摘要翻译：
从一组采集的数据中推断信息是任何信号处理(SP)方法的主要目标。特别是，从一组噪声测量中估计参数向量值的共同问题是过去几十年中科学和技术进步的核心；例如，无线通信、雷达和声纳、生物医学、图像处理和地震学，仅举几个例子。开发估计算法通常首先假设测量数据的统计模型，即概率密度函数(pdf)，如果正确，它将充分表征收集的数据/测量的行为。然而，使用真实数据的经验常常暴露出任何假定数据模型的局限性，因为在某种程度上建模错误总是存在的。因此，真实的数据模型和推导估计算法所假设的模型可能不同。当这种情况发生时，模型被称为不匹配或错误指定。因此，了解估计算法在模型错误规范下可能经历的性能损失或遗憾对于任何SP从业者来说都是至关重要的。此外，了解任何估计器的性能的限制受到模型错误的规范是有实际意义的。由于评估不匹配估计器性能的广泛和实际需要，本文的目标是帮助引起人们对估计理论的关注，特别是在过去50年中统计和经济计量文献中发表的关于模型不规范下的下界的主要理论发现。其次，讨论了一些应用，以说明该框架扩展到的广泛领域和问题，从而为SP研究人员提供了许多机会。
---
英文标题：
《Performance Bounds for Parameter Estimation under Misspecified Models:
  Fundamental findings and applications》
---
作者：
S. Fortunati, F. Gini, M. S. Greco, C. D. Richmond
---
最新提交年份：
2017
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--

---
英文摘要：
  Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. In particular, the common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in the last decades; for example, wireless communications, radar and sonar, biomedicine, image processing, and seismology, just to name a few. Developing an estimation algorithm often begins by assuming a statistical model for the measured data, i.e. a probability density function (pdf) which if correct, fully characterizes the behaviour of the collected data/measurements. Experience with real data, however, often exposes the limitations of any assumed data model since modelling errors at some level are always present. Consequently, the true data model and the model assumed to derive the estimation algorithm could differ. When this happens, the model is said to be mismatched or misspecified. Therefore, understanding the possible performance loss or regret that an estimation algorithm could experience under model misspecification is of crucial importance for any SP practitioner. Further, understanding the limits on the performance of any estimator subject to model misspecification is of practical interest. Motivated by the widespread and practical need to assess the performance of a mismatched estimator, the goal of this paper is to help to bring attention to the main theoretical findings on estimation theory, and in particular on lower bounds under model misspecification, that have been published in the statistical and econometrical literature in the last fifty years. Secondly, some applications are discussed to illustrate the broad range of areas and problems to which this framework extends, and consequently the numerous opportunities available for SP researchers.
---
PDF链接：
https://arxiv.org/pdf/1709.0821