WEIGHTED‐AVERAGE LEAST SQUARES (WALS): A SURVEY
综述了加权平均最小二乘法(WALS)的理论、扩展与应用，该方法介于频率学派和贝叶斯方法之间，能处理无知问题且计算极快。
Abstract Model averaging has become a popular method of estimation, following increasing evidence that model selection and estimation should be treated as one joint procedure. Weighted‐average least squares (WALS) is a recent model‐average approach, which takes an intermediate position between frequentist and Bayesian methods, allows a credible treatment of ignorance, and is extremely fast to compute. We review the theory of WALS and discuss extensions and applications.