Time Delay Estimation for Stochastic Time Series of Gravitationally Lensed Quasars

We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) , Tak et al. (2018) , Hu and Tak (2020) .


Reference manual

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1.0.11 by Hyungsuk Tak, a year ago

Browse source code at https://github.com/cran/timedelay

Authors: Hyungsuk Tak , Kaisey Mandel , David A. van Dyk , Vinay L. Kashyap , Xiao-Li Meng , Aneta Siemiginowska , and Zhirui Hu

Documentation:   PDF Manual  

GPL-2 license

Imports MASS, mvtnorm

See at CRAN