Bayesian Forecasting and Dynamic Models (2nd edn)
By Frederick P. Wheeler. 1997
Subfields: Probabilistic & Bayesian Methods, Time-Series & Forecasting
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Probabilistic & Bayesian Methods
- Arnaud Doucet
- Geir Storvik
- Simon J. Godsill
- Jaco Vermaak
- Matthew West
- Siddhartha Chib
- David M. Blei
- Nicolas Chopin
In Time-Series & Forecasting
- Rob J Hyndman
- J. De Gooijer
- Alysha M. De Livera
- Ralph D. Snyder
- José D. Bermúdez
- Enriqueta Vercher
- José Vicente Segura
- Konstantinos Fokianos
Citations in the corpus
- bridgesampling: An R Package for Estimating Normalizing Constants (2017, unknown): “Here we reanalyze a data set that contains the changes in monthly international exchange rates for pounds sterling from January 1975 to December 1986 (West and Harrison 1997, pp. 612-615).”
- A Spatio-Temporal Downscaler for Output From Numerical Models (2010, methodology): “In that case, for the updating of those parameters that evolve dynamically in time, we use the forward filtering backward sampling (FFBS) algorithm (Carter and Kohn 1994; West and Harrison 1999 ) within the MCMC.”
- Forecasting the European carbon market (2013, methodology): “Riskmetrics proposed setting κ=0:94 for daily data and our main results are for this choice, but we investigate sensitivity to it.”
- Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship (2007, methodology): “We employ the Gibbs sampler for estimation of our parameters, as in West and Harrison (1997).”
- Time-varying models for extreme values (2007, methodology): “For this purpose, we considered a second order polynomial (West and Harrison 1997) DLM for µt .”
- Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “…flexible parsimonious approach, we introduce the following trigonometric representation of seasonal components based on Fourier series (Harvey 1989; West and Harrison 1997): s(i)t = ki∑ j=1 s(i)j,t , (4a) s(i)j,t = s(i)j,t−1 cosλ(i)j + s∗(i)j,t−1 sinλ(i)j + γ (i)1 dt, (4b) s∗(i)j,t = −sj,t−1…”
- Partial Likelihood Inference For Time Series Following Generalized Linear Models (2004, methodology): “We focus on observation-driven models and refer the reader to the excellent texts by Fahrmeir and Tutz (2001), Harvey (1989) and West and Harrison (1997), for example, for further information on parameter-driven models.”