# Questions tagged [likelihood]

Given a random variable $X$ which arise from a parameterized distribution $F(X;θ)$, the likelihood is defined as proportional to the probability of observed data as a function of $θ$: $\operatorname{L}(θ | x)=\operatorname{P}(X=x \mid θ)$

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### How to make the profile likelihood model for estimation?

I tried to make the age estimation model using the chemical compound results from The soil. Initially, I used the multivariable regression model. However, the reviewer highly recommend using the ...
1 vote
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### To what extent can likelihood methods be used for functional responses?

Let's suppose that we are working with a functional data set, $Y_i(t)$, $Y_i\in L^2[0,1]$, $1\le i\le n$. If we were working with univariate or even multivariate data set, likelihood methods would ...
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### Is Pitman-Koopman-Darmois Theorem valid for discrete random variables?

I am interested in the Pitman-Koopman-Darmois theorem. I'm having a hard time finding a simple rigorous version of this theorem as I struggle finding sources. This helpful post provides three sources ...
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### Turning a list of cost into categorical probability mass distribution

Background Given a noisy dataset $D$, I have to solve a classification problem where the possible anserwer is $i\in\{1,\dots,N\}$. So far I can get pretty decent result with an algorithm that, based ...
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75 views

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### Are these two equivalent forms for the likelihood of a Poisson point process?

I have a Poisson point process in a bounded region $W$. I'm trying to calculate the likelihood of observing a particular set of points within $W$. I'm told that there are two equivalent forms of ...
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### Is likelihood the y axis coordinate on the distribution curve?

Josh Starmer says it in here. I have been searching for a simple way to understand likelihood and it's Bayesian and Frequentist use. Josh's way seems simple to me. Is he correct?
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1 vote
The score test says that we take the derivative of the log-likelihood at $H_0$ and divide it by the fisher information at $H_0$. $U(\theta )={\frac {\partial \log L(\theta \mid x)}{\partial \theta }}.$...