A Universal Selection Method in Linear Regression Models
نویسندگان
چکیده
منابع مشابه
A Universal Selection Method in Linear Regression Models
In this paper we consider a linear regression model with fixed design. A new rule for the selection of a relevant submodel is introduced on the basis of parameter tests. One particular feature of the rule is that subjective grading of the model complexity can be incorporated. We provide bounds for the mis-selection error. Simulations show that by using the proposed selection rule, the mis-selec...
متن کاملRobust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملA Stepwise Regression Method and Consistent Model Selection for High-dimensional Sparse Linear Models
We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p À n, the sample size) sequentially so that the selected variable at each step minimizes the residual sum squares. We derive the convergence rate of OGA and develop a consistent model selection procedure along the OGA ...
متن کاملAlternative Strategies for Variable Selection in Linear Regression Models
1. INTRODUCTION 1.1.1. Variable Selection for Incomplete Data sets In statistical practice, many real-life data sets are incomplete for reasons like non-responses or drop-outs. When a data set is incomplete, practitioners frequently resort to a " case-deletion " strategy within which the incomplete cases are excluded from analysis and the complete cases are formed into a reduced rectangular com...
متن کاملSelection Model in Functional Linear Regression Models for Scalar Response
The so-called Functional Linear Regression model consists in explaining a scalar response by a regressor which is a random function observed on a compact subset of R: in this context, the “parameter” of linear model is a function of the weights. In order to estimate this functional coefficient some estimators such as Functional Principal Component Regression Estimator, Smooth Principal Componen...
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: Open Journal of Statistics
سال: 2012
ISSN: 2161-718X,2161-7198
DOI: 10.4236/ojs.2012.22017