Selection models under generalized symmetry settings
نویسندگان
چکیده
منابع مشابه
Generalized Adjustment Under Confounding and Selection Biases
Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditi...
متن کاملSemiparametric Estimation of Heteroscedastic Binary Choice Sample Selection Models under Symmetry
Binary choice sample selection models are widely used in applied economics with large crosssectional data where heteroscedaticity is typically a serious concern. Existing parametric and semiparametric estimators for the binary selection equation and the outcome equation in such models su®er from serious drawbacks in the presence of heteroscedasticity of unknown form in the latent errors. In thi...
متن کاملBootstrap Model Selection in Generalized Linear Models
Model selection is a central component of data analysis Though there are a variety of methods for likelihood based estimation methods there are relatively few for non likelihood based generalized linear models GLM such as in the quasi likelihood and generalized es timating equation GEE approaches In this paper we develop basic and bias corrected bootstrap approaches to estimate the predictive m...
متن کاملRobust Model Selection in Generalized Linear Models
In this paper, we extend to generalized linear models (including logistic and other binary regression models, Poisson regression and gamma regression models) the robust model selection methodology developed by Müller and Welsh (2005) for linear regression models. As in Müller and Welsh (2005), we combine a robust penalized measure of fit to the sample with a robust measure of out of sample pred...
متن کاملVariable Selection in Generalized Functional Linear Models.
Modern research data, where a large number of functional predictors is collected on few subjects are becoming increasingly common. In this paper we propose a variable selection technique, when the predictors are functional and the response is scalar. Our approach is based on adopting a generalized functional linear model framework and using a penalized likelihood method that simultaneously cont...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annals of the Institute of Statistical Mathematics
سال: 2011
ISSN: 0020-3157,1572-9052
DOI: 10.1007/s10463-011-0328-7