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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
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2023/5/9
Variable selection is often needed in many fields and has been discussed by many authors in various situations.
Asymptotic equivalence for nonparametric generalized linear models
Nonparametric regression Statistical experiment De® - ciency distance Global white noise approximation Exponential family Variance stabilizing transformation
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2015/8/25
We establish that a non-Gaussian nonparametric regression model is asymptotically equivalent to a regression model with Gaussian noise. The approximation is in the sense of Le Cam's de®- ciency d...
L1-regularization path algorithm for generalized linear models
Generalized linear model Lasso Path algorithm Predictor–corrector method Regularization Variable selection
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2015/8/21
We introduce a path following algorithm for L1-regularized generalized linear models. The L 1-regularization procedure is useful especially because it, in effect, selects variables according to the am...
ON HIERARCHICAL GENERALIZED LINEAR MODELS
HIERARCHICAL GENERALIZED LINEAR MODELS
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2015/3/20
ON HIERARCHICAL GENERALIZED LINEAR MODELS.
Fitting Longitudinal Data with Hierarchical Generalized Linear Models
H-likelihood L-N estimators Poisson-Gamma models
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2015/3/18
We propose a class of hierarchical generalized linear models (HGLMs) with random dispersions in this paper, and focus on the properties of the L-N estimators for the fixed
effect β in the extended Po...
Bayesian variable selection for spatially dependent generalized linear models
generalized linear models variable selection Bayesian spatially
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2012/11/22
Despite the abundance of methods for variable selection and accommodating spatial structure in regression models, there is little precedent for incorporating spatial dependence in covariate inclusion ...
A local stochastic Lipschitz condition with application to Lasso for high dimensional generalized linear models
Lasso sparsity measure concentration generalized linear models
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2010/11/30
For regularized estimation, the upper tail behavior of the random Lipschitz coefficient asso-
ciated with empirical loss functions is known to play an important role in the error bound of
Lasso for ...
Asymptotic Properties of the Maximum Likelihood Estimate in Generalized Linear Models with Stochastic Regressors
Generalized linear models Consistency Asymptotic normality
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2007/12/12
For generalized linear models (GLM), in case the regressors are stochastic and have different distributions, the asymptotic properties of the maximum likelihood estimate (MLE) $\hat{\beta}_n$ of the p...