機率極限 &機率收斂

Probability Limit and Convergence in Probability

Convergence Concepts

• This section treats the somewhat fanciful idea of allowing the sample size to approach infinity

• and investigates the behavior of certain sample quantities as this happens. We are mainly

• concerned with three types of convergence, and we treat them in varying amounts of detail.

Sequences of Random Variables(x1,x2,…,x3)

• Interested in behavior of functions of random variables such as means, variances, proportions

• For large samples, exact distributions can be difficult/impossible to obtain

• Limit Theorems can be used to obtain properties of estimators as the sample sizes tend to infinity– Convergence in Probability – Limit of an estimator– Convergence in Distribution – Limit of a CDF– Central Limit Theorem – Large Sample Distribution of

the Sample Mean of a Random Sample

理論骰子平均出現點數是

1*1/6+2*1/6+…+6*1/6=21/6

骰子模擬 1000次後的平圴出現點數是

5+2+ …+3+….+6/1000-21/6

dx

dx

=

x

lim n

x

Law of Large Numbers

Convergence in Probability

Convergence in Probability

• The sequence of random variables, X1,…,Xn, is said to converge in probability to the constant c, if for every >0,

• Weak Law of Large Numbers (WLLN): Let X1,…,Xn be iid random variables with E(Xi)= and V(Xi)=2 < . Then the sample mean converges in probability to :

1)|(|lim

cXP nn

n

XX

XPXP

n

i in

nn

nn

1 where

1limor 0lim

Weak Law of Large Numbers WLLN

Proof of WLLN

Prob

2

2

2

2

2

2

2

22

22

2

22

2

2

00lim||lim

1||

1 :Let

1||

1)|(|

11)|(|

)1(1

1)( :Inequality sChebyshev'

n

nXnn

XXn

XXn

XXXX

XXXX

XnXn

X

nXP

nkn

kkXP

nk

nk

nk

n

k

kn

kkXP

kkXP

kkXP

kk

kXkP

nnXVXE

Other Case/Rules

• Binomial Sample Proportions

• Useful Generalizations:

pp

n

pppVppE

n

X

n

Xp

pnpXVnpXEXX

ppXVpXEi

iXpnX

n

i i

n

ii

iii

Prob^

^^1

^

1

)1(,Let

)1()(,)(

)1()()(Failure a is Trial if 0

Success a is Trial if 1),(Binomial~

)1)0( provided()4

)0 provided(//)3

)2

1)

:Then and :Suppose

Prob

Prob

Prob

Prob

ProbProb

nXn

YYXnn

YXnn

YXnn

YnXn

XPX

YX

YX

YX

YX

Convergence in Distribution

Convergence in Distribution

• Let Yn be a random variable with CDF Fn(y).

• Let Y be a random variable with CDF F(y).

• If the limit as n of Fn(y) equals F(y) for every point y where F(y) is continuous, then we say that Yn converges in distribution to Y

• F(y) is called the limiting distribution function of Yn

• If Mn(t)=E(etYn) converges to M(t)=E(etY), then Yn converges in distribution to Y

Limiting Distribution

Example – Binomial Poisson

• Xn~Binomial(n,p) Let =np p=/n

• Mn(t) = (pet + (1-p))n = (1+p(et-1))n = (1+(et-1)/n)n

• Aside: limn (1+a/n)n = ea

limn Mn(t) = limn (1+(et-1)/n)n = exp((et-1))

• exp((et-1)) ≡ MGF of Poisson()

Xn converges in distribution to Poisson(=np)

Example – Scaled Poisson N(0,1)

))1,0((

!3

/

!2explim)(lim

: aslimit takingNow

!3

/

!2exp

!3

/

!2exp

!3

/

!2

//11exp)(

!3

/

!2

//111

! :Aside

1exp)(

)()(

,1

)(

)(

)(),()()(~

2/2/132

2/1322/132

2/332

2/332/

0

/)1(

)1(

2

/

NMGFett

tM

tttttt

tttttM

ttte

i

xe

eteetM

atMetM

babaXX

XV

XEXY

etMXVXEPoissonX

tY

Y

t

i

ix

tetY

Xbt

baX

eX

t

t

Poisson/Normal CDF Y=(X-L)/sqrt(L) L=25

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-6 -4 -2 0 2 4 6 8

y

F(y

) Poisson CDF

Z CDF

Central Limit Theorem

Central Limit Theorem

• Let X1,X2,…,Xn be a sequence of independently and identically distributed random variables with finite mean , and finite variance 2. Then:

• Thus the limiting distribution of the sample mean is a normal distribution, regardless of the distribution of the individual measurements

n

XXN

Xnn

i i 1

Dist

where)1,0(

Proof of Central Limit Theorem (I)

• Additional Assumptions for this Proof:• The moment-generating function of X, MX(t), exists in

a neighborhood of 0 (for all |t|<h, h>0).• The third derivative of the MGF is bounded in a

neighborhood of 0 (M(3)(t) ≤ B< for all |t|<h, h>0).

• Elements of Proof• Work with Yi=(Xi-)/

• Use Taylor’s Theorem (Lagrange Form)

• Calculus Result: limn[1+(an/n)]n = ea if limnan=a

Proof of CLT (II)

target""our is This )()()(

1

11

)(

1)(0)( nt)(independe :Define

11

11

1

/)/(/

tMtMtM

Ynn

YnYn

Xn

XX

nY

tMeeEeeEtM

YVYEX

Y

nY

nXn

n

i i

n

i i

n

i

i

XttXt

Xt

Y

iii

i

ni i

i

i

i

i

Proof of CLT (III)

xataxk

tfxr

axk

tfax

k

afaxafafxf

n

tM

n

tM

n

tM

eeEeEtM

xkx

k

k

kxk

kk

n

YYY

YntYntYnt

n

n

ni

and between strictly with )()!1(

)()( :where

)()!1(

)()(

!

)())((')()(

:form) (Lagrange Theorem sTaylor' :Aside

)(

1)(

1)1()(

)/()/()/(

1

1

Proof of CLT (IV)

2/3

3232

)3()3(

22)2()2(

0

1)1()(

6210

!30

!2

1001

)assumption (Previous )()(

101)()()0()(

0)()0(')('

1)1()0()(

2,00)()(

:nApplicatioCurrent

)max()min(

)()!1(

)()(

!

)())((')()(

n

tB

n

t

n

tB

n

t

n

t

n

tM

BBtMtf

YEYVYEMaf

YEMaf

EeEMaf

kn

tta

n

txMf

a,x,a,xt

axk

tfax

k

afaxafafxf

nnY

nxYx

Y

Y

YY

xY

x

kxk

kk

Proof of CLT (V)

)1,0(

))1,0(()(lim

)(262

lim62

limlim

62 ere wh1lim

62

11lim

621limlim)(lim

Dist

2/

2

2/1

32

2/1

32

2/1

32

2/1

32

2/3

32

2

NXn

NMGFeetM

Bat

n

Btt

n

tBta

n

tBta

n

a

n

tBt

n

n

tB

n

t

n

tMtM

tan

n

n

n

nn

n

nn

n

n

n

n

n

n

n

n

n

n

Yn

nn

Asymptotic Distribution

Obtaining an asymptotic distribution from a limiting distribution

Obtain the limiting distribution via a stabilizing transformation

Assume the limiting distribution applies reasonably well in

finite samples Invert the stabilizing transformation to obtain the asymptotic

distribution

Asymptotic normality of a distribution.

d

a 2

a 2

a 2

2

n(x ) / N[0,1]

Assume holds in finite samples. Then,

n(x ) N[0, ]

(x ) N[0, / n]

x N[ , / n]

Asymptotic distribution.

/ n the asymptotic variance.

趨近效率Asymptotic Efficiency

Asymptotic Efficiency• Comparison of asymptotic variances• How to compare consistent estimators? If both

converge to constants, both variances go to zero. – Example: Random sampling from the normal

distribution,

– Sample mean is asymptotically normal[μ,σ2/n]

– Median is asymptotically normal [μ,(π/2)σ2/n]

– Mean is asymptotically more efficient

Properties of MLEs: Asymptotic Normality

10 0 0

10 0

One can derive that

1n ( ) [0,{ [ ( )]} ]

which gives the asymptotic distribution of the MLE:

[ ,{ ( )} ]

d

a

N E Hn

N I

Properties of MLEs: Asymptotic Efficiency

iAssuming that the density of y satisfies the regularity conditions

R1-R3, the asymptotic variance of a consistent and asymptotically

normally distributed estimator o

Theorem 17.4 Cramèr - Rao Lower Bound

0

11

21 0 0 0

0 0 00 0 0 0

f the parameter vector will always

be at least as large as

ln ( ) ln ( ) ln ( )[ ( )]

'

For consistent estimators, the MLE ac

L L LE E

I

hieves the CRLB and is therefore

efficient.

Convergence in Mean Square

Convergence in mean square

Convergence in Probability

Almost Sure

Almost Sure

Not Almost Sure