Francisco Cribari-Neto, José Jairo Santana-e-Silva, Klaus L.P. Vasconcellos
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引用次数: 0
摘要
贝塔回归模型适用于在标准单位区间内取值的响应。它包括两个子模型，一个用于平均响应，另一个用于精确参数。我们对这种模型的正确规范进行了检验。这些检验以信息矩阵相等为基础，当模型被正确指定时，信息矩阵相等成立。我们确定了检验在不同精度贝塔回归类中的有效性，提供了检验统计中使用的量的闭式表达式，并提出了检验的无效和非无效行为的模拟证据。我们表明，在采用数据重采样时，可以很好地控制 I 类错误概率，而且检验能够可靠地检测出错误的模型规范，尤其是在样本量不小的情况下。本文介绍并讨论了一个经验应用。
The beta regression model is tailored for responses that assume values in the standard unit interval. It comprises two submodels, one for the mean response and another for the precision parameter. We develop tests of correct specification for such a model. The tests are based on the information matrix equality, which holds when the model is correctly specified. We establish the validity of the tests in the class of varying precision beta regressions, provide closed-form expressions for the quantities used in the test statistics, and present simulation evidence on the tests’ null and non-null behavior. We show that it is possible to achieve very good control of the type I error probability when data resampling is employed and that the tests are able to reliably detect incorrect model specification, especially when the sample size is not small. An empirical application is presented and discussed.
期刊介绍：
The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.
We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
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