Timeline for linear regression on exponential distributed dependent variable
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| May 12, 2017 at 3:47 | comment | added | Glen_b | It will be important to be clear about whether the f's and g's are known (and if not known exactly, other properties like monotonicity or smoothness may be important) | |
| May 12, 2017 at 3:45 | comment | added | user6396 | right, I am posing another question. | |
| May 12, 2017 at 3:42 | comment | added | Glen_b | Are the f's and g's known? (Note that when I said "in a new question" I meant click the "ASK QUESTION" button and post a new question) | |
| May 12, 2017 at 3:36 | comment | added | user6396 | question2: suppose I have a dataset with dependent variable y, and 1000 features (or independent variables) x_s, after removing some feature which are highly correlated with other features, I have, saying 300 x_s left, for whatever reasons, I decide to use linear regression (ols or glm, with or without regularization), I don't assume y is linearly dependent on x_s, but I assume the linear relation holds between f(y) and [g1(x1),g2(x2),...], where f and g1,g2... are just some transformation function, such as log. how should I proceed? | |
| May 12, 2017 at 3:09 | comment | added | Glen_b | 1. Not necessarily. If they were jointly normal, yes, but that's not a good reason to transform since you may already satisfy the requirements of regression and transformation may screw up more important things. 2. I'm not quite sure I follow. Perhaps you could write a longer explanation in a new question. | |
| May 12, 2017 at 3:03 | comment | added | user6396 | Thank you for the great answer. I have following questions: 1. I understand that whether linear regression is valid has no direct link to the distribution of y and x, but if both x and y are normal, the residual should be normal right? this is why I transform all x_s and y so that the histogram of them looks more or less normal. 2. If linear regression (ols or glm) is chosen for the sake of being interpretable, and I only assume there is a linear relation between f(y) and [g1(x1),g2(x2),gn(xn)], are there any guidelines on how to treat x_s and y (again, embarrassingly general )? | |
| May 12, 2017 at 2:40 | history | edited | Glen_b | CC BY-SA 3.0 |
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| May 11, 2017 at 18:28 | history | answered | Glen_b | CC BY-SA 3.0 |