This kind of regression seems to be much more difficult. I've read several sources, but the calculus for general quantile regression is going over my head. My question is this: How can I calculate the slope of the line of best fit that minimizes L1 error? Some constraints on the answer I am looking for:
I have a problem where I need to standardize the variables run the (ridge regression) to calculate the ridge estimates of the betas. I then need to convert these back to the original variables scale.
Multivariable regression is any regression model where there is more than one explanatory variable. For this reason it is often simply known as "multiple regression". In the simple case of just one explanatory variable, this is sometimes called univariable regression. Unfortunately multivariable regression is often mistakenly called multivariate regression, or vice versa. Multivariate ...
Linear regression can use the same kernels used in SVR, and SVR can also use the linear kernel. Given only the coefficients from such models, it would be impossible to distinguish between them in the general case (with SVR, you might get sparse coefficients depending on the penalization, due to $\epsilon$-insensitive loss)
What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?
Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values. Hence, it must be non-negative. For simple OLS regression with one predictor, this is equivalent to the squared correlation between the predictor and the dependent variable -- again, this must be non-negative.
I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mea...
Consider the following figure from Faraway's Linear Models with R (2005, p. 59). The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a
The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be the ...
In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividin...
