Efficient high-dimensional importance sampling

By Jean-François Richard, Wei Zhang. 2007

273 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f1d7ee70aff9fc8c5388c35f7823888657ca8a24

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Citations in the corpus

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models (2016, unknown): “We follow the approach of Richard and Zhang (2007) and choose an importance sampling density that (approximately) minimises the variance of (24). Koopman, Lucas, and Scharth (2011) develop a new method to obtain such an efficient importance sampler using a combination of numerical integration techni”
Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model (2015, unknown): “Instead we use a Maximum Likelihood technique by introducing the Efficient Importance Sampling (EIS) procedure introduced by Liesenfeld and Richard (2003)[21] and Richard and Zhang (2007)[22].”
Multivariate stochastic volatility (2009, methodology): “Based on the Efficient Importance Sampling (EIS) method proposed by Richard and Zhang (2004), Liesenfeld and Richard (2004) developed an alternative multi-move MCMC algorithm.”
Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility (2014, background): “General approaches for estimating non-Gaussian state space models include importance sampling (Durbin and Koopman(2012) and Richard and Zhang(2007)), particle filters (see Malik and Pitt(2011)), and MCMC (see Jacquier, Johannes, and Polson(2007)).”
Importance sampling: a review (2010, background): “A variation of the above idea was proposed in Richard and Zhang (2007).”
Discrete Choice Methods with Simulation (2016, background): “Since importance sampling is introduced, the concept of efficient importance sampling (Richard and Zhang, 2007) would fit very well into the book.”
A Semiparametric Stochastic Volatility Model (2012, unknown): “Several numerical methods have been proposed to approximate the integral via importance sampling techniques; see, for example, Shephard and Pitt (1997), Durbin and Koopman (1997) and Richard and Zhang (2007).”
Importance Sampling Squared for Bayesian Inference in Latent Variable Models (2013, methodology): “apply the particle ecient importance sampling (PEIS) method of Scharth and Kohn (2013). The PEIS method is a sequential Monte Carlo procedure for likelihood estimation that uses the EIS algorithm of Richard and Zhang (2007) to obtain a high-dimensional importance density for the states. We implement”
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006, methodology): “Fully likelihood-based methods include the simulated maximum likelihood method (SML) (Danielsson, 1994; Durham, 2005; Richard and Zhang, 2004; Sandmann and Koopman, 1998), the numerical maximum likelihood method (Fridman and Harris, 1998), and the Bayesian Markov chain Monte Carlo (MCMC) methods (Ja”
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models (2006, methodology): “Another highly sensitive test to assess the existence of the variance for the EIS sampler can be found in Richard and Zhang (2006).”