Timeline for Intuition behind logistic regression
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 6, 2016 at 20:39 | comment | added | AdamO | @user48956 Statistical Analysis with Missing Dada, Little & Rubin 2nd ed. Missing data is not "represented" per se, but "handled" by omission. This is not particular to logistic regression: it is the naive approach used by all statistical models. When data are formatted in a rectangular array, rows with missing values are omitted. This is known as a complete case analysis. GLMs and GLMMS are robust to missing data in the sense that complete case analyses are usually unbiased and not very inefficient. | |
| Jul 6, 2016 at 19:58 | comment | added | user48956 | You say: logit "robust to missing at random data". Can you provided a reference? How is missing data even represented in logit? | |
| Sep 30, 2013 at 16:47 | comment | added | AdamO | Elements of Statistical Learning by Hastie, Tibshirani, Friedman. | |
| Sep 30, 2013 at 14:23 | comment | added | user16168 | In general, what textbook do you advise in Machine learning with very detailed descriptive content? | |
| Sep 29, 2013 at 22:16 | comment | added | AdamO | I think McCullough and Nelder's book Generalized Linear Models would be a great background resource for a more statistics perspective. | |
| Sep 29, 2013 at 19:52 | comment | added | user16168 | Thank you very much for your answer! It seems like I have a huge lack in background. | |
| Sep 29, 2013 at 19:50 | vote | accept | user16168 | ||
| Sep 26, 2013 at 22:45 | history | edited | AdamO | CC BY-SA 3.0 | added 4 characters in body |
| Sep 26, 2013 at 19:52 | history | edited | AdamO | CC BY-SA 3.0 | added 1147 characters in body |
| Sep 26, 2013 at 19:46 | history | answered | AdamO | CC BY-SA 3.0 |