approximations to probability distributions: limit theorems
TRANSCRIPT
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Approximations to Probability Distributions: Limit Theorems
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Sequences of Random Variables
• 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
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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
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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
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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
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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
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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)
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Example – Scaled Poisson N(0,1)
))1,0((
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: aslimit takingNow
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,1
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2
/
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eteetM
atMetM
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etMXVXEPoissonX
tY
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t
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ix
tetY
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baX
eX
t
t
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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
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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(
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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
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Proof of CLT (II)
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1
11
)(
1)(0)( nt)(independe :Define
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1
/)/(/
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Proof of CLT (III)
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and between strictly with )()!1(
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)(
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1)1()(
)/()/()/(
1
1
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Proof of CLT (IV)
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22)2()2(
0
1)1()(
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)assumption (Previous )()(
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:nApplicatioCurrent
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)()(
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)())((')()(
n
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kk
![Page 15: Approximations to Probability Distributions: Limit Theorems](https://reader036.vdocuments.mx/reader036/viewer/2022082409/56649d975503460f94a805bf/html5/thumbnails/15.jpg)
Proof of CLT (V)
)1,0(
))1,0(()(lim
)(262
lim62
limlim
62 ere wh1lim
62
11lim
621limlim)(lim
Dist
2/
2
2/1
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2/1
32
2/1
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2/1
32
2/3
32
2
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