Efficient Computation of Hierarchical Trends
提出一种新算法，将状态空间模型分解为普通最小二乘和卡尔曼滤波两步，高效估计房价数据中的局部和总体趋势，适合处理含重复测量的大规模数据库。
To model a large database containing selling prices for houses, in which local trends, general trends, and specific characteristics play a role, we derived a new procedure to implement a state-space model for repeated measurements. The original model is decomposed into two parts, which are treated differently. The first part is ordinary least squares on data in deviation from means. This step provides a prior for coefficients to be used in the second step, which is a Kalman filter, providing estimates of the trends and the parameters. The procedure exploits and illustrates the Bayesian interpretation of a Kalman filter.