Transcript The Likelihood Function

The Likelihood Function - Introduction
• Recall: a statistical model for some data is a set  f :    of
distributions, one of which corresponds to the true unknown
distribution that produced the data.
• The distribution fθ can be either a probability density function or a
probability mass function.
• The joint probability density function or probability mass function
of iid random variables X1, …, Xn is
n
f  x1 ,..., x n    f  xi .
i 1
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The Likelihood Function
• Let x1, …, xn be sample observations taken on corresponding random
variables X1, …, Xn whose distribution depends on a parameter θ. The
likelihood function defined on the parameter space Ω is given by
L | x1 ,..., xn   f x1 ,..., xn .
• Note that for the likelihood function we are fixing the data, x1,…, xn,
and varying the value of the parameter.
• The value L(θ | x1, …, xn) is called the likelihood of θ. It is the
probability of observing the data values we observed given that θ is the
true value of the parameter. It is not the probability of θ given that we
observed x1, …, xn.
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Examples
• Suppose we toss a coin n = 10 times and observed 4 heads. With no
knowledge whatsoever about the probability of getting a head on a
single toss, the appropriate statistical model for the data is the
Binomial(10, θ) model. The likelihood function is given by
• Suppose X1, …, Xn is a random sample from an Exponential(θ)
distribution. The likelihood function is
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Maximum Likelihood Estimators
• In the likelihood function, different values of θ will attach different
probabilities to a particular observed sample.
• The likelihood function, L(θ | x1, …, xn), can be maximized over θ,
to give the parameter value that attaches the highest possible
probability to a particular observed sample.
• We can maximize the likelihood function to find an estimator of θ.
• This estimator is a statistics – it is a function of the sample data. It is
denoted by ˆ.
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The log likelihood function
• l(θ) = ln(L(θ)) is the log likelihood function.
• Both the likelihood function and the log likelihood function have
their maximums at the same value of ˆ.
• It is often easier to maximize l(θ).
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Examples
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Properties of MLE
• The MLE is invariant, i.e., the MLE of g(θ) equal to the function g
evaluated at the MLE.
• Proof:
• Examples:
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Important Comment
• Some MLE’s cannot be determined using calculus. This occurs
whenever the support is a function of the parameter θ.
• These are best solved by graphing the likelihood function.
• Example:
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