Comparisons of Tobit, Linear, and Poisson-Gamma Regression Models: An Application of Time Use Data
Comparisons of Tobit, Linear, and Poisson-Gamma Regression Models: An Application of Time Use Data
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DOI：
10.1177/0049124111415370
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发表时间：
2011-08-01
影响因子：
6.3
通讯作者：
Dunn, Peter K.
中科院分区：
文献类型：
--
作者：
Brown, Judith E.;Dunn, Peter K.
时间使用数据（TUD）是独特的，在本质上是偶发的，包括连续和离散（精确零）值。TUD是非负的，通常是右偏的。为了分析这些数据，经常使用Tobit模型，在较小程度上使用线性回归模型。Tobit模型假设零代表理论上包含负值的潜在正态分布潜变量的删失值。线性回归和Tobit模型都将正态性作为关键假设。泊松-伽马分布是一个点质量为零（对应于给定活动花费的零时间）和连续分量的分布。使用广义线性模型，TUD可以利用Poisson-gamma分布建模。使用TUD，Tobit和线性回归模型进行比较，泊松-伽马方面的解释模型，模型拟合（残差分析），并通过使用模拟数据实验模型的性能。泊松-γ被发现在许多情况下在理论上和经验上更合理。
Time use data (TUD) are distinctive, being episodic in nature and consisting of both continuous and discrete (exact zeros) values. TUD is non-negative and generally right skewed. To analyze such data, the Tobit, and to a lesser extent, linear regression models are often used. Tobit models assume the zeros represent censored values of an underlying normally distributed latent variable that theoretically includes negative values. Both the linear regression and Tobit models have normality as a key assumption. The Poisson-gamma distribution is a distribution with both a point mass at zero (corresponding to zero time spent on a given activity) and a continuous component. Using generalized linear models, TUD can be modeled utilizing the Poisson-gamma distribution. Using TUD, Tobit and linear regression models are compared to the Poisson-gamma with respect to the interpretation of the model, the model fit (analysis of residuals), and model performance through the use of a simulated data experiment. The Poisson-gamma is found to be theoretically and empirically more sound in many circumstances.