Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with least-squares
Search options not deleted
user 261146
Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value. Gaussian linear models are fit by least squares and least squares is the idea underlying the use of mean-squared-error (MSE) as a way of evaluating an estimator.
2
votes
Accepted
Is it possible for the zero conditional mean assumption to fail?
The short answer is yes, $\mathbb{E}[X_i\varepsilon_i] = 0$ by construction.
A few important remarks:
However, this does not imply that $\mathbb{E}[\varepsilon_i \mid X_i] = 0$.
The conditional mean …
- 928
10
votes
Why don't linear regression assumptions matter in machine learning?
A linear regression is a statistical procedure that can be interpreted from both perspectives. Instead I will tackle the question of comparing linear regression (and its assumptions) to other methods. …
- 928