多项式概率
多项式logistic回归
计量经济学
有序概率单位
估计员
独立性
统计
独立性(概率论)
逻辑回归
罗伊特
多项式分布
数学
普罗比特
变量(数学)
变量
帕累托原理
数学分析
作者
Changsoo Kwak,Alan Clayton-Matthews
出处
期刊:Nursing Research
[Lippincott Williams & Wilkins]
日期:2002-11-01
卷期号:51 (6): 404-410
被引量:244
标识
DOI:10.1097/00006199-200211000-00009
摘要
When the dependent variable consists of several categories that are not ordinal (i.e., they have no natural ordering), the ordinary least square estimator cannot be used. Instead, a maximum likelihood estimator like multinomial logit or probit should be used.The purpose of this article is to understand the multinomial logit model (MLM) that uses maximum likelihood estimator and its application in nursing research.The research on "Racial differences in use of long-term care received by the elderly" (Kwak, 2001) is used to illustrate the multinomial logit model approach. This method assumes that the data satisfy a critical assumption called the "independence of irrelevant alternatives." A diagnostic developed by Hausman is used to test the independence of irrelevant alternatives assumption. Models in which the dependent variable consists of several unordered categories can be estimated with the multinomial logit model, and these models can be easily interpreted.This method can handle situations with several categories. There is no need to limit the analysis to pairs of categories, or to collapse the categories into two mutually exclusive groups so that the (more familiar) logit model can be used. Indeed, any strategy that eliminates observations or combines categories only leads to less efficient estimates.
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