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Jun 16, 2022 01:21 PM
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Local Regression
We slide a window across the predictor values, and only observations in that window are used to make a regression model.
The model here could be a linear regression, or even a polynomial model. After all, we average the values as they go further.
We treat EVERY observation sort of features in the middle of as one window.
- 🤬 Hard to write up an equation or function easily.
- 😀 Does well in the awkward relationship.
- Take a sliding window to compute regression model
- then combine the results by averaging
Assumption:
- Around point x, the mean of y can be approximated by a small class of parametric functions in polynomial regression.
- The errors in estimating y are independent and randomly distributed with a mean of zero.
- Bias and variance are traded off by the choices for the settings of span and degree of polynomial.
Polynomial Regression
Polynomial Regression basically transforms (e.g., making x to be cubic) the variable to make a new variable, and then fit a model
Orthogonal Polynomial Regression makes the variables to be as uncorrelated as possible with the first variable; there's no correlation between the two variables so it sets up a system of new variables that are really just polynomials, but that's a special polynomial it adds additional parts
Assumption:
- the behavior of a dependent variable y is explained by a linear, or nonlinear — curvilinear, additive relationship between the dependent variable and a set of k independent variables (xi, i=1 to k)),
- the relationship between the dependent variable y and any independent variable xi is linear or curvilinear (specifically polynomial),
- the independent variables xi are independent of each other
- the errors are independent, normally distributed with mean zero and a constant variance (OLS).
Step Functions (Skipped in lecture 2 but used in tree models)
Essentially, cutting up all predictor into chunks.
Basis Functions
Regression Splines & Smoothing Splines
The degree of freedom (deg) is the number of knots in the natural spline model.
- For each sample, the R2 increases as deg increases.
- The fitted curve is more flexible as deg increases. Deg 10 appears to capture the true model shape better.
Generalized Additive Models
It is just an AUTOMATED version of Spline.
Assumption (Further here)
2'. Homogeneity of variance (Similar variance)
3'. Normality of residuals
FAQ
Reference
Extra Resource
- Author:Jason Siu
- URL:https://jason-siu.com/article/cae2c463-b61b-4a67-b833-0063da3f9943
- Copyright:All articles in this blog, except for special statements, adopt BY-NC-SA agreement. Please indicate the source!
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