We extend the model selection principle introduced by Birg茅 and Massart (2001) to logistic regression model. This selection is done by using penalized maximum likelihood criteria. We propose in this context a completely data-driven criteria based on the slope heuristics. We prove non asymptotic ...
2. Can different optimalities be attained simultaneously by a powerful learning procedure? In this talk, I will give a glimpse of some foundational theories on model selection for optimal regression learning. First, we will under...
(2009) A note on model selection in (time series) regression models - General-to-specific or specific-to-general ? Applied Economics Letters, forthcom- ing.Herwartz, H. 2007. A note on model selection in (time series) regression models-- general-to-specific or specific-to-general?
Model selection and estimation in regression with grouped variables[9]. Ming Yuan, Yi Lin.Journal of the Royal Statistical Society Series B, 2005. 2.10径向基网络(Radical Basis Function Network) 径向基函数(RBF)是多维空间插值的传统技术,取值仅仅依赖于离原点距离的实值函数,以网络的中心点为基准,根据...
Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods Using the relationships among ridge regression, LASSO estimation, and measurement error attenuation as motivation, a new measurement-error-model-based appr... LA Stefanski,Y Wu,K White - 《Journal of the...
Bayesian regressionModel selectionSymmetric unimodal errorWe discuss the problem of constructing a suitable regression model from a nonparametric Bayesian viewpoint. For this purpose, we consider the case when the error terms have symmetric and unimodal densities. By the Khintchine and Shepp theorem, ...
Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression. The envelope model allows efficient estimation in multivariate linear regression. In this paper, we propose the sparse envelope model, which is motivated b... Z.,ZHU,G.,... - 《Biometrika...
基于核回归的模型坍塌理论分析 (Theoretical Analysis of Model Collapse in Kernel Regression) 论文摘要 本文分析了合成数据时代模型崩溃现象,揭示了其作为由合成训练数据引起的典型比例定律的改变,并提出了通过适当的正则化和数据生成过程来理解和缓解模型崩溃影响的理论框架。
In theModelsgallery, clickAll Treesto try each of the nonoptimizable regression tree options and see which settings produce the best model with your data. Select the best model in theModelspane, and try to improve that model by using feature selection and changing some advanced options. ...
Univariate kernel density estimation Kernel Smoothing in Matlab Robust Regression and Quasi-likelihood Regression and Time Series Model Selection FRONT MATTER Regression and Time Series Model Selection The Vector Autoregressive Model Regression and Time Series Model Selectionback...