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Carl
Carl
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Estimating likelihood functions entails a two-step process.

First, one declares the log-likelihood function. Then one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. Minimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^{-1}$. The reason that we do not have to multiply the Hessian by -1 is that all the evaluation has been done in terms of -1 times the log-likelihood. This means that the Hessian that is produced by optim is already multiplied by -1.

Estimating likelihood functions entails a two-step process.

First, one declares the log-likelihood function. Then one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. Minimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^{-1}$. The reason that we do not have to multiply the Hessian by -1 is that all the evaluation has been done in terms of -1 times the log-likelihood. This means that the Hessian that is produced by optim is already multiplied by -1.

Estimating likelihood functions entails a two-step process.

First, one declares the log-likelihood function. Then one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. Minimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^{-1}$. The reason that we do not have to multiply the Hessian by -1 is that the evaluation has been done in terms of -1 times the log-likelihood. This means that the Hessian that is produced by optim is already multiplied by -1.

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edit approved Jun 13, 2021 at 0:02
Aubergine
edit approved Jun 13, 2021 at 0:02
Aubergine
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Estimating likelihood functions entails a two-step process.

First, one declares the log-likelihood function. thenThen one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. minimizationMinimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^-1$$(-H)^{-1}$. theThe reason that we do not have to multiply the hassianHessian by -1 is that all of the evaluation has been done in terms of -1 times the log-likelihood. This means that the hessianHessian that is produced by optim is already multiplied by -1.

Estimating likelihood functions entails a two-step process.

First, one declares the log-likelihood function. then one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. minimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^-1$. the reason that we do not have to multiply the hassian by -1 is that all of the evaluation has been done in terms of -1 times the log-likelihood. This means that the hessian that is produced by optim is already multiplied by -1

Estimating likelihood functions entails a two-step process.

First, one declares the log-likelihood function. Then one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. Minimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^{-1}$. The reason that we do not have to multiply the Hessian by -1 is that all the evaluation has been done in terms of -1 times the log-likelihood. This means that the Hessian that is produced by optim is already multiplied by -1.

edited the body
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edit approved Sep 23, 2015 at 14:17
Learner
edit approved Sep 23, 2015 at 14:17
Learner
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Estimating likelihood functions entails a two-step process. 

First, one declares the log-likelihood function. then one optimizes the log-likelihood functions. that'sThat's fine. writing

Writing the log-likelihood functions in R, we ask for -1*l$-1*l$ (where l$l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. minimization of -l is the same as maximization of l, which is what we want. Now

Now, the observed Fisher Information Matrix is equal to (-H)^-1$(-H)^-1$. the reason that we do not have to mulitplymultiply the hassian by -1 is that all of the evaluation has been done in terms of -1 times the log-likelihood. thisThis means that the hessian that is produced by optim is already multiplied by -1

Estimating likelihood functions entails a two-step process. First, one declares the log-likelihood function. then one optimizes the log-likelihood functions. that's fine. writing the log-likelihood functions in R, we ask for -1*l (where l represents the log - likelihood function) because the optim command in R minimizes a function by default. minimization of -l is the same as maximization of l, which is what we want. Now, the observed Fisher Information Matrix is equal to (-H)^-1. the reason that we do not have to mulitply the hassian by -1 is that all of the evaluation has been done in terms of -1 times the log-likelihood. this means that the hessian that is produced by optim is already multiplied by -1

Estimating likelihood functions entails a two-step process. 

First, one declares the log-likelihood function. then one optimizes the log-likelihood functions. That's fine.

Writing the log-likelihood functions in R, we ask for $-1*l$ (where $l$ represents the log - likelihood function) because the optim command in R minimizes a function by default. minimization of -l is the same as maximization of l, which is what we want.

Now, the observed Fisher Information Matrix is equal to $(-H)^-1$. the reason that we do not have to multiply the hassian by -1 is that all of the evaluation has been done in terms of -1 times the log-likelihood. This means that the hessian that is produced by optim is already multiplied by -1

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Adelino Martins
Adelino Martins
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