Exact Likelihood of Vector Autoregressive-Moving Average Process with Missing or Aggregated Data
本文指出，通过使用非恒定系数的卡尔曼滤波器，可以在观测数据存在缺失或聚合时，计算带噪声的自回归移动平均过程的精确似然。
This note points out that by using the Kalman filter with nonconstant coefficients, we can compute the exact likelihood of an autoregressive-moving average process observed with noise, when some of our observations are either missing or aggregated.