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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars:Best subset selection via distance covariance
距离协方差 最佳子集 回归分析
2023/4/14
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars:Variable selection for generalized linear models with interval-censored failure time data
区间删失 失效时间数据 广义线性模型 变量选择
2023/5/9
Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression Selection Consistency
2016/1/20
The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
BROADBAND SENSOR LOCATION SELECTION USING CONVEX OPTIMIZATION IN VERY LARGE SCALE ARRAYS
array processing multi-frequency beam pattern design sensor location selection very large scale arrays convex optimization simulated annealing
2015/9/29
Consider a sensing system using a large number of N microphones placed in multiple dimensions to monitor a broadband acoustic field. Using all the microphones at once is impractical because of the amo...
Higher Criticism Thresholding: Optimal Feature Selection when Useful Features are Rare and Weak
Criticism Thresholding Feature Selection
2015/8/21
Linear classication analysis is a fundamental tool for science
and technology. In important application elds today { genomics and proteomics are examples { one automatically obtains very high-dimen...
Regularization and variable selection via the elastic net
Grouping effect LARS algorithm Lasso Penalization p>n problem Variable selection
2015/8/21
We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar s...
“PRECONDITIONING” FOR FEATURE SELECTION AND REGRESSION IN HIGH-DIMENSIONAL PROBLEMS
Model selection prediction error lasso
2015/8/21
We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function, yiel...
This paper explores a class of empirical Bayes methods for levedependent threshold selection in wavelet shrinkage. The prior considered
for each wavelet coefficient is a mixture of an atom of p...
Selection and Estimation for Large-Scale Simultaneous Inference
Large-Scale Simultaneous Inference Selection
2015/8/20
Modern scientific technology is providing a new class of simultaneous inference
problems for the applied statistician, where there are hundreds or thousands or even
more hypothesis tests to co...
Estimation and Accuracy after Model Selection
model averaging Cp, Lasso bagging bootstrap smoothing
2015/8/20
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider
bootstrap methods for computing standard errors and condence intervals that take model selecti...
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider
bootstrap methods for computing standard errors and condence intervals that take model selecti...
We suppose that the statistician observes some large number of estimates zi
, each with
its own unobserved expectation parameter i
. The largest few of the zi
's are likely to
substantially over...
A dinner table seats k guests and holds n discrete morsels of food. Guests select morsels in turn until all are consumed. Each guest has a ranking of the morsels according to how much he would enjoy e...
Metric selection in Douglas-Rachford splitting and ADMM
Divided multiplier literature linear convergence and linear convergence parameter optimization algorithm
2015/8/7
Recently, several convergence rate results for Douglas-Rachford splitting and the alternating direction method of multipliers (ADMM) have been presented in the literature. In this paper, we show linea...
Near-ideal model selection by `1 minimization
Model selection oracle inequalities the lasso compressed sensing incoherence eigenvalues of random matrices
2015/6/17
We consider the fundamental problem of estimating the mean of a vector y = Xβ + z, where X is an n× p design matrix in which one can have far more variables than observations and z is a stochastic err...