Recent Advances in the Measurement Error Literature
Recent Advances in the Measurement Error Literature
复制标题
测量误差文献的最新进展
DOI：
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复制
发表时间：
2016
期刊：
影响因子：
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通讯作者：
Susanne M. Schennach
中科院分区：
文献类型：
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作者：
Susanne M. Schennach
本文回顾了最近在发展非线性模型的估计和推理方法方面取得的重大进展，这些方法可能符合也可能不符合独立零平均误差的经典假设。其目的是涵盖具有不同复杂程度和所需假设强度的广泛方法。首先讨论构成更高级方法的基本构件的简单方法。然后，特别关注依赖于现成的辅助变量（例如，重复测量，指标或工具变量）的方法。结果放宽了大多数常用的简化假设（线性测量结构，独立误差，零均值误差，辅助信息的可用性）。本文还概述了与相关领域的重要联系，例如潜在变量模型，非线性面板数据，因素模型和集识别，以及方法在传统上与测量误差模型无关的其他领域的应用。
This article reviews recent significant progress made in developing estimation and inference methods for nonlinear models in the presence of mismeasured data that may or may not conform to the classical assumption of independent zero-mean errors. The aim is to cover a broad range of methods having differing levels of complexity and strength of the required assumptions. Simple approaches that form the elementary building blocks of more advanced approaches are discussed first. Then, special attention is devoted to methods that rely on readily available auxiliary variables (e.g., repeated measurements, indicators, or instrumental variables). Results relaxing most of the commonly invoked simplifying assumptions are presented (linear measurement structure, independent errors, zero-mean errors, availability of auxiliary information). This article also provides an overview of important connections with related fields, such as latent variable models, nonlinear panel data, factor models, and set identification, and applications of the methods to other fields traditionally unrelated to measurement error models.
识别无单调性的不可分离模型中的边际效应
影响因子：
6.1
作者：
Hoderlein;Mammen
通讯作者：
Mammen