help 哑元变量 负二项回归模型拟合优度检验

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

<P>请教：</P>
<P> 1、   由于处理数据用到stata，但是对与poisson回归中的哑元变量不太了解其作用，望高手指点。</P>
<P>2、poisson回归的拟合优度检验命令poisgof，但是负二项回归的拟合优度检验命令是什么？</P>
<P>请指教。。。</P>

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：拟合优度检验 负二项回归 模型拟合 回归模型 Help 模型

没有人帮忙，呜呜

这个论坛里可以下载：<现代医学统计方法与Stata应用>,不过poisson回归介绍得不是很多.再找找SAS的书看看吧，SAS里也可以做这个回归

https://bbs.pinggu.org/thread-76335-1-1.html&page=2

Regression Models For Categorical Dependent Variables USING STATA

应该讲吧

Regression Models for Categorical and Limited Dependent Variables
  
J. Scott Long  Indiana University, Bloomington  
   
&copy; 1997 328 pages  

SAGE Publications, Inc
Series: Advanced Quantitative Techniques in the Social Sciences Volume 7     
   
Hardcover ISBN: 9780803973749 $106.00   
Description:
THE APPROACH

"J. Scott Long’s approach is one that I highly commend. There is a decided emphasis on the application and interpretation of the specific statistical techniques. Long works from the premise that the major difficulty with the analysis of limited and categorical dependent variables (LCDVs) is the complexity of interpreting nonlinear models, and he provides tools for interpretation that can be widely applied across the different techniques."

--Robert L. Kaufman, Sociology, Ohio State University

"A thorough and comprehensive introduction to analyzing categorical and limited dependent variables from a traditional regression perspective that provides unusually clear discussions concerning estimation, identification, and the multiplicity of models available to the researcher to analyze such data."

--Scott Hershberger, Psychology, University of Kansas

THE ORGANIZATION

"The thing that impresses me the most about this book is how organized it is. The chapters are in excellent logical sequence. There is a useful repetition of important concepts (e.g., estimation, hypothesis testing) from chapter to chapter. J. Scott Long has done a terrific job of organizing like things from disparate literatures, such as the scaler measures of fit in Chapter 4."

--Herbert L. Smith, Sociology, University of Pennsylvania

"A major strength of the book is the way that it is organized. The chapter about each technique is written in a highly organized and parallel format. First the statistical basis and assumptions for the particular model are developed, then estimation issues are considered, then issues of testing and interpretation are considered, then variations and extensions are explored."

--Robert L. Kaufman, Sociology, Ohio State University

FOR THE COURSE

"I have been teaching a course on categorical data analysis to sociology graduate students for close to 20 years, but I have never found a book with which I was happy. J. Scott Long’s book, on the other hand, is nearly ideal for my objectives and preferences, and I expect that many other social scientists will feel the same way. I will definitely adopt it the next time I teach the course. It deals with the right topics in the most desirable sequence and it is clearly written."

--Paul D. Allison, Sociology, University of Pennsylvania

Class-tested at two major universities and written by an award-winning teacher, J. Scott Long’s book gives readers unified treatment of the most useful models for categorical and limited dependent variables (CLDVs). Throughout the book, the links among models are made explicit, and common methods of derivation, interpretation, and testing are applied. In addition, Long explains how models relate to linear regression models whenever possible. In order for the reader to see how these models can be applied, Long illustrates each model with data from a variety of applications, ranging from attitudes toward working mothers to scientific productivity.

The book begins with a review of the linear regression model and an introduction to maximum likelihood estimation. It then covers the logit and probit models for binary outcomes--providing details on each of the ways in which these models can be interpreted, reviews standard statistical tests associated with maximum likelihood estimation, and considers a variety of measures for assessing the fit of a model. Long extends the binary logit and probit models to ordered outcomes, presents the multinomial and conditioned logit models for nominal outcomes, and considers models with censored and truncated dependent variables with a focus on the tobit model. He also describes models for sample selection bias and presents models for count outcomes by beginning with the Poisson regression model and showing how this model leads to the negative binomial model and zero inflated count models. He concludes by comparing and contrasting the models from earlier chapters and discussing the links between these models and models not discussed in the book, such as loglinear and event history models. Helpful exercises are included in the book with brief answers included in the appendix so that readers can practice the techniques as they read about them.