We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system. Counterintuitively, we show that when the underlying dynamics are stable the parameters of this model can be estimated from data by combining just two ordinary least squares estimates. We offer a finite sample guarantee on the estimation error of our method and discuss certain advantages it has over Expectation-Maximization (EM), which is the main approach proposed by prior work.

翻译：我们重新审视一个时间变化线性回归模型,该模型假设未知参数会根据线性动态系统演变。 反直观地说,我们表明,当基本动态稳定时,该模型的参数可以通过仅仅合并两个普通最小方位估计数从数据中估算出来。 我们对我们方法的估计错误提供有限的样本保证,并讨论它比预期-最大化(EM)(这是先前工作建议的主要方法)具有的某些优势。