Model Selection and Minimax Estimation in Generalized Linear Models
نویسندگان
چکیده
منابع مشابه
Bootstrap 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 کاملMaximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...
متن کاملEstimation and Variable Selection for Generalized Additive Partial Linear Models.
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus ...
متن کاملModel selection and parameter estimation in non-linear nested models: a sequential generalized DKL-optimum design
This work proposes a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate efficiently the parameters of the chosen model. At the first step of this procedure, a generalized DKL-optimum design is computed, which is optimal for the double goal of model selection and parameter estimation. Subsequently, at each following step, an adapt...
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: IEEE Transactions on Information Theory
سال: 2016
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2016.2555812