弱收敛余经验过程是概率统计专业博士生的必修课程，也是经典书籍。

Empirical Market Microstructure

REMD是一种改进的经验模式分解，由软筛选停止标准（SSSC）提供支持。 SSSC是一种自适应筛分停止标准，用于自动停止EMD的筛分过程。 它从混合信号中提取出一组单分量信号（称为固有模式函数）。 它可以与Hilbert变换（或其他解调技术）一起用于时频分析。

Empirical Modeling and Data Analysis for Engineers and Applied Scientists English | 25 July 2016 | ISBN: 3319327674 | 264 Pages This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and “applied science” is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as “Statistics for Engineers and Scientists” without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models – predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.

matlab计算地球面积代码经验路径损失模型 奥村模型 无线电传播预测是无线电网络规划的基础之一。 因此，至关重要的是，传播预测模型必须尽可能准确。 传播路径损耗是电信系统链路预算的分析和设计的主要内容。 路径损耗的计算通常称为预测。 精确的预测只有在更简单的情况下才有可能，例如自由空间传播或平坦地球模型。 对于实际情况，使用各种近似值来计算路径损耗。 在本报告中，我们将讨论经验路径损耗模型，然后主要关注用于信号预测的Okumura模型。 Okumura模型是在城市地区使用最广泛的经验传播预测模型之一。 它是由Okumura Y.的作品开发而成，并基于日本某些城市和郊区的广泛测量结果。 我们将考虑造成传播损失的参数，例如自由空间传播损失，基站天线高度因子，移动天线高度增益因子，基本中值衰减和环境增益。 为了检查实际的实现和工作，可以在MATLAB上创建一些演示视图，方法是编写一些代码以查看接收功率与距离之间的关系，发射机高度与传播路径损耗之间的关系的实际图。

本文提出了一种构造自适应小波的新方法。其主要思想是通过设计合适的小波滤波器组来提取信号的不同模式。这种构造导致了一种新的小波变换，称为经验小波变换（EWT）。实验结果表明，与经典的经验模式分解(EMD)方法相比，该方法是可行的。

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and

通过概率经验回报分布估计和混合整数线性规划的均值-VaR投资组合优化 在此存储库中的Jupyter笔记本（.ipynb）中，我们提供了一种流行的现代投资组合理论（MPT）方法的替代方法，以优化资产分配。 与MPT相反，在MPT中，财务风险是通过预测收益的波动性（即标准误）来建模的，我们选择通过预测收益的经验性联合分布并制定优化问题以选择资产的目的，来更明确地表征此风险。分配以最大化该分布的均值，并限制资产选择的选择，以确保不会违反根据此经验分布测得的某些风险值（VaR）。 这种方法的主要原因是要解决MPT的主要缺点之一，即不一定捕获回报分配中可能很重的尾巴的行为，从而低估了资产的实际风险。 总体方法可以总结如下： 我们基于历史资产收益建立时间序列模型，以使模型的残差独立且均匀地分布（iid）。 我们使用模型和残差来生成自举预测，即使用时间序列模型预测下一个返回值，并从iid残差中随机采

多维集成经验模态分解法（THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD，MEEMD）

An empirical Bayes approach to efficient portfolio selection.pdfAn empirical Bayes approach to efficient portfolio selection.pdf