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arXiv 0708.0169math.STmath.PRstat.MEstat.TH
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数据驱动的拟合优度检验

Data-driven goodness-of-fit tests

Mikhail Langovoy

摘要:

我们提出并研究了一种通用的方法,用于构建一致的统计检验的基础上,可能间接的,损坏的,或部分可用的观察。在本文中设计的测试类包含内曼的平滑测试,数据驱动的分数测试,和一些类型的多样本测试作为基本的例子。我们的测试是数据驱动的,并且还结合了模型选择规则。该方法允许使用广泛的一类模型选择规则的基础上的惩罚的想法。特别是,许多最佳的惩罚,来自统计文献,可以在我们的测试中使用。我们建立了模型选择规则和数据驱动测试的行为下的原假设和备择假设,推导出一个明确的可检测性规则备择假设,并证明了一个主一致性定理的测试从类。本文表明,该测试是适用于广泛的问题,包括假设检验在统计逆问题,多样本问题,和非参数假设检验。

英文摘要:

We propose and study a general method for construction of consistent statistical tests on the basis of possibly indirect, corrupted, or partially available observations. The class of tests devised in the paper contains Neyman's smooth tests, data-driven score tests, and some types of multi-sample tests as basic examples. Our tests are data-driven and are additionally incorporated with model selection rules. The method allows to use a wide class of model selection rules that are based on the penalization idea. In particular, many of the optimal penalties, derived in statistical literature, can be used in our tests. We establish the behavior of model selection rules and data-driven tests under both the null hypothesis and the alternative hypothesis, derive an explicit detectability rule for alternative hypotheses, and prove a master consistency theorem for the tests from the class. The paper shows that the tests are applicable to a wide range of problems, including hypothesis testing in statistical inverse problems, multi-sample problems, and nonparametric hypothesis testing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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