Modeling measurement error problems using generalized quasi-likelihood method

使用广义拟似然法对测量误差问题进行建模

• 批准号: 326970-2009
• 负责人: Fan, Zhaozhi
• 金额: $ 1.53万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=526762
• 关键词: Modeling; measurement; error; problems; using

In statistical modeling of data with dependent responses, measurement errors in covariates are often neglected, especially for data collected in socioeconomic studies. This causes estimation bias of the model parameters and usually leads to unsatisfactory efficiency of the inference. The correction of this estimation bias has recently attracted the attention of statisticians.The generalized method of moments (GMM) has been widely used to dependent data by economists to obtain consistent and efficient estimates of model parameters. It has be also demonstrated that the generalized quasilikelihood approach, which exploits the mean vector and the covariance structure of the repeated data, produces consistent and more efficient estimates compared with the GMM and other generalized estimating equations (GEE) methods.

在具有相关响应的数据的统计建模中，协变量中的测量误差往往被忽略，特别是对于社会经济研究中收集的数据。这会导致模型参数的估计偏差，并通常导致推理效率不理想。 对这种估计偏差的修正近年来引起了统计学家的关注，广义矩方法（GMM）被经济学家广泛应用于相依数据，以获得模型参数的一致和有效的估计。它还表明，广义拟似然方法，利用重复数据的平均向量和协方差结构，产生一致的和更有效的估计相比，GMM和其他广义估计方程（GEE）的方法。