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Updated: Jul 6, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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适用于SIS模型的临时本地最大概率

Christian Gourieroux1,2, Joann Jasiak3

  • 1University of Toronto, Toronto, Canada.

Journal of time series econometrics
|December 29, 2023
PubMed
概括

本研究分析了非线性时间序列中的时间变化的参数的局部最大概率 (TLML) 估计器. 这项研究强调了估计器重量如何显著影响结果,通过易受感染易受感染 (SIS) 模型模拟证明了这一点.

关键词:
C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C01 C02 C02 C02 C02 C02 C01 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 C02 没有一个人没有一个人在C1313中,它是C13的.C2222 这是一个很好的例子.这是一个SIS模型.偏见减少偏见减少偏见一般化的线性模型.当地的最大概率.后勤增长 增长 后勤增长忽略了异质性的遗漏.滚动估计器的滚动估计器超长距离运行 超长距离运行

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