# Invariance Property and Likelihood Equation of MLE - Module 4

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Invariance Property and Likelihood Equation of MLE - Module 4Saurav
De

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 1 / 26

Invariance Property and Likelihood Equation of MLE

MLE and Invariance Property

Let θ be MLE of θ. Then for the parametric function g(θ) : → Γ; MLE is g(θ).

Proof. Let us define γ = {θ : g(θ) = γ} . This means = γ∈Γ

γ .

We are to find γ at which Mx(γ) is maximised.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 2 / 26

Invariance Property and Likelihood Equation of MLE

Now Mx(γ0) = sup θ∈γ0

Lx(θ) ≥ Lx(θ) θ∈γ0

where γ0 = {θ : g(θ) = γ0} . As g(θ) = γ0 so θ ∈ γ0

Again

Invariance Property and Likelihood Equation of MLE

Therefore Mx(γ0) = Lx(θ) = sup

γ∈Γ Mx(γ).

Hence γ0 is the MLE of γ, i.e. g(θ)(= γ0) is the MLE of g(θ)(= γ). Proved

Ex.3 Let X1,X2, . . . ,Xn ∼ Bin (1, p) ; 0 ≤ p ≤ 1

Then Vp(X ) = p(1− p)(= g(p)) and pMLE = X n.

By invariance property MLE of Vp(X ) is g(pMLE ) = pMLE (1− pMLE ) = X n(1− X n).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 4 / 26

Invariance Property and Likelihood Equation of MLE

Application 4. Suppose that n observations are taken on a random variable X with distribution N(µ, 1), but instead of recording all the observations, one notes only whether or not the observation is less than 0. If {X < 0} occurs m(< n) times, find the MLE of µ.

Let X1,X2, . . . ,Xn ∼ N(µ, 1).

Let θ = Pµ[X1 < 0] = Φ(−µ) = 1− Φ(µ).

This means µ = −Φ−1(θ), a continuous function of θ.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 5 / 26

Invariance Property and Likelihood Equation of MLE

Yi = 1 if Xi < 0

= 0 if Xi ≥ 0

n∑ i=1

n . (See the Application

3).

Hence by the invariance property the MLE of µ is −Φ−1(mn ).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 6 / 26

Invariance Property and Likelihood Equation of MLE

Application. A commuter trip consists of first riding a subway to the bus stop and then taking a bus. The bus she should like to catch arrives uniformly over the interval θ1 to θ2. She would like to estimate both θ1 and θ2 so that she would have some idea about the time she should be at the bus stop (θ1) and she should have too late and wait for the next bus (θ2). Over an 8-day period she makes certain to be at the bus stop so early not to miss the bus and records the following arrival time of the bus.

5:15 PM , 5:21 PM , 5:14 PM , 5:23 PM , 5:29 PM , 5:17 PM , 5:15 PM , 5:18 PM

Estimate θ1 and θ2. Also give the MLEs for the mean and the variability of the arrival distribution.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 7 / 26

Invariance Property and Likelihood Equation of MLE Solution: Let T : arrival time of the bus. Then according to the question T ∼ R(θ1, θ2).

Let T1,T2, . . . ,Tn be n independent random arrival times of the bus.

Then the likelihood of θ = (θ1, θ2) is

L(θ) = 1

(θ2 − θ1)n if θ1 ≤ ti ≤ θ2; i = 1, 2, . . . , n

= 0 otherwise

= 0 otherwise

where t(1) = min {t1, t2, . . . , tn} and t(n) = max {t1, t2, . . . , tn} . Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 8 / 26

Invariance Property and Likelihood Equation of MLE

Under θ1 ≤ t(1) < t(n) ≤ θ2 , t(n) − t(1) ≤ θ2 − θ1.

Hence L(θ1, θ2) = 1 (θ2−θ1)n ≤

1 (t(n)−t(1))n = L(t(1), t(n)).

Thus MLE of (θ1, θ2) is (T(1),T(n)), where T(1) and T(n) are the minimum and maximum order statistic respectively.

Now E (T ) = θ1+θ2 2 and V (T ) = (θ2−θ1)2

12 are two continuous functions of (θ1, θ2).

So MLE of Mean : T(1)+T(n)

2 and of variability : (T(n)−T(1))√

12 (from invariance

property of MLE )

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 9 / 26

Invariance Property and Likelihood Equation of MLE Computation using R : (The minute component of the time has been considered as the data)

R Code and Output :

> samp=c(15 ,21 ,14 ,23 ,29 ,17 ,15 ,18)# given sample

> n=length(samp)# size of the sample

> n

> MLE_theta2=max(samp)

> cat("The mean arrival time is :",MLE_Mean ,"minutes after 5pm.\n")

The mean arrival time is : 21.5 minutes after 5pm.

> MLE_Var=(MLE_theta2 -MLE_theta1)/sqrt (12)# MLE of variability

> MLE_Var

Invariance Property and Likelihood Equation of MLE

Likelihood Equations and Related Discussions

lx(θ) = ln Lx(θ) is called log-likelihood function of θ.

Likelihood Equation : ∂lx (θ) ∂θ = 0.

Any MLE is a root of the likelihood equation.

Any root may be local minima or local maxima.

Possible verification for the root θ to be an MLE: ∂2lx (θ) ∂θ2 |θ=θ < 0.

If θ = (θ1, . . . , θs)′, the likelihood equations: ∂lx (θ) ∂θi

= 0 i = 1, . . . , s.

Possible verification for the root θ to be an MLE of θ: The Hessian matrix(( ∂2lx (θ) ∂θi∂θj

|θi=θi ,θj=θj ))

Invariance Property and Likelihood Equation of MLE

Result: Let T be a sufficient statistic for the family of distributions {fθ : θ ∈ } . If a unique MLE of θ exists, it is a (nonconstant) function of T . If a MLE of θ exists but is not unique, one can find a MLE that is a function of T .

Proof. Since T is sufficient, from Neyman-Fisher factorisability we can write

L(θ) = fθ(x) = gθ(T (x))h(x)

for all x, all θ and some h and gθ, where L(θ) is the likelihood function of θ and fθ(x) is the joint pmf or pdf of sample observations x = (x1, . . . , xn).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 12 / 26

Invariance Property and Likelihood Equation of MLE

If L(θ) is maximised by a unique MLE θ, naturally it will also maximise the function gθ(T (x)).

=⇒ θ should be a function of T .

If MLE of θ exists but is not unique, then ∃ some MLE θ that can be expressed as a function of T .

Proved

Invariance Property and Likelihood Equation of MLE

Result: Under regular estimation case (i.e. the situation where all the regularity conditions of Cramer-Rao Inequality hold) if an estimator θ of θ attains the Cramer-Rao Lower Bound CRLB for the variance, the likelihood equation has a unique solution θ that maximises the likelihood function.

Proof. Let L(θ|x) denote the likelihood function of real-valued parameter θ given the sample observations x = (x1, . . . , xn). If fθ(x) denotes the joint

∂

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 14 / 26

Invariance Property and Likelihood Equation of MLE

that is ∂

∂θ log L(θ|x) = 0 has the unique solution

θ = θ.

Differentiating both sides of (∗) again with respect to θ we get

∂2

Hence ∂2

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 15 / 26

Invariance Property and Likelihood Equation of MLE

Now, If T is an unbiased estimator of a real-valued estimable parametric function g(θ) which is differentiable at least once, from CR regularity conditions we directly get∫

T (x))fθ(x)dx = g(θ)

∂

∂θ log fθ(x)dx = g ′(θ)

In particular choosing g(θ) = θ and noting that Eθ [ ∂ ∂θ log L(θ|X)

] = 0 we

Invariance Property and Likelihood Equation of MLE

Finally substituting

∂θ log L(θ|X)

(looking at (∗) as T (X) is just like θ(X) by its definition) we get

[k(θ)]−1Eθ

Thus from (∗∗) the S.O.C. for maximising L(θ) holds. Hence proved.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 17 / 26

Invariance Property and Likelihood Equation of MLE

Result: Suppose ∂2lx (θ) ∂θ2 ≤ 0 ∀ θ ∈ . Then θ satisfying ∂lx (θ)

∂θ = 0 is the global maxima.

Proof. lx(θ) = lx(θ) + (θ − θ)∂lx (θ) ∂θ |θ=θ + (θ−θ)2

2 ∂2lx (θ) ∂θ2 |θ=θ∗ θ∗ ∈ (θ, θ).

Note that the RHS ≤ lx(θ) because, in RHS the 2nd factor of 2nd term vanishes and the 2nd factor of the third term ≤ 0. Hence proved.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 18 / 26

Invariance Property and Likelihood Equation of MLE

Result: Suppose

(i) ∂lx (θ) ∂θ = 0 iff θ = θ.

(ii) ∂2lx (θ) ∂θ2 |θ=θ < 0. And

(iii) θ is an interior point of an interval I ⊂ .

Then θ is the global maxima.

Proof. If possible suppose θ∗ is such that lx(θ∗) > lx(θ). Then there must be a local minima in between the local maxima θ and θ∗. This means for that minima point also ∂lx (θ)

∂θ = 0, which is a contradiction to the supposition (i). Hence proved.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 19 / 26

Invariance Property and Likelihood Equation of MLE

Ex.1 Let X1,X2, . . . ,Xn ∼ N(µ, σ2) independently.

Then lx(θ) = constant -

∂σ2 = 0 imply µ = x , σ2 = 1 n

∑ (xi − x)2 = s2.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 20 / 26

Invariance Property and Likelihood Equation of MLE

∂2lx(θ) ∂µ2 |(µ,σ2)=(x ,s2) = − n

s2 , ∂2lx(θ) ∂∂(σ2)2 |(µ,σ2)=(x ,s2) = − n

s4 .

( −n/s2 0

0 −n/2s4

) is negative definite. Hence (X ,S2) is the global maxima point and is the MLE of (µ, σ2).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 21 / 26

Invariance Property and Likelihood Equation of MLE

Aliter: If ψ(x) = x − 1− lnx then ψ′(x) = 1− 1/x , ψ′′(x) = 1/x2 > 0.

Therefore ψ(x) is minimum at x = 1 and minψ(x) = 1− 1− 0 = 0. Based on this result we can write

lx(µ, σ2)− lx(µ, σ2) =

σ2 − n 2 = n

Invariance Property and Likelihood Equation of MLE

TRY YOURSELF!

M4.1. Let X1,X2, . . . ,Xn ∼ Lognormal(µ, σ2) independently. Then MLE of (µ, σ2) is (Y ,S∗2) where Y = logX and (Y ,S∗2) = (sample mean , sample variance(with divisor n)) on Y .

Hint. If X ∼ Lognormal(µ, σ2) then Y = logX ∼ N(µ, σ2). Now proceed as in Ex. 1.

M4.2. (continuation)If in M4.1. µ = 0, find the MLE of σ2

M4.3. Consider a random sample of size n from Exponential (mean = β). It is given only that k , 0 < k < n, of these n observations are ≤ M, where M is a known positive number. Find the MLE of β.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 23 / 26

Invariance Property and Likelihood Equation of MLE

TUTORIAL DISCUSSION :

M4.2. If X ∼ Lognormal(0, σ2) then Y = logX ∼ N(0, σ2).

Given y = (y1, . . . , yn), the loglikelihood function of σ2 is

l(σ2) = constant − n

2 σ2 − 1

σ2 = 1 n

n∑ i=1

y2 i = σ2 (say) is the only solution of the likelihood equation.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 24 / 26

Invariance Property and Likelihood Equation of MLE

Also ∂2

2σ2 < 0.

Moreover σ2 is an interior point of the parameterspace = (0,∞).

=⇒ σ2 is the point of global maxima of the likelihood function i.e. the unique MLE of σ2.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 25 / 26

Invariance Property and Likelihood Equation of MLE

M4.3. Let X1,X2, . . . ,Xn ∼ Exponential (mean = β).

Define Yi = 1(0) if Xi ≤ M (Xi > M).

Then Y1,Y2, . . . ,Yn ∼ Bin (1, θ) ; 0 < θ < 1,

where θ = Pβ[X1 ≤ M] = 1− exp [ −M

β

n∑ i=1

n . (See the Application

4).

Hence by the invariance property the MLE of β is − M log(1− k

n ) .

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 1 / 26

Invariance Property and Likelihood Equation of MLE

MLE and Invariance Property

Let θ be MLE of θ. Then for the parametric function g(θ) : → Γ; MLE is g(θ).

Proof. Let us define γ = {θ : g(θ) = γ} . This means = γ∈Γ

γ .

We are to find γ at which Mx(γ) is maximised.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 2 / 26

Invariance Property and Likelihood Equation of MLE

Now Mx(γ0) = sup θ∈γ0

Lx(θ) ≥ Lx(θ) θ∈γ0

where γ0 = {θ : g(θ) = γ0} . As g(θ) = γ0 so θ ∈ γ0

Again

Invariance Property and Likelihood Equation of MLE

Therefore Mx(γ0) = Lx(θ) = sup

γ∈Γ Mx(γ).

Hence γ0 is the MLE of γ, i.e. g(θ)(= γ0) is the MLE of g(θ)(= γ). Proved

Ex.3 Let X1,X2, . . . ,Xn ∼ Bin (1, p) ; 0 ≤ p ≤ 1

Then Vp(X ) = p(1− p)(= g(p)) and pMLE = X n.

By invariance property MLE of Vp(X ) is g(pMLE ) = pMLE (1− pMLE ) = X n(1− X n).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 4 / 26

Invariance Property and Likelihood Equation of MLE

Application 4. Suppose that n observations are taken on a random variable X with distribution N(µ, 1), but instead of recording all the observations, one notes only whether or not the observation is less than 0. If {X < 0} occurs m(< n) times, find the MLE of µ.

Let X1,X2, . . . ,Xn ∼ N(µ, 1).

Let θ = Pµ[X1 < 0] = Φ(−µ) = 1− Φ(µ).

This means µ = −Φ−1(θ), a continuous function of θ.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 5 / 26

Invariance Property and Likelihood Equation of MLE

Yi = 1 if Xi < 0

= 0 if Xi ≥ 0

n∑ i=1

n . (See the Application

3).

Hence by the invariance property the MLE of µ is −Φ−1(mn ).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 6 / 26

Invariance Property and Likelihood Equation of MLE

Application. A commuter trip consists of first riding a subway to the bus stop and then taking a bus. The bus she should like to catch arrives uniformly over the interval θ1 to θ2. She would like to estimate both θ1 and θ2 so that she would have some idea about the time she should be at the bus stop (θ1) and she should have too late and wait for the next bus (θ2). Over an 8-day period she makes certain to be at the bus stop so early not to miss the bus and records the following arrival time of the bus.

5:15 PM , 5:21 PM , 5:14 PM , 5:23 PM , 5:29 PM , 5:17 PM , 5:15 PM , 5:18 PM

Estimate θ1 and θ2. Also give the MLEs for the mean and the variability of the arrival distribution.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 7 / 26

Invariance Property and Likelihood Equation of MLE Solution: Let T : arrival time of the bus. Then according to the question T ∼ R(θ1, θ2).

Let T1,T2, . . . ,Tn be n independent random arrival times of the bus.

Then the likelihood of θ = (θ1, θ2) is

L(θ) = 1

(θ2 − θ1)n if θ1 ≤ ti ≤ θ2; i = 1, 2, . . . , n

= 0 otherwise

= 0 otherwise

where t(1) = min {t1, t2, . . . , tn} and t(n) = max {t1, t2, . . . , tn} . Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 8 / 26

Invariance Property and Likelihood Equation of MLE

Under θ1 ≤ t(1) < t(n) ≤ θ2 , t(n) − t(1) ≤ θ2 − θ1.

Hence L(θ1, θ2) = 1 (θ2−θ1)n ≤

1 (t(n)−t(1))n = L(t(1), t(n)).

Thus MLE of (θ1, θ2) is (T(1),T(n)), where T(1) and T(n) are the minimum and maximum order statistic respectively.

Now E (T ) = θ1+θ2 2 and V (T ) = (θ2−θ1)2

12 are two continuous functions of (θ1, θ2).

So MLE of Mean : T(1)+T(n)

2 and of variability : (T(n)−T(1))√

12 (from invariance

property of MLE )

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 9 / 26

Invariance Property and Likelihood Equation of MLE Computation using R : (The minute component of the time has been considered as the data)

R Code and Output :

> samp=c(15 ,21 ,14 ,23 ,29 ,17 ,15 ,18)# given sample

> n=length(samp)# size of the sample

> n

> MLE_theta2=max(samp)

> cat("The mean arrival time is :",MLE_Mean ,"minutes after 5pm.\n")

The mean arrival time is : 21.5 minutes after 5pm.

> MLE_Var=(MLE_theta2 -MLE_theta1)/sqrt (12)# MLE of variability

> MLE_Var

Invariance Property and Likelihood Equation of MLE

Likelihood Equations and Related Discussions

lx(θ) = ln Lx(θ) is called log-likelihood function of θ.

Likelihood Equation : ∂lx (θ) ∂θ = 0.

Any MLE is a root of the likelihood equation.

Any root may be local minima or local maxima.

Possible verification for the root θ to be an MLE: ∂2lx (θ) ∂θ2 |θ=θ < 0.

If θ = (θ1, . . . , θs)′, the likelihood equations: ∂lx (θ) ∂θi

= 0 i = 1, . . . , s.

Possible verification for the root θ to be an MLE of θ: The Hessian matrix(( ∂2lx (θ) ∂θi∂θj

|θi=θi ,θj=θj ))

Invariance Property and Likelihood Equation of MLE

Result: Let T be a sufficient statistic for the family of distributions {fθ : θ ∈ } . If a unique MLE of θ exists, it is a (nonconstant) function of T . If a MLE of θ exists but is not unique, one can find a MLE that is a function of T .

Proof. Since T is sufficient, from Neyman-Fisher factorisability we can write

L(θ) = fθ(x) = gθ(T (x))h(x)

for all x, all θ and some h and gθ, where L(θ) is the likelihood function of θ and fθ(x) is the joint pmf or pdf of sample observations x = (x1, . . . , xn).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 12 / 26

Invariance Property and Likelihood Equation of MLE

If L(θ) is maximised by a unique MLE θ, naturally it will also maximise the function gθ(T (x)).

=⇒ θ should be a function of T .

If MLE of θ exists but is not unique, then ∃ some MLE θ that can be expressed as a function of T .

Proved

Invariance Property and Likelihood Equation of MLE

Result: Under regular estimation case (i.e. the situation where all the regularity conditions of Cramer-Rao Inequality hold) if an estimator θ of θ attains the Cramer-Rao Lower Bound CRLB for the variance, the likelihood equation has a unique solution θ that maximises the likelihood function.

Proof. Let L(θ|x) denote the likelihood function of real-valued parameter θ given the sample observations x = (x1, . . . , xn). If fθ(x) denotes the joint

∂

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 14 / 26

Invariance Property and Likelihood Equation of MLE

that is ∂

∂θ log L(θ|x) = 0 has the unique solution

θ = θ.

Differentiating both sides of (∗) again with respect to θ we get

∂2

Hence ∂2

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 15 / 26

Invariance Property and Likelihood Equation of MLE

Now, If T is an unbiased estimator of a real-valued estimable parametric function g(θ) which is differentiable at least once, from CR regularity conditions we directly get∫

T (x))fθ(x)dx = g(θ)

∂

∂θ log fθ(x)dx = g ′(θ)

In particular choosing g(θ) = θ and noting that Eθ [ ∂ ∂θ log L(θ|X)

] = 0 we

Invariance Property and Likelihood Equation of MLE

Finally substituting

∂θ log L(θ|X)

(looking at (∗) as T (X) is just like θ(X) by its definition) we get

[k(θ)]−1Eθ

Thus from (∗∗) the S.O.C. for maximising L(θ) holds. Hence proved.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 17 / 26

Invariance Property and Likelihood Equation of MLE

Result: Suppose ∂2lx (θ) ∂θ2 ≤ 0 ∀ θ ∈ . Then θ satisfying ∂lx (θ)

∂θ = 0 is the global maxima.

Proof. lx(θ) = lx(θ) + (θ − θ)∂lx (θ) ∂θ |θ=θ + (θ−θ)2

2 ∂2lx (θ) ∂θ2 |θ=θ∗ θ∗ ∈ (θ, θ).

Note that the RHS ≤ lx(θ) because, in RHS the 2nd factor of 2nd term vanishes and the 2nd factor of the third term ≤ 0. Hence proved.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 18 / 26

Invariance Property and Likelihood Equation of MLE

Result: Suppose

(i) ∂lx (θ) ∂θ = 0 iff θ = θ.

(ii) ∂2lx (θ) ∂θ2 |θ=θ < 0. And

(iii) θ is an interior point of an interval I ⊂ .

Then θ is the global maxima.

Proof. If possible suppose θ∗ is such that lx(θ∗) > lx(θ). Then there must be a local minima in between the local maxima θ and θ∗. This means for that minima point also ∂lx (θ)

∂θ = 0, which is a contradiction to the supposition (i). Hence proved.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 19 / 26

Invariance Property and Likelihood Equation of MLE

Ex.1 Let X1,X2, . . . ,Xn ∼ N(µ, σ2) independently.

Then lx(θ) = constant -

∂σ2 = 0 imply µ = x , σ2 = 1 n

∑ (xi − x)2 = s2.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 20 / 26

Invariance Property and Likelihood Equation of MLE

∂2lx(θ) ∂µ2 |(µ,σ2)=(x ,s2) = − n

s2 , ∂2lx(θ) ∂∂(σ2)2 |(µ,σ2)=(x ,s2) = − n

s4 .

( −n/s2 0

0 −n/2s4

) is negative definite. Hence (X ,S2) is the global maxima point and is the MLE of (µ, σ2).

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 21 / 26

Invariance Property and Likelihood Equation of MLE

Aliter: If ψ(x) = x − 1− lnx then ψ′(x) = 1− 1/x , ψ′′(x) = 1/x2 > 0.

Therefore ψ(x) is minimum at x = 1 and minψ(x) = 1− 1− 0 = 0. Based on this result we can write

lx(µ, σ2)− lx(µ, σ2) =

σ2 − n 2 = n

Invariance Property and Likelihood Equation of MLE

TRY YOURSELF!

M4.1. Let X1,X2, . . . ,Xn ∼ Lognormal(µ, σ2) independently. Then MLE of (µ, σ2) is (Y ,S∗2) where Y = logX and (Y ,S∗2) = (sample mean , sample variance(with divisor n)) on Y .

Hint. If X ∼ Lognormal(µ, σ2) then Y = logX ∼ N(µ, σ2). Now proceed as in Ex. 1.

M4.2. (continuation)If in M4.1. µ = 0, find the MLE of σ2

M4.3. Consider a random sample of size n from Exponential (mean = β). It is given only that k , 0 < k < n, of these n observations are ≤ M, where M is a known positive number. Find the MLE of β.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 23 / 26

Invariance Property and Likelihood Equation of MLE

TUTORIAL DISCUSSION :

M4.2. If X ∼ Lognormal(0, σ2) then Y = logX ∼ N(0, σ2).

Given y = (y1, . . . , yn), the loglikelihood function of σ2 is

l(σ2) = constant − n

2 σ2 − 1

σ2 = 1 n

n∑ i=1

y2 i = σ2 (say) is the only solution of the likelihood equation.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 24 / 26

Invariance Property and Likelihood Equation of MLE

Also ∂2

2σ2 < 0.

Moreover σ2 is an interior point of the parameterspace = (0,∞).

=⇒ σ2 is the point of global maxima of the likelihood function i.e. the unique MLE of σ2.

Saurav De (Department of Statistics Presidency University)Invariance Property and Likelihood Equation of MLE 25 / 26

Invariance Property and Likelihood Equation of MLE

M4.3. Let X1,X2, . . . ,Xn ∼ Exponential (mean = β).

Define Yi = 1(0) if Xi ≤ M (Xi > M).

Then Y1,Y2, . . . ,Yn ∼ Bin (1, θ) ; 0 < θ < 1,

where θ = Pβ[X1 ≤ M] = 1− exp [ −M

β

n∑ i=1

n . (See the Application

4).

Hence by the invariance property the MLE of β is − M log(1− k

n ) .