Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives
暫譯: 有序迴歸模型：平行、部分及非平行替代方案
Fullerton, Andrew, Xu, June
- 出版商: CRC
- 出版日期: 2020-12-18
- 售價: $2,450
- 貴賓價: 9.5 折 $2,328
- 語言: 英文
- 頁數: 188
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367737213
- ISBN-13: 9780367737214
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相關主題
商品描述
Estimate and Interpret Results from Ordered Regression Models
Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption.
The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R.
This book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable.
Web Resource
More detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.
商品描述(中文翻譯)
估計與解釋有序迴歸模型的結果
有序迴歸模型：平行、部分與非平行替代方案介紹了針對有序結果的迴歸模型，這些結果是具有有序類別但類別之間間距未知的變數。本書全面涵蓋了三大類有序迴歸模型（累積模型、階段模型和相鄰模型），以及基於平行迴歸假設應用的變體。
作者首先介紹了三種「平行」有序迴歸模型，然後再討論不受限的部分模型、受限的部分模型和非平行模型。接著，他們回顧了現有的平行迴歸假設檢驗，提出幾種新變體的檢驗方法，並討論與平行迴歸假設檢驗相關的重要實務問題。本書還描述了有序迴歸模型的擴展，包括異質選擇模型、多層次有序模型，以及有序迴歸模型的貝葉斯方法。一些章節包含使用 Stata 和 R 的簡短範例。
本書提供了一個理解有序迴歸模型的概念框架，基於感興趣的機率和平行迴歸假設的應用。它展示了多種建模替代方案的實用性，並指導您如何根據有序結果的類型和每個變數的平行假設的限制性來選擇最合適的模型。
網路資源
更詳細的範例可在補充網站上獲得。該網站還包含 JAGS、R 和 Stata 代碼以估計模型，以及重現結果的語法。
作者簡介
Andrew S. Fullerton is an associate professor of sociology at Oklahoma State University. His primary research interests include work and occupations, social stratification, and quantitative methods. His work has been published in journals such as Social Forces, Social Problems, Sociological Methods & Research, Public Opinion Quarterly, and Social Science Research.
Jun Xu is an associate professor of sociology at Ball State University. His primary research interests include Asia and Asian Americans, social epidemiology, and statistical modeling and programing. His work has been published in journals such as Social Forces, Social Science & Medicine, Sociological Methods & Research, Social Science Research, and The Stata Journal.
作者簡介(中文翻譯)
Andrew S. Fullerton 是俄克拉荷馬州立大學的社會學副教授。他的主要研究興趣包括工作與職業、社會階層以及定量方法。他的研究成果已發表於《Social Forces》、《Social Problems》、《Sociological Methods & Research》、《Public Opinion Quarterly》及《Social Science Research》等期刊。
Jun Xu 是巴爾州立大學的社會學副教授。他的主要研究興趣包括亞洲及亞裔美國人、社會流行病學，以及統計建模與程式設計。他的研究成果已發表於《Social Forces》、《Social Science & Medicine》、《Sociological Methods & Research》、《Social Science Research》及《The Stata Journal》等期刊。
