English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponentialsmoothing, and navigation are often formulated and solved using state-spacemodels (SSM), which allow a range of statistical distributions to informinnovations and errors. In many applications the main goal is to identify notonly the hidden state, but also additional unknown model parameters (e.g. ARcoefficients or unknown dynamics).  We show how to efficiently optimize over model parameters in SSM that usesmooth process and measurement losses. Our approach is to project out statevariables, obtaining a value function that only depends on the parameters ofinterest, and derive analytical formulas for first and second derivatives thatcan be used by many types of optimization methods.  The approach can be used with smooth robust penalties such as Hybrid and theStudent's t, in addition to classic least squares. We use the approach toestimate robust AR models and long-run unemployment rates with sudden changes.

Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models