An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 1900-01-01 DOI:10.2202/1557-4679.1032

Xiaogang Su, Chih-Ling Tsai

引用次数: 1

Abstract

We propose an improved Akaike information criterion (AICc) for generalized log-gamma regression models, which include the extreme-value and normal regression models as special cases. Moreover, we extend our proposed criterion to situations when the data contain censored observations. Monte Carlo results show that AICc outperforms the classical Akaike information criterion (AIC), and an empirical example is presented to illustrate its usefulness.

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广义Log-Gamma回归模型的改进Akaike信息准则

本文提出了一种改进的Akaike信息准则(Akaike information criterion, AICc)用于广义对数回归模型，其中包括极值回归模型和正态回归模型作为特例。此外，我们将我们提出的标准扩展到数据包含删减观测值的情况。蒙特卡罗结果表明，该方法优于经典的赤池信息准则(AIC)，并给出了一个实例来说明其有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.