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Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
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
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 ...
Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
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...
A stochastic variational framework for fitting and diagnosing generalized linear mixed models
Hierarchical model Identify divergent units Large longitudinal data Non-conjugate model Stochastic approximation Variational Bayes
2012/9/17
Variational Bayes computational methods are attracting increasing in-terest because of their ability to scale to large data sets. Here, application of the
non-conjugate variational message passing (N...
Massive parallelization of serial inference algorithms for a complex generalized linear model
Massive parallelization serial inference algorithms generalized linear model
2012/9/17
Following a series of high-pro le drug safety disasters in recent years, many countries are redoubling their e orts to ensure the safety of licensed medical products. Large-scale observa-tional databa...
A Note on an R^2 Measure for Fixed Effects in the Generalized Linear Mixed Model
Goodness-of-fit Correlated data Model selection Multiple correlation Maximum likelihood Nonnormal distribution
2010/11/8
Using the LRT statistic, a model V# is proposed for the generalized linear mixed model for
assessing the association between the correlated outcomes and fixed effects. The V# compares
the full model...
Penalized maximum likelihood estimation for generalized linear point processes
Penalized maximum likelihood estimation generalized linear point processes
2010/3/11
A framework of generalized linear point process models (glppm) much akin to glm for regression is developed where the intensity depends upon a linear predictor process through a known function.In the ...
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
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
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
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...
A Default Conjugate Prior for Variance Components in Generalized Linear Mixed Models(Comment on Article by Browne and Draper)
Choice of prior hierarchical models noninformative priors random effects
2009/9/21
For a scalar random-eect variance, Browne and Draper (2005) have found that the uniform prior works well. It would be valuable to know more about the vector case, in which a second-stage prior on the ...
DE-BIASING WEIGHTED MLE VIA INDIRECT INFERENCE：THE CASE OF GENERALIZED LINEAR LATENT VARIABLE MODELS
factor analysis latent variables M-estimators
2009/2/25
In this paper we study bias-corrections to the weighted MLE (Dupuis and Morgen-
thaler, 2002), a robust estimator simply defined through a weighted score function.
Indeed, although the WMLE is relat...
Factorial experimental designs and generalized linear models
generalized linear model exponential family Fisher-Scoring algorithm factorial designs regular fraction
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...
Testing polynomial covariate effects in linear and generalized linear mixed models
Likelihood Ratio Test Restricted Maximum Likelihood （REML） Score Test
2009/2/11
An important feature of linear mixed models and generalized linear mixed models is that the conditional mean of the response given the random effects, after transformed by a link function, is linearly...
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Bayesian analysis Double exponential family Hierarchical priors Variance estimation
2010/4/30
Flexibly modeling the response variance in regression is important for efficient parameter
estimation, correct inference, and for understanding the sources of variability in
the response. Our articl...