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- Binomial_regression abstract "In statistics, binomial regression is a technique in which the response (often referred to as Y) is the result of a series of Bernoulli trials, or a series of one of two possible disjoint outcomes (traditionally denoted "success" or 1, and "failure" or 0). In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables.Binomial regression models are essentially the same as binary choice models, one type of discrete choice model. The primary difference is in the theoretical motivation: Discrete choice models are motivated using utility theory so as to handle various types of correlated and uncorrelated choices, while binomial regression models are generally described in terms of the generalized linear model, an attempt to generalize various types of linear regression models. As a result, discrete choice models are usually described primarily with a latent variable indicating the "utility" of making a choice, and with randomness introduced through an error variable distributed according to a specific probability distribution. Note that the latent variable itself is not observed, only the actual choice, which is assumed to have been made if the net utility was greater than 0. Binary regression models, however, dispense with both the latent and error variable and assume that the choice itself is a random variable, with a link function that transforms the expected value of the choice variable into a value that is then predicted by the linear predictor. It can be shown that the two are equivalent, at least in the case of binary choice models: the link function corresponds to the quantile function of the distribution of the error variable, and the inverse link function to the cumulative distribution function (CDF) of the error variable. The latent variable has an equivalent if one imagines generating a uniformly distributed number between 0 and 1, subtracting from it the mean (in the form of the linear predictor transformed by the inverse link function), and inverting the sign. One then has a number whose probability of being greater than 0 is the same as the probability of success in the choice variable, and can be thought of as a latent variable indicating whether a 0 or 1 was chosen.In machine learning, binomial regression is considered a special case of probabilistic classification, and thus a generalization of binary classification.".
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- Binomial_regression wikiPageRevisionID "656606739".
- Binomial_regression wikiPageWikiLink Bernoulli_trial.
- Binomial_regression wikiPageWikiLink Binary_choice_model.
- Binomial_regression wikiPageWikiLink Binary_classification.
- Binomial_regression wikiPageWikiLink Binomial_distribution.
- Binomial_regression wikiPageWikiLink Categorical_variable.
- Binomial_regression wikiPageWikiLink Category:Generalized_linear_models.
- Binomial_regression wikiPageWikiLink Cumulative_distribution_function.
- Binomial_regression wikiPageWikiLink Dependent_and_independent_variables.
- Binomial_regression wikiPageWikiLink Dependent_variable.
- Binomial_regression wikiPageWikiLink Discrete_choice.
- Binomial_regression wikiPageWikiLink Dummy_variable.
- Binomial_regression wikiPageWikiLink Error_variable.
- Binomial_regression wikiPageWikiLink Explanatory_variable.
- Binomial_regression wikiPageWikiLink Generalised_linear_model.
- Binomial_regression wikiPageWikiLink Generalized_extreme_value_distribution.
- Binomial_regression wikiPageWikiLink Generalized_linear_model.
- Binomial_regression wikiPageWikiLink Generative_model.
- Binomial_regression wikiPageWikiLink Identifiability.
- Binomial_regression wikiPageWikiLink Independent_variable.
- Binomial_regression wikiPageWikiLink Indicator_function.
- Binomial_regression wikiPageWikiLink Latent_variable.
- Binomial_regression wikiPageWikiLink Latent_variable_model.
- Binomial_regression wikiPageWikiLink Likelihood.
- Binomial_regression wikiPageWikiLink Likelihood_function.
- Binomial_regression wikiPageWikiLink Linear_probability_model.
- Binomial_regression wikiPageWikiLink Linear_regression.
- Binomial_regression wikiPageWikiLink Link_function.
- Binomial_regression wikiPageWikiLink Logistic_distribution.
- Binomial_regression wikiPageWikiLink Logistic_function.
- Binomial_regression wikiPageWikiLink Logistic_regression.
- Binomial_regression wikiPageWikiLink Logit.
- Binomial_regression wikiPageWikiLink Logit_function.
- Binomial_regression wikiPageWikiLink Logit_model.
- Binomial_regression wikiPageWikiLink Machine_learning.
- Binomial_regression wikiPageWikiLink Maximum_likelihood.
- Binomial_regression wikiPageWikiLink Normal_distribution.
- Binomial_regression wikiPageWikiLink Ordinal_data.
- Binomial_regression wikiPageWikiLink Ordinal_variable.
- Binomial_regression wikiPageWikiLink Poisson_regression.
- Binomial_regression wikiPageWikiLink Predictive_modelling.
- Binomial_regression wikiPageWikiLink Probabilistic_classification.
- Binomial_regression wikiPageWikiLink Probability_distribution.
- Binomial_regression wikiPageWikiLink Probit_model.
- Binomial_regression wikiPageWikiLink Quantile_function.
- Binomial_regression wikiPageWikiLink Random_variable.
- Binomial_regression wikiPageWikiLink Regression_coefficient.
- Binomial_regression wikiPageWikiLink Response_variable.
- Binomial_regression wikiPageWikiLink Scale_parameter.
- Binomial_regression wikiPageWikiLink Standard_normal_distribution.
- Binomial_regression wikiPageWikiLink Statistics.
- Binomial_regression wikiPageWikiLink Students_t-distribution.
- Binomial_regression wikiPageWikiLink Utility.
- Binomial_regression wikiPageWikiLink Utility_theory.
- Binomial_regression wikiPageWikiLinkText "Binomial regression".
- Binomial_regression wikiPageWikiLinkText "binary response model".
- Binomial_regression wikiPageWikiLinkText "binomial regression".
- Binomial_regression hasPhotoCollection Binomial_regression.
- Binomial_regression wikiPageUsesTemplate Template:=.
- Binomial_regression wikiPageUsesTemplate Template:Statistics.
- Binomial_regression subject Category:Generalized_linear_models.
- Binomial_regression hypernym Technique.
- Binomial_regression type Article.
- Binomial_regression type Model.
- Binomial_regression type Software.
- Binomial_regression type Article.
- Binomial_regression type Model.
- Binomial_regression comment "In statistics, binomial regression is a technique in which the response (often referred to as Y) is the result of a series of Bernoulli trials, or a series of one of two possible disjoint outcomes (traditionally denoted "success" or 1, and "failure" or 0).".
- Binomial_regression label "Binomial regression".
- Binomial_regression sameAs m.0cv1w7.
- Binomial_regression sameAs Q4914493.
- Binomial_regression sameAs Q4914493.
- Binomial_regression wasDerivedFrom Binomial_regression?oldid=656606739.
- Binomial_regression isPrimaryTopicOf Binomial_regression.