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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.
Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
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2016/1/26
We consider testing regression coefficients in high dimensional generalized linear mod-els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ...
Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
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2016/1/20
We consider testing regression coefficients in high dimensional generalized linear mod-els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ...
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...
Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
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2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
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 ...
An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors
ordered explanatory variable constrained estimation least squares logistic regression Coxregression active set algorithm
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2010/3/18
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariate...
Sure Independence Screening in Generalized Linear Models with NP-Dimensionality
generalized linear models independent learning sure indepen-dent screening variable selection
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2010/3/19
Ultrahigh dimensional variable selection plays an increasingly
important role in contemporary scientific discoveries and statisti-
cal research. Among others, Fan and Lv (2008) propose an indepen-
...
Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors
Bayes factor Conditional Predictive Ordinate Conjugate prior Poisson regression Logistic regression
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2009/9/22
In this paper, we consider theoretical and computational connections
between six popular methods for variable subset selection in generalized linear
models (GLMs) Under the conjugate priors develope...
Factorial experimental designs and generalized linear models
generalized linear model exponential family Fisher-Scoring algorithm factorial designs regular fraction
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2009/2/23
This paper deals with experimental designs adapted to a generalized linear model. We introduce a special link function for which the orthogonality of design matrix obtained under Gaussian assumption i...
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...