They begin with a brief general discussion of nonlinear and generalized linear models, then discuss one very specific and popular case of a nonlinear model, that of logistic regression. To understand logistic regression, one first requires a familiarity with exponential and logarithmic functions. These...
generalized linear modelJeffreys priorlogistic regressionmultinomial outcomeminimally informative priornonlinear regressionIn Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves ...
tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models... Tung Ho Lam si,A Cécile - 《Systematic Biology》 被引量: 276发表: 2014年 On Bayesian Analysis of Generalized Linear Models Using Jeffr...
We introduce an estimate of the distribution of the deviance residuals of generalized linear models. We propose a new QQ plot where the observed deviance residuals are plotted against the quantiles of the estimated distribution. The method is illustrated by the analysis of real and simulated data....
指数族分布是广义线性模型(generalized linear models)的核心,参考本书9.3. 指数族分布也是变分推理(variational inference)的核心,参考本书21.2. 9.2.1 定义 概率密度函数(pdf)或者概率质量函数(pmf)$p(x|\theta)$,对$x=(x_1,...,x_m)\in X^m, \theta\in \Theta \subseteq R^d$,如果满...
McCullagh and Nelder 在他们的《Generalized linear model》一书中提到:在一开始,人们似乎认为好的model就是能很好的fit data的model,然后他们发现参数的数量可能会导致fit的程度不同,所以这不是一个好的度量方式。这是对的,因为过多的参数可能会引起overfitting,其准确性是biased。但是可以有方法去掉这部分bias,为了...
proceeds to examine in greater detail generalized linear models for count data,including contingency tables; briefly sketches the statistical theory underlying generalized linearmodels; and concludes with the extension ofregression diagnostics to generalized linear models.The unstarred sections ofthis chapter ...
Efficiency and Validity Analyses of Two-Stage Estimation Procedures and Derived Testing Procedures in Quantitative Linear Models with AR(1) Errors In a quantitative linear model with errors following a stationary Gaussian, first-order autoregressive or AR(1) process, Generalized Least Squares (GLS) on...
For both generalized linear models and GLMMs, it is important to understand that the most typical link functions (e.g., the logit for binomial data, the log for Poisson data) are not guaranteed to be a good representation of the relationship of the predictors with the outcomes. ...
My talk will start with connections between sure independence screening and t-test for high-dimensional two-sample mean problem with false discovery rate control. I then present an overview on marginal screening procedures for linear models and generalized linear models along with their theoretical prop...