搜索结果: 121-134 共查到“统计学 selection”相关记录134条 . 查询时间(0.047 秒)
The sparsity and bias of the Lasso selection in high-dimensional linear regression
Penalized regression high-dimensional data variable selection bias rate consistency spectral analysis random matrices
2010/4/30
Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436–1462]
showed that, for neighborhood selection in Gaussian graphical models,
under a neighborhood stability condition, the LASSO is consistent,
...
Fence methods for mixed model selection
Adaptive fence consistency F-B fence finite sample performance GLMM linear mixed model model selection
2010/4/30
Many model search strategies involve trading off model fit with
model complexity in a penalized goodness of fit measure. Asymptotic
properties for these types of procedures in settings like linear
...
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Bayesian analysis Double exponential family Hierarchical priors Variance estimation
2010/4/30
Flexibly modeling the response variance in regression is important for efficient parameter
estimation, correct inference, and for understanding the sources of variability in
the response. Our articl...
Variable Selection Incorporating Prior Constraint Information into Lasso
lasso linear models prior constraint information sample information variableselection
2010/4/29
We propose the variable selection procedure incorporating prior constraint information into
lasso. The proposed procedure combines the sample and prior information, and selects significant variables ...
Can One Estimate The Unconditional Distribution of Post-Model-Selection Estimators?
Inference after model selection Post-model-selection estimator Pre-test estimator Selection of regressors
2010/4/28
We consider the problem of estimating the unconditional distribution of a post-model-selection estimator.The notion of a post-model-selection estimator here refers to the combined procedure resulting ...
“Pre-conditioning” for feature selection and regression in high-dimensional problems
Pre-conditioning feature selection regression high-dimensional problems
2010/4/27
The primary method used for this initial regression is supervised principal components. Then we
apply a standard procedure such as forward stepwise selection or the
LASSO to the pre-conditioned resp...
The Loss Rank Principle for Model Selection
Model selection loss rank principle non-parametric regression classification general loss function k nearest neighbors
2010/4/27
We introduce a new principle for model selection in regression and classification.
Many regression models are controlled by some smoothness or flexibility
or complexity parameter c, e.g. the number ...
Best subset selection，persistence in high-dimensional statistical learning and optimization under L1 constraint
Variable selection persistence
2010/4/26
Let (Y,X1, . . . ,Xm) be a random vector. It is desired to predict Y
based on (X1, . . . ,Xm). Examples of prediction methods are regression,
classification using logistic regression or separating h...
Can one estimate the conditional distribution of post-model-selection estimators?
Inference after model selection post-model-selection estimator pre-test estimator selection of regressors
2010/4/27
We consider the problem of estimating the conditional distribution
of a post-model-selection estimator where the conditioning is on
the selected model. The notion of a post-model-selection estimator...
Component selection and smoothing in multivariate nonparametric regression
Smoothing spline ANOVA method of regularization nonparametricregression nonparametric classification model selection
2010/4/26
We propose a new method for model selection and model fitting
in multivariate nonparametric regression models, in the framework
of smoothing spline ANOVA. The “COSSO” is a method of
regularization ...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
Method Feature Selection Application Naive Bayes Classification Models
2010/4/26
For many classification and regression problems, a large number of features are available
for possible use — this is typical of DNA microarray data on gene expression, for example. Often,for computat...
An improved method for model selection based on Information Criteria
improved method model selection Information Criteria
2010/4/26
Information criteria are an appropriate and widely
used tool for solving model selection problems. However, different
ways to use them exist, each leading to a more or less precise
approximation of...
Model selection by resampling penalization。
Bootstrap techniques (also called resampling computation techniques) have introduced
new advances in modeling and model evaluation [10]. Using resampling
methods to construct a series of new samples...