摘要翻译:
我们考虑在反卷积模型中检验关于信号密度的统计假设。提出了解决这一问题的一种新方法。在已知噪声密度的情况下构造了反卷积的分数检验,在未知噪声密度的情况下构造了有效分数检验。将试验与模型选择规则相结合,根据数据自动选择合理的模型尺寸。证明了试验的一致性。
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英文标题:
《Data-driven efficient score tests for deconvolution problems》
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作者:
Mikhail Langovoy
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最新提交年份:
2007
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分类信息:
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类:Statistics 统计学
二级分类:Applications 应用程序
分类描述:Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学,教育学,流行病学,工程学,环境科学,医学,物理科学,质量控制,社会科学
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一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
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英文摘要:
We consider testing statistical hypotheses about densities of signals in deconvolution models. A new approach to this problem is proposed. We constructed score tests for the deconvolution with the known noise density and efficient score tests for the case of unknown density. The tests are incorporated with model selection rules to choose reasonable model dimensions automatically by the data. Consistency of the tests is proved.
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PDF链接:
https://arxiv.org/pdf/707.0861