Quiz: MLE Mastery Quiz


MLE Mastery QuizOnline version MLE basics quiz by s rohan 1 What does MLE stand for in statistics? a Most Likely Event b Maximum Likelihood Evaluation c Maximum Likelihood Estimation d Mean Likelihood Estimation 2 In MLE, which function is typically maximized to estimate parameters? a Probability density function b Moment generating function c Cumulative distribution function d Log-likelihood function 3 For iid normal data with known variance, what is the MLE for the mean mu? a Median b Mode c Midpoint d Sample mean 4 In MLE, what must be specified to estimate parameters? a Data without assumptions b Correlation structure c Probabilistic model (likelihood) d P-value 5 Under regularity conditions, what is a key asymptotic property of the MLE? a Always finite-sample optimal b Inconsistent c Consistency d Unbiased 6 Why do we maximize the log-likelihood instead of the likelihood? a Because log-likelihood is always higher b Because log is monotone and turns products into sums c Because it avoids using derivatives d Because the likelihood is always negative 7 For iid Poisson data with unknown rate lambda, the MLE for lambda is... a Sum of counts b Mode of counts c Sample mean of the counts d Sample median 8 What does the invariance property of MLE state? a MLE always exists b MLE is always unbiased c MLE of g(theta) is g(theta_hat) d MLE is always finite 9 What method is commonly used to compute MLE for complex models? a Closed-form solution always exists b Exact algebraic solution c Numerical optimization (e.g., Newton-Raphson) d Analytical integration 10 Is MLE applicable to independent but non-identically distributed data? a No, only IID data allowed b Only for normal distribution c Yes, using the joint likelihood d Only discrete data

1

What does MLE stand for in statistics?

a

Most Likely Event

b

Maximum Likelihood Evaluation

c

Maximum Likelihood Estimation

d

Mean Likelihood Estimation

2

In MLE, which function is typically maximized to estimate parameters?

a

Probability density function

b

Moment generating function

c

Cumulative distribution function

d

Log-likelihood function

3

For iid normal data with known variance, what is the MLE for the mean mu?

a

Median

b

Mode

c

Midpoint

d

Sample mean

4

In MLE, what must be specified to estimate parameters?

a

Data without assumptions

b

Correlation structure

c

Probabilistic model (likelihood)

d

P-value

5

Under regularity conditions, what is a key asymptotic property of the MLE?

a

Always finite-sample optimal

b

Inconsistent

c

Consistency

d

Unbiased

6

Why do we maximize the log-likelihood instead of the likelihood?

a

Because log-likelihood is always higher

b

Because log is monotone and turns products into sums

c

Because it avoids using derivatives

d

Because the likelihood is always negative

7

For iid Poisson data with unknown rate lambda, the MLE for lambda is...

a

Sum of counts

b

Mode of counts

c

Sample mean of the counts

d

Sample median

8

What does the invariance property of MLE state?

a

MLE always exists

b

MLE is always unbiased

c

MLE of g(theta) is g(theta_hat)

d

MLE is always finite

9

What method is commonly used to compute MLE for complex models?

a

Closed-form solution always exists

b

Exact algebraic solution

c

Numerical optimization (e.g., Newton-Raphson)

d

Analytical integration

10

Is MLE applicable to independent but non-identically distributed data?

a

No, only IID data allowed

b

Only for normal distribution

c

Yes, using the joint likelihood

d

Only discrete data

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MLE Mastery QuizOnline version

by s rohan

What does MLE stand for in statistics?

In MLE, which function is typically maximized to estimate parameters?

For iid normal data with known variance, what is the MLE for the mean mu?

In MLE, what must be specified to estimate parameters?

Under regularity conditions, what is a key asymptotic property of the MLE?

Why do we maximize the log-likelihood instead of the likelihood?

For iid Poisson data with unknown rate lambda, the MLE for lambda is...

What does the invariance property of MLE state?

What method is commonly used to compute MLE for complex models?

Is MLE applicable to independent but non-identically distributed data?

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