Distribution Error Term Logistic Regression
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Why Is There No Error Term In Logistic Regression
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Logistic Regression Normal Distribution
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Logistic Regression Distribution Assumption
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 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 interaction term logistic regression at 12:43 Frank Harrell 39.1k172156 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.org/wiki/Discrete_choice#Binary_Choice. –whuber♦ Nov 20 '14 at 20:11 @whuber: I've corrected my answer wrt (3), which wasn't well thought through; but still puzzled about in what sense (2) might be right. –Scortchi♦ Nov 20 '14 at 21:27 1 @Scortchi Although you are right that (2)
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 model Nonlinear regression interaction term logistic regression sas Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle Local Segmented interaction term logistic regression spss Errors-in-variables Estimation Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Non-negative Iteratively reweighted Ridge quadratic term in logistic regression 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 Statistics portal v t e http://stats.stackexchange.com/questions/124818/logistic-regression-error-term-and-its-distribution "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 as pass/fail, win/lose, alive/dead or healthy/sick. Cases with https://en.wikipedia.org/wiki/Logistic_regression 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 regression can be seen as a special case of the generalized linear model and thus analog
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