Quasi Standard Error
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Glm.fit: Fitted Probabilities Numerically 0 Or 1 Occurred In R
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Firth Logistic Regression R
are voted up and rise to the top Standard error computation in a non-linear model up vote 1 down vote favorite I have the following model: $$ Y_t = \varphi(X_t^\prime\beta)\times Z_t $$ where $X_t$ is a vector of exogenous regressors and $Y_t$ and $Z_t$ are random variables and $\phi$ is a cumulative normal distribution. Can I use quasi-MLE standard errors of individual parameters of the beta vector or it is necessary a solution to the problem of separation in logistic regression to use a delta method to obtain SE? I believe they should be similar but I'm not sure. maximum-likelihood standard-error delta-method share|improve this question edited Nov 13 '15 at 12:11 mpiktas 24.8k449104 asked Nov 13 '15 at 12:08 user17648 82 add a comment| 1 Answer 1 active oldest votes up vote 0 down vote accepted You can use quasi-MLE standard errors, if you are sure that quasi-MLE really works. Both MLE and quasi-MLE are estimation methods which ensure that your estimates are asymptotically normal: $$\sqrt{n}(\hat{\theta}_n-\theta_0)\to N(0, \Sigma)$$ So you immediately get the standard errors of estimates. Now Delta method is used to get the asymptotic result for the function of $\hat{\theta}_n$. To be more precise Delta method says that for function $g$: $$\sqrt{n}(g(\hat{\theta}_n)-g(\theta_0))\to N(0,g'_{\theta_0}\Sigma (g'_{\theta_0})^T),$$ where $g'_{\theta_0}$ is the jacobian of $g$ at the point $\theta_0$. So if you apply MLE or quasi MLE to your original model, then you are fine, i.e. you do not need to use Delta method. share|improve this answer answered Nov 13 '15 at 12:42 mpiktas 24.8k449104 add a comment| Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest
εμάς.Μάθετε περισσότερα Το κατάλαβαΟ λογαριασμός μουΑναζήτησηΧάρτεςYouTubePlayΕιδήσειςGmailDriveΗμερολόγιοGoogle+ΜετάφρασηΦωτογραφίεςΠερισσότεραΈγγραφαBloggerΕπαφέςHangoutsΑκόμη περισσότερα από την GoogleΕίσοδοςΚρυφά πεδίαΒιβλίαbooks.google.gr - This book introduces basic and advanced concepts of
Logistic Regression Does Not Converge
categorical regression with a focus on the structuring bayesglm r package constituents of regression, including regularization techniques to structure predictors. In addition to linear separation logistic regression standard methods such as the logit and probit model and extensions to multivariate settings,...https://books.google.gr/books/about/Regression_for_Categorical_Data.html?hl=el&id=hvxuqoxD00kC&utm_source=gb-gplus-shareRegression for Categorical DataΗ βιβλιοθήκη μουΒοήθειαΣύνθετη Αναζήτηση ΒιβλίωνΑγορά http://stats.stackexchange.com/questions/181601/standard-error-computation-in-a-non-linear-model eBook - 60,80 $Λήψη αυτού του βιβλίου σε έντυπη μορφήCambridge University PressΕλευθερουδάκηςΠαπασωτηρίουΕύρεση σε κάποια βιβλιοθήκηΌλοι οι πωλητές»Regression for Categorical DataGerhard TutzCambridge University Press, 21 Νοε 2011 - 561 σελίδες 0 Κριτικέςhttps://books.google.gr/books/about/Regression_for_Categorical_Data.html?hl=el&id=hvxuqoxD00kCThis book introduces basic and advanced concepts of categorical regression with a https://books.google.gr/books?id=hvxuqoxD00kC&pg=PA107&lpg=PA107&dq=quasi+standard+error&source=bl&ots=OYx_mgM8Am&sig=8IvrzS8f-ABV-BN_yJcAtgLJfyM&hl=en&sa=X&ved=0ahUKEwi64f7T2-nPAhWMFiwKHV6YD8YQ6AEIPjAE focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count d
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