=q1.

Then the UMP test of sizeahas critical region:

where K is chosen so that

Defn (Likelihood Ratio Test of size and Sufficiency Principle)a)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q)where the unknown parameter vectorq W.

Consider testing the the Null Hypothesis

H0:q w

against the alternative hypothesis

H1:q w.

wherew is any subset ofW

Then the Likelihood Ratio (LR) test of size a has critical region:

where K is chosen so that

Theorem (Asymptotic distribution of Likelihood ratio test criterion)

Let x = (x1 ,x2 ,x3 , ... , xn) denote the vector of observations having joint density f(x|q)where the unknown parameter vectorq W.

Consider testing the the Null Hypothesis

H0:q w

against the alternative hypothesis

H1:q w.

wherew is any subset ofW

Then under proper regularity conditions on U = -2lnl(x)possesses an asymptotic Chi-square distribution with degrees of freedom equal to the difference between the number of independent parameters inWandw.

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