The document discusses likelihood functions and inference. It begins by defining the likelihood function as the function that gives the probability of observing a sample given a parameter value. The likelihood varies with the parameter, while the density function varies with the data. Maximum likelihood estimation chooses parameters that maximize the likelihood function. The score function is the gradient of the log-likelihood and has an expected value of zero at the true parameter value. The Fisher information matrix measures the curvature of the likelihood surface and provides information about the precision of parameter estimates. It relates to the concentration of likelihood functions around the true parameter value as sample size increases.

1. Chapter 3 :
Likelihood function and inference
[0]
4 Likelihood function and inference
The likelihood
Information and curvature
Suciency and ancilarity
Maximum likelihood estimation
Non-regular models
EM algorithm

2. The likelihood
Given an usually parametric family of distributions
F 2 fF, 2 g
with densities f [wrt a

3. xed measure ], the density of the iid
sample x1, . . . , xn is
Yn
i=1
f(xi)
Note In the special case is a counting measure,
Yn
i=1
f(xi)
is the probability of observing the sample x1, . . . , xn among all
possible realisations of X1, . . . ,Xn

4. The likelihood
De

5. nition (likelihood function)
The likelihood function associated with a sample x1, . . . , xn is the
function
L : ! R+
!
Yn
i=1
f(xi)
same formula as density but dierent space of variation

6. Example: density function versus likelihood function
Take the case of a Poisson density
[against the counting measure]
f(x; ) =
x
x!
e- IN(x)
which varies in N as a function of x
versus
L(; x) =
x
x!
e-
which varies in R+ as a function of = 3

7. Example: density function versus likelihood function
Take the case of a Poisson density
[against the counting measure]
f(x; ) =
x
x!
e- IN(x)
which varies in N as a function of x
versus
L(; x) =
x
x!
e-
which varies in R+ as a function of x = 3

8. Example: density function versus likelihood function
Take the case of a Normal N(0, )
density [against the Lebesgue measure]
f(x; ) =
1
p
2
e-x2=2 IR(x)
which varies in R as a function of x
versus
L(; x) =
1
p
2
e-x2=2
which varies in R+ as a function of
= 2

9. Example: density function versus likelihood function
Take the case of a Normal N(0, )
density [against the Lebesgue measure]
f(x; ) =
1
p
2
e-x2=2 IR(x)
which varies in R as a function of x
versus
L(; x) =
1
p
2
e-x2=2
which varies in R+ as a function of
x = 2

10. Example: density function versus likelihood function
Take the case of a Normal N(0, 1=)
density [against the Lebesgue measure]
f(x; ) =
p
p
2
e-x2=2 IR(x)
which varies in R as a function of x
versus
L(; x) =
p
p
2
e-x2=2 IR(x)
which varies in R+ as a function of
= 1=2

11. Example: density function versus likelihood function
Take the case of a Normal N(0, 1=)
density [against the Lebesgue measure]
f(x; ) =
p
p
2
e-x2=2 IR(x)
which varies in R as a function of x
versus
L(; x) =
p
p
2
e-x2=2 IR(x)
which varies in R+ as a function of
x = 1=2

12. Example: Hardy-Weinberg equilibrium
Population genetics:
Genotypes of biallelic genes AA, Aa, and aa
sample frequencies nAA, nAa and naa
multinomial model M(n; pAA, pAa, paa)
related to population proportion of A alleles, pA:
pAA = p2
A , pAa = 2pA(1 - pA) , paa = (1 - pA)2
likelihood
L(pAjnAA, nAa, naa) / p2nAA
A [2pA(1 - pA)]nAa(1 - pA)2naa
[Boos Stefanski, 2013]

13. mixed distributions and their likelihood
Special case when a random variable X may take speci

14. c values
a1, . . . , ak and a continum of values A
Example: Rainfall at a given spot on a given day may be zero with
positive probability p0 [it did not rain!] or an arbitrary number
between 0 and 100 [capacity of measurement container] or 100
with positive probability p100 [container full]

15. mixed distributions and their likelihood
Special case when a random variable X may take speci

16. c values
a1, . . . , ak and a continum of values A
Example: Tobit model where y N(XT

17. , 2) but
y = y Ify 0g observed

18. mixed distributions and their likelihood
Special case when a random variable X may take speci

19. c values
a1, . . . , ak and a continum of values A
Density of X against composition of two measures, counting and
Lebesgue:
fX(a) =
P(X = a) if a 2 fa1, . . . , akg
f(aj) otherwise
Results in likelihood
L(jx1, . . . , xn) =
Yk
j=1
P(X = ai)nj
Y
xi =2fa1,...,akg
f(xij)
where nj # observations equal to aj

20. Enters Fisher, Ronald Fisher!
Fisher's intuition in the 20's:
the likelihood function contains the
relevant information about the
parameter
the higher the likelihood the more
likely the parameter
the curvature of the likelihood
determines the precision of the
estimation

21. Concentration of likelihood mode around true parameter
Likelihood functions for x1, . . . , xn P(3) as n increases
n = 40, ..., 240

22. Concentration of likelihood mode around true parameter
Likelihood functions for x1, . . . , xn P(3) as n increases
n = 38, ..., 240

23. Concentration of likelihood mode around true parameter
Likelihood functions for x1, . . . , xn N(0, 1) as n increases

24. Concentration of likelihood mode around true parameter
Likelihood functions for x1, . . . , xn N(0, 1) as sample varies

25. Concentration of likelihood mode around true parameter
Likelihood functions for x1, . . . , xn N(0, 1) as sample varies

26. why concentration takes place
Consider
x1, . . . , xn
iid
F
Then
log
Yn
i=1
f(xij) =
Xn
i=1
log f(xij)
and by LLN
1=n
Xn
i=1
log f(xij)
L
!
Z
X
log f(xj) dF(x)
Lemma
Maximising the likelihood is asymptotically equivalent to
minimising the Kullback-Leibler divergence
Z
X
log f(x)=f(xj) dF(x)
c Member of the family closest to true distribution

27. Score function
Score function de

28. ned by
rlog L(jx) =
@=@1L(jx), . . . , @=@pL(jx)
L(jx)
Gradient (slope) of likelihood function at point
lemma
When X F,
E[rlog L(jX)] = 0

29. Score function
Score function de

30. ned by
rlog L(jx) =
@=@1L(jx), . . . , @=@pL(jx)
L(jx)
Gradient (slope) of likelihood function at point
lemma
When X F,
E[rlog L(jX)] = 0

31. Score function
Score function de

32. ned by
rlog L(jx) =
@=@1L(jx), . . . , @=@pL(jx)
L(jx)
Gradient (slope) of likelihood function at point
lemma
When X F,
E[rlog L(jX)] = 0
Reason:
Z
X
rlog L(jx) dF(x) =
Z
X
rL(jx) dx = r
Z
X
dF(x)

33. Score function
Score function de

34. ned by
rlog L(jx) =
@=@1L(jx), . . . , @=@pL(jx)
L(jx)
Gradient (slope) of likelihood function at point
lemma
When X F,
E[rlog L(jX)] = 0
Connected with concentration theorem: gradient null on average
for true value of parameter

35. Score function
Score function de

36. ned by
rlog L(jx) =
@=@1L(jx), . . . , @=@pL(jx)
L(jx)
Gradient (slope) of likelihood function at point
lemma
When X F,
E[rlog L(jX)] = 0
Warning: Not de

37. ned for non-dierentiable likelihoods, e.g. when
support depends on

38. Fisher's information matrix
Another notion attributed to Fisher [more likely due to Edgeworth]
Information: covariance matrix of the score vector
I() = E
h
rlog f(Xj) frlog f(Xj)gT
i
Often called Fisher information
Measures curvature of the likelihood surface, which translates as
information brought by the data
Sometimes denoted IX to stress dependence on distribution of X

39. Fisher's information matrix
Second derivative of the log-likelihood as well
lemma
If L(jx) is twice dierentiable [as a function of ]
I() = -E
rTrlog f(Xj)
Hence
Iij() = -E
@2
@i@j
log f(Xj)

40. Illustrations
Binomial B(n, p) distribution
f(xjp) =
n
x
px(1 - p)n-x
@=@p log f(xjp) = x=p - n-x=1-p
@2=@p2 log f(xjp) = -x=p2 - n-x=(1-p)2
Hence
I(p) = np=p2 + n-np=(1-p)2
= n=p(1-p)

41. Illustrations
Multinomial M(n; p1, . . . , pk) distribution
f(xjp) =
n
x1 xk
px1
1 pxk
k
@=@pi log f(xjp) = xi=pi - xk=pk
@2=@pi@pj log f(xjp) = -xk=p2
k
@2=@p2i
log f(xjp) = -xi=p2i
- xk=p2
k
Hence
I(p) = n
0
1=p1 + 1=pk 1=pk
BBB@
1=pk 1=pk
. . .
1=pk 1=pk-1 + 1=pk
1
CCCA

42. Illustrations
Multinomial M(n; p1, . . . , pk) distribution
f(xjp) =
n
x1 xk
px1
1 pxk
k
@=@pi log f(xjp) = xi=pi - xk=pk
@2=@pi@pj log f(xjp) = -xk=p2
k
@2=@p2i
log f(xjp) = -xi=p2i
- xk=p2
k
and
I(p)-1 = 1=n
0
p1(1 - p1) -p1p2 -p1pk-1
-p1p2 p2(1 - p2) -p2pk-1
BBB@
. . .
. . .
-p1pk-1 -p2pk-1 pk-1(1 - pk-1)
1
CCCA

43. Illustrations
Normal N(, 2) distribution
f(xj) =
1
p
2
1
exp
-(x-)2=22
@=@ log f(xj) = x-=2
@=@ log f(xj) = -1= + (x-)2=3 @2=@2 log f(xj) = -1=2
@2=@@ log f(xj) = -2 x-=3 @2=@2 log f(xj) = 1=2 - 3 (x-)2=4
Hence
I() = 1=2
1 0
0 2

44. Properties
Additive features translating as accumulation of information:
if X and Y are independent, IX() + IY() = I(X,Y)()
IX1,...,Xn() = nIX1()
if X = T(Y) and Y = S(X), IX() = IY()
if X = T(Y), IX() 6 IY()
If = () is a bijective transform, change of parameterisation:
I() =

45. @
@
T
I()

46. @
@
In information geometry, this is seen as a change of
coordinates on a Riemannian manifold, and the intrinsic
properties of curvature are unchanged under dierent
parametrization. In general, the Fisher information
matrix provides a Riemannian metric (more precisely, the
Fisher-Rao metric). [Wikipedia]

47. Properties
Additive features translating as accumulation of information:
if X and Y are independent, IX() + IY() = I(X,Y)()
IX1,...,Xn() = nIX1()
if X = T(Y) and Y = S(X), IX() = IY()
if X = T(Y), IX() 6 IY()
If = () is a bijective transform, change of parameterisation:
I() =

48. @
@
T
I()

49. @
@
In information geometry, this is seen as a change of
coordinates on a Riemannian manifold, and the intrinsic
properties of curvature are unchanged under dierent
parametrization. In general, the Fisher information
matrix provides a Riemannian metric (more precisely, the
Fisher-Rao metric). [Wikipedia]

50. Approximations
Back to the Kullback{Leibler divergence
D(0, ) =
Z
X
f(xj0) log f(xj0)=f(xj) dx
Using a second degree Taylor expansion
log f(xj) = log f(xj0) + ( - 0)Trlog f(xj0)
+
1
2
( - 0)TrrT log f(xj0)( - 0) + o(jj - 0 jj2)
approximation of divergence:
D(0, )
1
2
( - 0)TI(0)( - 0)
[Exercise: show this is exact in the normal case]

51. Approximations
Central limit law of the score vector
p
nrlog L(jX1, . . . ,Xn) N(0, IX1())
1=
Notation I1() stands for IX1() and indicates information
associated with a single observation

52. Suciency
What if a transform of the sample
S(X1, . . . ,Xn)
contains all the information, i.e.
I(X1,...,Xn)() = IS(X1,...,Xn)()
uniformly in ?
In this case S() is called a sucient statistic [because it is
sucient to know the value of S(x1, . . . , xn) to get complete
information]
A statistic is an arbitrary transform of the data X1, . . . ,Xn

53. Suciency (bis)
Alternative de

54. nition:
If (X1, . . . ,Xn) f(x1, . . . , xnj) and if T = S(X1, . . . ,Xn) is such
that the distribution of (X1, . . . ,Xn) conditional on T does not
depend on , then S() is a sucient statistic
Factorisation theorem
S() is a sucient statistic if and only if
f(x1, . . . , xnj) = g(S(x1, . . . , xnj)) h(x1, . . . , xn)
another notion due to Fisher

55. Illustrations
Uniform U(0, ) distribution
L(jx1, . . . , xn) = -n
Yn
i=1
I(0,)(xi) = -nI max
i
xi
Hence
S(X1, . . . ,Xn) = max
i
Xi = X(n)
is sucient

56. Illustrations
Bernoulli B(p) distribution
L(pjx1, . . . , xn) =
Yn
i=1
pxi(1 - p)n-xi = fp=1-pg
P
i xi (1 - p)n
Hence
S(X1, . . . ,Xn) = Xn
is sucient

57. Illustrations
Normal N(, 2) distribution
L(, jx1, . . . , xn) =
Yn
i=1
1
p
2
expf-(xi-)2=22g
=
1
f22gn=2
exp
-
1
22
Xn
i=1
(xi - xn + xn - )2
=
1
f22gn=2
exp
-
1
22
Xn
i=1
(xi - xn)2 -
1
22
Xn
i=1
(xn - )2
Hence
S(X1, . . . ,Xn) =
Xn,
Xn
i=1
(Xi - Xn)2
!
is sucient

58. Suciency and exponential families
Both previous examples belong to exponential families
f(xj) = h(x) exp
T()TS(x) - ()
Generic property of exponential families:
f(x1, . . . , xnj) =
Yn
i=1
h(xi) exp
T()T
Xn
i=1
S(xi) - n()
lemma
For an exponential family with summary statistic S(), the statistic
S(X1, . . . ,Xn) =
Xn
i=1
S(Xi)
is sucient

59. Suciency as a rare feature
Nice property reducing the data to a low dimension transform but...
How frequent is it within the collection of probability distributions?
Very rare as essentially restricted to exponential families
[Pitman-Koopman-Darmois theorem]
with the exception of parameter-dependent families like U(0, )

60. Pitman-Koopman-Darmois characterisation
If X1, . . . ,Xn are iid random variables from a density
f(j) whose support does not depend on and verifying
the property that there exists an integer n0 such that, for
n n0, there is a sucient statistic S(X1, . . . ,Xn) with

61. xed [in n] dimension, then f(j) belongs to an
exponential family
[Factorisation theorem]
Note: Darmois published this result in 1935 [in French] and
Koopman and Pitman in 1936 [in English] but Darmois is generally
omitted from the theorem... Fisher proved it for one-D sucient
statistics in 1934

62. Minimal suciency
Multiplicity of sucient statistics, e.g., S0(x) = (S(x),U(x))
remains sucient when S() is sucient
Search of a most concentrated summary:
Minimal suciency
A sucient statistic S() is minimal sucient if it is a function of
any other sucient statistic
Lemma
For a minimal exponential family representation
f(xj) = h(x) exp
T()TS(x) - ()
S(X1) + . . . + S(Xn) is minimal sucient

63. Ancillarity
Opposite of suciency:
Ancillarity
When X1, . . . ,Xn are iid random variables from a density f(j), a
statistic A() is ancillary if A(X1, . . . ,Xn) has a distribution that
does not depend on
Useless?! Not necessarily, as conditioning upon A(X1, . . . ,Xn)
leads to more precision and eciency:
Use of F(x1, . . . , xnjA(x1, . . . , xn)) instead of F(x1, . . . , xn)
Notion of maximal ancillary statistic

64. Illustrations
1 If X1, . . . ,Xn
iid
U(0, ), A(X1, . . . ,Xn) = (X1, . . . ,Xn)=X(n)
is ancillary
2 If X1, . . . ,Xn
iid
N(, 2),
A(X1, . . . ,Xn) =
(X1P- Xn, . . . ,Xn - Xn n
i=1(Xi - Xn)2
is ancillary
3 If X1, . . . ,Xn
iid
f(xj), rank(X1, . . . ,Xn) is ancillary
x=rnorm(10)
rank(x)
[1] 7 4 1 5 2 6 8 9 10 3
[see, e.g., rank tests]

65. Point estimation, estimators and estimates
When given a parametric family f(j) and a sample supposedly
drawn from this family
(X1, . . . ,XN)
iid
f(xj)
an estimator of is a statistic T(X1, . . . ,XN) or ^n providing a
[reasonable] substitute for the unknown value .
an estimate of is the value of the estimator for a given [realised]
sample, T(x1, . . . , xn)
Example: For a Normal N(, 2 sample X1, . . . ,XN,
T(X1, . . . ,XN) = ^n = 1=n Xn
is an estimator of and ^n = 2.014 is an estimate

66. Maximum likelihood principle
Given the concentration property of the likelihood function,
reasonable choice of estimator as mode:
MLE
A maximum likelihood estimator (MLE) ^n satis

67. es
L(^njX1, . . . ,XN) L(^njX1, . . . ,XN) for all 2
Under regularity of L(jX1, . . . ,XN), MLE also solution of the
likelihood equations
rlog L(njX1, . . . ,XN) = 0
Warning: ^n is not most likely value of but makes observation
(x1, . . . , xN) most likely...

68. Maximum likelihood invariance
Principle independent of parameterisation:
If = h() is a one-to-one transform of , then
^
MLE
n = h(^MLE
n )
[estimator of transform = transform of estimator]
By extension, if = h() is any transform of , then
^
MLE
n = h(^MLE
n )

69. Unicity of maximum likelihood estimate
Depending on regularity of L(jx1, . . . , xN), there may be
1 an a.s. unique MLE ^MLE
n
2
3
1 Case of x1, . . . , xn N(, 1)
2
3

70. Unicity of maximum likelihood estimate
Depending on regularity of L(jx1, . . . , xN), there may be
1
2 several or an in

71. nity of MLE's [or of solutions to likelihood
equations]
3
1
2 Case of x1, . . . , xn N(1 + 2, 1) [[and mixtures of normal]
3

72. Unicity of maximum likelihood estimate
Depending on regularity of L(jx1, . . . , xN), there may be
1
2
3 no MLE at all
1
2
3 Case of x1, . . . , xn N(i, -2)

73. Unicity of maximum likelihood estimate
Consequence of standard dierential calculus results on
`() = L(jx1, . . . , xn):
lemma
If is connected and open, and if `() is twice-dierentiable with
lim
!@
`() +1
and if H() = rrT`() is positive de

74. nite at all solutions of the
likelihood equations, then `() has a unique global maximum
Limited appeal because excluding local maxima

75. Unicity of MLE for exponential families
lemma
If f(j) is a minimal exponential family
f(xj) = h(x) exp
T()TS(x) - ()
with T() one-to-one and twice dierentiable over , if is open,
and if there is at least one solution to the likelihood equations,
then it is the unique MLE
Likelihood equation is equivalent to S(x) = E[S(x)]

76. Unicity of MLE for exponential families
Likelihood equation is equivalent to S(x) = E[S(x)]
lemma
If is connected and open, and if `() is twice-dierentiable with
lim
!@
`() +1
and if H() = rrT`() is positive de

77. nite at all solutions of the
likelihood equations, then `() has a unique global maximum

78. Illustrations
Uniform U(0, ) likelihood
L(jx1, . . . , xn) = -nI max
i
xi
not dierentiable at X(n) but
^MLE
n = X(n)

79. Illustrations
Bernoulli B(p) likelihood
L(pjx1, . . . , xn) = fp=1-pg
P
i xi (1 - p)n
dierentiable over (0, 1) and
^pMLE
n = Xn

80. Illustrations
Normal N(, 2) likelihood
L(, jx1, . . . , xn) / -n exp
-
1
22
Xn
i=1
(xi - xn)2 -
1
22
Xn
i=1
(xn - )2
dierentiable with
n , ^2
(^MLE
MLE
n ) =
Xn,
1
n
Xn
i=1
(Xi - Xn)2
!

81. The fundamental theorem of Statistics
fundamental theorem
Under appropriate conditions, if (X1, . . . ,Xn)
iid
f(xj), if ^n is
solution of rlog f(X1, . . . ,Xnj) = 0, then
p
nf^n - g
L
! Np(0, I()-1)
Equivalent of CLT for estimation purposes

82. Assumptions
identi

83. able
support of f(j) constant in
`() thrice dierentiable
[the killer] there exists g(x) integrable against f(j) in a
neighbourhood of the true parameter such that

87. @3
@i@j@k
f(j)

91. 6 g(x)
the identity
I() = E
h
rlog f(Xj) frlog f(Xj)gT
i
= -E
rTrlog f(Xj)
stands [mostly super
uous]
^n converges in probability to [similarly super
uous]
[Boos Stefanski, 2014, p.286; Lehmann Casella, 1998]

92. Inecient MLEs
Example of MLE of = jjjj2 when x Np(, Ip):
^MLE = jjxjj2
Then E[jjxjj2] = + p diverges away from with p
Note: Consistent and ecient behaviour when considering the
MLE of based on
Z = jjXjj2 2
p()
[Robert, 2001]

93. Inconsistent MLEs
Take X1, . . . ,Xn
iid
f(x) with
f(x) = (1 - )
1
()
f0(x-=()) + f1(x)
for 2 [0, 1],
f1(x) = I[-1,1](x) f0(x) = (1 - jxj)I[1,1](x)
and
() = (1 - ) expf-(1 - )-4 + 1g
Then for any
^MLE
n
a.s.
! 1
[Ferguson, 1982; John Wellner's slides, ca. 2005]

94. Inconsistent MLEs
Consider Xij i = 1, . . . , n, j = 1, 2 with Xij N(i, 2). Then
^MLE
i = Xi1+Xi2=2 ^2
MLE
=
1
4n
Xn
i=1
(Xi1 - Xi2)2
Therefore
^2
MLE a.s.
! 2=2
[Neyman Scott, 1948]

95. Inconsistent MLEs
Consider Xij i = 1, . . . , n, j = 1, 2 with Xij N(i, 2). Then
^MLE
i = Xi1+Xi2=2 ^2
MLE
=
1
4n
Xn
i=1
(Xi1 - Xi2)2
Therefore
^2
MLE a.s.
! 2=2
[Neyman Scott, 1948]
Note: Working solely with Xi1 - Xi2 N(0, 22) produces a
consistent MLE

96. Likelihood optimisation
Practical optimisation of the likelihood function
? = arg max
L(jx) =
Yn
i=1
g(Xij).
assuming X = (X1, . . . ,Xn)
iid
g(xj)
analytical resolution feasible for exponential families
rT()
Xn
i=1
S(xi) = nr()
use of standard numerical techniques like Newton-Raphson
(t+1) = (t) + Iobs(X, (t))-1r`((t))
with `(.) log-likelihood and Iobs observed information matrix

97. EM algorithm
Cases where g is too complex for the above to work
Special case when g is a marginal
g(xj) =
Z
Z
f(x, zj) dz
Z called latent or missing variable

98. Illustrations
censored data
X = min(X, a) X N(, 1)
mixture model
X .3N1(0, 1) + .7N1(1, 1),
desequilibrium model
X = min(X, Y) X f1(xj) Y f2(xj)

99. Completion
EM algorithm based on completing data x with z, such as
(X,Z) f(x, zj)
Z missing data vector and pair (X,Z) complete data vector
Conditional density of Z given x:
k(zj, x) =
f(x, zj)
g(xj)

100. Likelihood decomposition
Likelihood associated with complete data (x, z)
Lc(jx, z) = f(x, zj)
and likelihood for observed data
L(jx)
such that
log L(jx) = E[log Lc(jx,Z)j0, x] - E[log k(Zj, x)j0, x] (1)
for any 0, with integration operated against conditionnal
distribution of Z given observables (and parameters), k(zj0, x)

101. [A tale of] two 's
There are two 's ! : in (1), 0 is a

102. xed (and arbitrary) value
driving integration, while both free (and variable)
Maximising observed likelihood
L(jx)
equivalent to maximise r.h.s. term in (1)
E[log Lc(jx,Z)j0, x] - E[log k(Zj, x)j0, x]

103. Intuition for EM
Instead of maximising wrt r.h.s. term in (1), maximise only
E[log Lc(jx,Z)j0, x]
Maximisation of complete log-likelihood impossible since z
unknown, hence substitute by maximisation of expected complete
log-likelihood, with expectation depending on term 0

104. Expectation{Maximisation
Expectation of complete log-likelihood denoted
Q(j0, x) = E[log Lc(jx,Z)j0, x]
to stress dependence on 0 and sample x
Principle
EM derives sequence of estimators ^(j), j = 1, 2, . . ., through
iteration of Expectation and Maximisation steps:
Q(^(j)j^(j-1), x) = max
Q(j^(j-1), x).

105. EM Algorithm
Iterate (in m)
1 (step E) Compute
Q(j^(m), x) = E[log Lc(jx,Z)j^(m), x] ,
2 (step M) Maximise Q(j^(m), x) in and set
^(m+1) = arg max
Q(j^(m), x).
until a

106. xed point [of Q] is found
[Dempster, Laird, Rubin, 1978]

107. Justi

108. cation
Observed likelihood
L(jx)
increases at every EM step
L(^(m+1)jx) L(^(m)jx)
[Exercice: use Jensen and (1)]

109. Censored data
Normal N(, 1) sample right-censored
L(jx) =
1
(2)m=2
exp
-
1
2
Xm
i=1
(xi - )2
[1 - (a - )]n-m
Associated complete log-likelihood:
log Lc(jx, z) / -
1
2
Xm
i=1
(xi - )2 -
1
2
Xn
i=m+1
(zi - )2 ,
where zi's are censored observations, with density
k(zj, x) =
expf-1
2 (z - )2g
p
2[1 - (a - )]
=
'(z - )
1 - (a - )
, a z.

110. Censored data (2)
At j-th EM iteration
Q(j^(j), x) / -
1
2
Xm
i=1
(xi - )2 -
1
2
E
Xn
i=m+1
(Zi - )2

115. #
^(j), x
/ -
1
2
Xm
i=1
(xi - )2
-
1
2
Xn
i=m+1
Z1
a
(zi - )2k(zj^(j), x) dzi

116. Censored data (3)
Dierenciating in ,
n ^(j+1) = mx + (n -m)E[Zj^(j)] ,
with
E[Zj^(j)] =
Z1
a
zk(zj^(j), x) dz = ^(j) +
'(a - ^(j))
1 - (a - ^(j))
.
Hence, EM sequence provided by
^(j+1) =
m
n
x +
n -m
n
^(j) +
'(a - ^(j))
1 - (a - ^(j))
#
,
which converges to likelihood maximum ^

117. Mixtures
Mixture of two normal distributions with unknown means
.3N1(0, 1) + .7N1(1, 1),
sample X1, . . . ,Xn and parameter = (0, 1)
Missing data: Zi 2 f0, 1g, indicator of component associated with
Xi ,
Xijzi N(zi , 1) Zi B(.7)
Complete likelihood
log Lc(jx, z) / -
1
2
Xn
i=1
zi(xi - 1)2 -
1
2
Xn
i=1
(1 - zi)(xi - 0)2
= -
1
2
n1(^1 - 1)2 -
1
2
(n - n1)(^0 - 0)2
with
n1 =
Xn
i=1
zi , n1^1 =
Xn
i=1
zixi , (n - n1)^0 =
Xn
i=1
(1 - zi)xi

118. Mixtures (2)
At j-th EM iteration
Q(j^(j), x) =
1
2
E
n1(^1 - 1)2 + (n - n1)(^0 - 0)2j^(j), x
Dierenciating in
^(j+1) =
0
BB@
E
n1^1

120. .
^(j), x
E
n1j^(j), x
E
(n - n1)^0

122. .
^(j), x
E
(n - n1)j^(j), x
1
CCA

123. Mixtures (3)
Hence ^(j+1) given by
0
BB@
Pn
i=1 E
Zi

125. ^(j), xi
xi
.Pn
i=1 E
Zij^(j), xi
Pn
i=1 E
(1 - Zi)

127. ^(j), xi
xi
.Pn
i=1 E
(1 - Zi)j^(j), xi
1
CCA
Conclusion
Step (E) in EM replaces missing data Zi with their conditional
expectation, given x (expectation that depend on ^(m)).

128. Mixtures (3)
−1 0 1 2 3
−1 0 1 2 3
m1
m2
EM iterations for several starting values

129. Properties
EM algorithm such that
it converges to local maximum or saddle-point
it depends on the initial condition (0)
it requires several initial values when likelihood multimodal