搜索结果: 76-90 共查到“统计学 regression”相关记录243条 . 查询时间(0.203 秒)
Finite sample posterior concentration in high-dimensional regression
asymptotics Bayesian compressible prior high-dimensional posterior contraction regression shrinkage prior.
2012/9/19
We study the behavior of the posterior distribution in ultra high-dimensional Bayesian Gaussian linear regression models havingp佲n,withpthe number of predictors and nthe sample size. In particular, ou...
Adaptive confidence bands in the nonparametric fixed design regression model
Adaptive confidence bands nonparametric fixed design regression model
2012/9/19
In this note, we consider the problem of existence of adaptive confidence bands in the fixed design regression model, adapting ideas in Hoffmann and Nickl [10] to the present case. In the course of th...
A Robust, Fully Adaptive M-estimator for Pointwise Estimation in Heteroscedastic Regression
Adaptation Huber contrast Lepski’s method M-estimation minimax estimation nonparamet-ric regression pointwise estimation robust estimation.
2012/9/19
We introduce a robust and fully adaptive method for pointwise estimation in heteroscedastic regression. We allow for noise and design distributions that are unknown and fulfill very weak assumptions o...
Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression
Cluster-weighted modeling finite mixtures of regression EM-algorithm
2012/9/19
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
Spatially-adaptive sensing in nonparametric regression
Nonparametric regression, adaptive sensing sequential design active learning spatial adaptation spatially-inhomogeneous functions.
2012/9/18
While adaptive sensing has provided improved rates of convergence in sparse regression and classi cation, results in nonparametric regres-sion have so far been restricted to quite speci c classes of f...
Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Diverging number of parameters Feature selection
2011/7/19
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem...
A Dirty Model for Multiple Sparse Regression
Terms—Multi-task Learning High-dimensional Statistics
2011/7/7
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sp...
Distributional Results for Thresholding Estimators in High-Dimensional Gaussian Regression Models
Markov chain Monte Carlo Hamiltonian dynamics Bayesian analysis
2011/7/6
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n and may diverge with ...
Efficient Gaussian Process Regression for Large Data Sets
Bayesian Compressive Sensing Dimension Reduction
2011/7/6
Gaussian processes (GPs) are widely used in nonparametric regression, classification and spatio-temporal modeling, motivated in part by a rich literature on theoretical properties.
A pseudo-RIP for multivariate regression
Multivariate regression Restricted Isometry Property
2011/7/6
We give a suitable RI-Property under which recent results for trace regression translate into strong risk bounds for multivariate regression. This pseudo-RIP is compatible with the setting $n < p$.
Nonparametric Regression Estimation with Incomplete Data: Minimax Global Convergence Rates and Adaptivity
Adaptivity Besov spaces inhomogeneous data minimax estimation
2011/7/6
We consider the nonparametric regression estimation problem of recovering an unknown response function $f$ on the basis of incomplete data when the design points follow a known density $g$ with a fini...
Gaussian Process Regression with a Student-t Likelihood
Gaussian process robust regression Student-t likelihood
2011/7/6
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model. The challenge with the Student-t model is the analytically intractable i...
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...
Stochastic Search for Semiparametric Linear Regression Models
Stochastic Search Semiparametric Linear Regression Models
2011/7/6
This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar (1987).
The random design setting for linear regression concerns estimators based on a random sample of covariate/response pairs.