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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
2023/5/9
Variable selection is often needed in many fields and has been discussed by many authors in various situations.
This is an advanced course. Participants should have seen at least one semester of statistics at university or college. Additionally, we will use the statistical package R for most of the practicals, ...
Asymptotic equivalence for nonparametric generalized linear models
Nonparametric regression Statistical experiment De® - ciency distance Global white noise approximation Exponential family Variance stabilizing transformation
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
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.
Fitting Longitudinal Data with Hierarchical Generalized Linear Models
H-likelihood L-N estimators Poisson-Gamma models
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
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 ...
Estimation in high-dimensional linear models with deterministic design matrices
Identifiability projection ridge regression sparsity thresholding variable selection
2012/6/21
Because of the advance in technologies, modern statistical studies often encounter linear models with the number of explanatory variables much larger than the sample size. Estimation and variable sele...
An Adaptive Semiparametric Estimation for Partially Linear Models
Partially linear model adaptability adjustment
2011/11/11
In this paper, we propose an adaptive semiparametric estimation for the nonparametric component of partially linear models. The new estimator is better than the usual nonparametric method in the sense...
Detecting changes in functional linear models
functional data projections weak dependence change point weak convergence
2011/6/15
We observe two sequences of curve which are connected via an integral operator. Our model includes linear models as well as autoregressive models in Hilbert spaces. We wish to test the null hypothesis...
On universal oracle inequalities related to high-dimensional linear models
On universal oracle inequalities high-dimensional linear models
2010/11/17
This paper deals with recovering an unknown vector $\theta$ from the noisy data $Y=A\theta+\sigma\xi$, where $A$ is a known $(m\times n)$-matrix and $\xi$ is a white Gaussian noise. It is assumed tha...
A local stochastic Lipschitz condition with application to Lasso for high dimensional generalized linear models
Lasso sparsity measure concentration generalized linear models
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
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...
Comparison of MINQUE and Simple Estimate of the Error Variance in the General Linear Models
general linear model MINQUE mean square error
2007/12/11
Comparison is made between the MINQUE and simple estimate of the error variance in the normal linear model under the mean square errors criterion, where the model matrix need not have full rank and th...
For a general linear model, spherical distributions are often considered when errors do not have normal distribution. Several authors[1-3] studied the least squares and James-Stein estimations for a l...