Newy-West adjustment provides a consistent estimation given autocorrelation in samples. The origin of Newy-West adjustment is closely related to Generalized Method of Moments. This blog includes the motivations, the definition and some properties of Generalized methods of Moments.
Before we dig deeper in the Kalman filter, I would like to share one of the applications of Kalman filter with you. N-factors Gaussian model is a direct application of what we have discussed in Kalman filter Part1. This blog includes a brief introduction about this method, and some tricks in this algorithm‘s implement.
R.E Kalman published a paper about a recursive solution to a discrete-data linear filtering problem. The Kalman filter provided us a computational method to estimate unobservable variables through minimization the posterior error. Moreover, Kalman filter enabled us to obtain the consistent parameter estimates by maximizing the likelihood function of error innovations.
Generally, stochastic approximation methods are a family of iterative methods. The goal of these algorithms is to recover some properties of a function depending on random variables. The application of stochastic approximation ranges from deep learning (e.g., SGD ) to online learning methods.
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