搜索结果: 16-27 共查到“统计学其他学科 regression”相关记录27条 . 查询时间(0.022 秒)
Dimensionally Constrained Symbolic Regression
Dimensionally Constrained Symbolic Regression
2011/7/6
We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can deri...
Quasi-estimation as a Basis for Two-stage Solving of Regression Problem
Gauss-Markov’s scheme least squares method quasi-estimator
2010/10/19
An effective two-stage method for an estimation of parameters of the linear regression is considered. For this purpose we introduce a certain quasi-estimator that, in contrast to usual estimator, pro...
Variable importance in binary regression trees and forests
CART random forests maximal subtree
2009/9/16
We characterize and study variable importance (VIMP) and pairwise variable associations in binary regression trees. A key component involves the node mean squared error for a quantity we refer to as a...
We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron, Hastie, Johnstone & Tibshirani (2004) it is proved that the l...
Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression
Gaussian regression heteroscedasticity model selection Kullback risk convergence rate
2009/9/16
Let $Y$ be a Gaussian vector of $R^{n}$ of mean $s$ and diagonal covariance matrix $Gamma$. Our aim is to estimate both $s$ and the entries $sigma_{i}=Gamma_{i,i}$, for $i=1,dots,n$, on the basis of t...
Selection of variables and dimension reduction in high-dimensional non-parametric regression
dimension reduction high dimension LASSO
2009/9/16
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...
Honest variable selection in linear and logistic regression models via $ell_1$ and $ell_1 + ell_2$ penalization
penalty sparse consistent variable selection regression generalized linear models logistic regression
2009/9/16
This paper investigates correct variable selection in finite samples via $ell_1$ and $ell_1 + ell_2$ type penalization schemes. The asymptotic consistency of variable selection immediately follows fro...
Theoretical properties of Cook's PFC dimension reduction algorithm for linear regression
Principal Components Principal Fitted Components random matrix theory regression
2009/9/16
We analyse the properties of the Principal Fitted Components (PFC) algorithm proposed by Cook. We derive theoretical properties of the resulting estimators, including sufficient conditions under which...
Empirical likelihood based testing for regression
Marked empirical process Model check for regression Nonlinear regression Partial linear model Residuals
2009/9/16
Consider a random vector $(X,Y)$ and let $m(x)=E(Y|X=x)$. We are interested in testing $H_0 : m in {cal M}_{Theta,{cal G}} = {gamma(cdot,theta,g) : theta in Theta, g in {cal G}}$ for some known functi...
Comparing two samples by penalized logistic regression
Empirical likelihood biased sampling penalty semiparametric shrinkage mean square error power
2009/9/16
Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log--likelihood function of two unknown densities is of so...
Uniform limit laws of the logarithm for estimators of the additive regression function in the presence of right censored data
nonparametric estimation additive regression function censored data uniform laws of the logarithm
2009/9/16
It has been recently shown that nonparametric estimators of the additive regression function could be obtained in the presence of right censoring by coupling the marginal integration method with initi...
Penalized orthogonal-components regression for large p small n data
Empirical Bayes thresholding Latent-variable model Sparse predictors Supervised dimension reduction
2009/9/16
Here we propose a penalized orthogonal-components regression (POCRE) for large $p$ small $n$ data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlati...