Logistic Regression Model Error Term
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Logistic Regression Error Variance
company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered logit regression Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes logit model example a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Logistic Regression - Error Term and its Distribution up vote 12 down vote favorite 6 On whether an error term exists in logistic regression (and its assumed distribution), I have read in various places that:
Probit Model
no error term exists the error term has a binomial distribution (in accordance with the distribution of the response variable) the error term has a logistic distribution Can someone please clarify? logistic binomial bernoulli-distribution share|improve this question edited Nov 20 '14 at 12:43 Frank Harrell 39.1k173156 asked Nov 20 '14 at 10:57 user61124 6314 4 With logistic regression - or indeed GLMs more generally - it's typically not useful to think in terms of the observation $y_i|\mathbf{x}$ as "mean + error". Better to think in terms of the conditional distribution. I wouldn't go so far as to say 'no error term exists' as 'it's just not helpful to think in those terms'. So I wouldn't so much say it's a choice between 1. or 2. as I would say it's generally better to say "none of the above". However, irrespective of the degree to which one might argue for "1." or "2.", though, "3." is definitely wrong. Where did you see that? –Glen_b♦ Nov 20 '14 at 13:52 @Glen_b: Might one argue for (2)? I've known people to say it b
(academic discipline) Machine Learning Existence QuestionIs there an error term in logistic regression?If so, does it have a particular distribution, like the normal error in linear regression?UpdateCancelAnswer Wiki2 Answers Michael Hochster, PhD in Statistics, Stanford; Director logit vs probit of Research, PandoraWritten 75w ago · Upvoted by Peter Flom, Independent statistical consultant simple logistic regression example for researchers in behavioral, social and medical sciencesYou can think of the logistic regression model as arising from a linear
Binary Logistic Regression Spss
model plus a logistic error term, but all you observe is a 1 if the linear part plus error is positive and 0 if it is negative. This is called the latent variable formulation, http://stats.stackexchange.com/questions/124818/logistic-regression-error-term-and-its-distribution and you can learn more details about it here:Logistic regressionYou can get other kinds of model (e.g. probit) by assuming a different distribution for the error term.3.9k Views · View UpvotesRelated QuestionsMore Answers BelowHow can the errors of logistic regression be modelled using likelihood principal?What does the bias term represent in logistic regression?Machine Learning: In layman's terms, what is the relationship between Grid Search and Logistic Regression?Are there https://www.quora.com/Is-there-an-error-term-in-logistic-regression researchers actively working on logistic regression?Why is logistic regression considered a linear model? Jay Verkuilen, PhD Psychometrics, MS Mathematical Statistics, UIUCWritten 75w ago · Upvoted by Justin Rising, MSE in CS, PhD in Statistics and Peter Flom, Independent statistical consultant for researchers in behavioral, social and medical sciencesYes but it's implicit. By assuming that the binary variable is Bernoulli conditionally on the regressors, we have chosen it as the error distribution. The regression is not linear though so it's not expressible as an additive error term.1.9k Views · View Upvotes · Answer requested by 1 personView More AnswersRelated QuestionsWhat is the difference between linear classification and logistic regression?What is logistic regression?What's the relationship between linear and logistic regression?What is autologistic regression in layman's terms and how is it related to logistic regression and Markov random field?Why is the asymptotic generalization error of logistic regression less than or equal to that of naive Bayes?How do you implement your own loss/error function in linear/logistic regression? Please provide an example.What are the recommended Python machine learning libraries for boosting, logistic regression etc in terms of performance?Is there a comparison for logistic regression in terms of accuracy between MLlib and BidMach?What can we say abo
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