Wurm Michael J, Rathouz Paul J
Department of Statistics, University of Wisconsin-Madison, 11300 University Avenue, Madison, WI 53706, USA.
Department of Biostatistics & Medical Informatics, University of Wisconsin School of Medicine and Public Health, K6/446 CSC, Box 4675, 600 Highland Avenue, Madison, WI 53792-4675, USA.
R J. 2018 Jul;10(1):288-307.
This paper introduces a new algorithm to estimate and perform inferences on a recently proposed and developed semiparametric generalized linear model (glm). Rather than selecting a particular parametric exponential family model, such as the Poisson distribution, this semiparametric glm assumes that the response is drawn from the more general exponential tilt family. The regression coefficients and unspecified reference distribution are estimated by maximizing a semiparametric likelihood. The new algorithm incorporates several computational stability and efficiency improvements over the algorithm originally proposed. In particular, the new algorithm performs well for either small or large support for the nonparametric response distribution. The algorithm is implemented in a new R package called .
本文介绍了一种新算法，用于对最近提出并开发的半参数广义线性模型（glm）进行估计和推断。这种半参数广义线性模型并非选择特定的参数指数族模型（如泊松分布），而是假设响应来自更一般的指数倾斜族。通过最大化半参数似然来估计回归系数和未指定的参考分布。与最初提出的算法相比，新算法在计算稳定性和效率方面有多项改进。特别是，对于非参数响应分布的小支持或大支持，新算法都表现良好。该算法在一个名为 的新R包中实现。
