No Error Term In Logistic Regression
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Logistic Regression Error Variance
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Assumptions Of Logistic Regression
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: no error term exists the error term has a binomial distribution (in accordance with the distribution of binary logistic regression spss 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 but never to defend it when it's questioned. –Scortchi♦ Nov 20 '14 at 14:49 2 @Glen_b All three statements have constructive interpretations in which they are true. (3) is addressed at en.wikipedia.org/wiki/Logistic_distribution#Applications and en.wikipedia
(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
Binary Logistic Regression Example
linear regression?UpdateCancelAnswer Wiki2 Answers Michael Hochster, PhD in Statistics, Stanford; Director of
Logit Distribution
Research, PandoraWritten 75w ago · Upvoted by Peter Flom, Independent statistical consultant for researchers in behavioral, social logistic error distribution and medical sciencesYou can think of the logistic regression model as arising from a linear model plus a logistic error term, but all you observe is a 1 http://stats.stackexchange.com/questions/124818/logistic-regression-error-term-and-its-distribution if the linear part plus error is positive and 0 if it is negative. This is called the latent variable formulation, 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 https://www.quora.com/Is-there-an-error-term-in-logistic-regression 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 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 asymptoti
model Generalized linear model Discrete choice Logistic regression Multinomial logit Mixed logit Probit Multinomial probit Ordered logit Ordered probit Poisson Multilevel model Fixed effects Random effects Mixed https://en.wikipedia.org/wiki/Logistic_regression model Nonlinear regression Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle Local Segmented Errors-in-variables Estimation Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Non-negative Iteratively reweighted Ridge regression Least absolute deviations Bayesian Bayesian multivariate Background Regression model validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem logistic regression Statistics portal v t e "Logit model" redirects here. It is not to be confused with Logit function. In statistics, logistic regression, or logit regression, or logit model[1] is a regression model where the dependent variable (DV) is categorical. This article covers the case of binary dependent variables—that is, where it can take only two values, such logistic regression error as pass/fail, win/lose, alive/dead or healthy/sick. Cases with more than two categories are referred to as multinomial logistic regression, or, if the multiple categories are ordered, as ordinal logistic regression.[2] Logistic regression was developed by statistician David Cox in 1958.[2][3] The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). As such it is not a classification method. It could be called a qualitative response/discrete choice model in the terminology of economics. Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution. Thus, it treats the same set of problems as probit regression using similar techniques, with the latter using a cumulative normal distribution curve instead. Equivalently, in the latent variable interpretations of these two methods, the first assumes a standard logistic distribution of errors and the second a standard normal distribution of errors.[citation needed] Logistic