Driven by technological developments, most approaches have been designed for high-dimensional problems with metric variables, whereas categorical data has largely been neglected. In recent years, however, it has become ...
Regression for Categorical Data 作者: Tutz, Gerhard 出版年: 2012-6定价: $ 141.25ISBN: 9780511842061豆瓣评分 目前无人评价 评价: 写笔记 写书评 加入购书单 分享到 推荐 我来说两句 短评 ··· 热门 / 最新 / 好友 还没人写过短评呢 我要写书评 Regression for Categorical Data的书评 ··· (...
Regression for Categorical Data 来自 Semantic Scholar 喜欢 0 阅读量: 58 作者: G Tutz 摘要: Publisher's description: This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure ...
Regression Models for Count Data As was noted on page 25, the problem is that least squares is based on a "signal-plusnoise" model using an underlying Gaussian distribution, when the correct analysis uses distributions appropriate for categorical data, such as those ... JS Simonoff - Springer...
However, in many cases these kinds of data, such as number of young... TL Mcdonald,GC White - 《Journal of Wildlife Management》 被引量: 80发表: 2010年 Conceptual and Visual Models for Categorical Data A dynamic conceptual model for categorical data is described that likens observations to ...
The density estimation results for categorical data are also related to de-noising results in randomized response methods12. The work on continuous domains is often done by binning or quantizing the domain. However, finding the optimal number of bins or quantization resolution depending on privacy ...
Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J. Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data. Although regression models for categorical dependent variables are common...
Structured additive regression for overdispersed and zero-inflated count data In count data regression there can be several problems that prevent the use of the standard Poisson log-linear model: overdispersion, caused by unobserved ... L Fahrmeir,Leyre Osuna Echavarria - 《Applied Stochastic Models...
Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and...
All of these conclusions come from a model that treats all of the data as coming from a rather monolithic whole. We can use other types of data in our modeling to try to get different insights. Next unit: Categorical data PreviousNext ...