Robit regression in Stata

IF 3.2 2区 数学 Q1 SOCIAL SCIENCES, MATHEMATICAL METHODS
Roger B. Newson, Milena Falcaro
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引用次数: 3

Abstract

Logistic and probit models are the most popular regression models for binary outcomes. A simple robust alternative is the robit model, which replaces the underlying normal distribution in the probit model with a Student’s t distribution. The heavier tails of the t distribution (compared with the normal distribution) mean that model outliers are less influential. Robit regression models can be fit as generalized linear models with the link function defined as the inverse cumulative t distribution function with a specified number of degrees of freedom; they have been advocated as being particularly suitable for estimating inverse-probability weights and propensity scoring more generally. Here we describe a new command, robit, that implements robit regression in Stata.
Robit回归状态
Logistic模型和probit模型是最常用的二元结果回归模型。一个简单的鲁棒替代方案是robit模型,它将probit模型中的底层正态分布替换为Student 's t分布。t分布尾部较重(与正态分布相比)意味着模型异常值的影响较小。Robit回归模型可以拟合为广义线性模型,其中链接函数定义为具有一定自由度的逆累积t分布函数;它们被认为特别适用于估计反概率权重和倾向评分。这里我们描述一个新的命令,robit,它在Stata中实现了robit回归。
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来源期刊
Stata Journal
Stata Journal 数学-统计学与概率论
CiteScore
7.80
自引率
4.20%
发文量
44
审稿时长
>12 weeks
期刊介绍: The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata''s language. The Stata Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.
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