> For the complete documentation index, see [llms.txt](https://zavalab.gitbook.io/statschemebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://zavalab.gitbook.io/statschemebook/chapter-4-estimation-for-random-variables/01-estimation-techniques.md).

# Estimation Techniques

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**Slides:** [stats\_ch4\_estimationRV.pdf](https://github.com/vzavala/StatsBook/blob/master/Slides/stats_ch4_estimationRV.pdf) · [4-up handout](https://github.com/vzavala/StatsBook/blob/master/Slides/handouts/stats_ch4_estimationRV_4up.pdf) · **Code:** [Python notebooks](https://github.com/vzavala/StatsBook/tree/master/Python) · [MATLAB scripts](https://github.com/vzavala/StatsBook/tree/master/Matlab)
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* We have now a basic idea of the types of RV models available to model phenomena.
* We proceed to develop procedures to determine if an RV model fits data at hand.
* We define an RV model ($$f\_X(x|\theta)$$ or $$F\_X(x|\theta)$$), where $$\theta$$ are the model parameters.
* By estimating RV model we mean that we seek to find $$\theta$$ that *best* fits data.

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We explore a couple of estimation methods:

* Point Estimation (Method of Moments and Least-Squares Method)

* Maximum Likelihood Estimation (MLE)
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* First step will be to explore our data and application and postulate an RV model (e.g., Gaussian or Exponential) based on any patterns exposed.

* Second step will be to find $$\theta$$ that *best* fits data. If best fit is not adequate, we can conclude that we need to propose a different model.