De Iorio, M;
The time machine: a simulation approach for stochastic trees.
P ROY SOC A-MATH PHY
In this paper, we consider a simulation technique for stochastic trees. One of the most important areas in computational genetics is the calculation and subsequent maximization of the likelihood function associated with such models. This typically consists of using importance sampling and sequential Monte Carlo techniques. The approach proceeds by simulating the tree, backward in time from observed data, to a most recent common ancestor. However, in many cases, the computational time and variance of estimators are often too high to make standard approaches useful. In this paper, we propose to stop the simulation, subsequently yielding biased estimates of the likelihood surface. The bias is investigated from a theoretical point of view. Results from simulation studies are also given to investigate the balance between loss of accuracy, saving in computing time and variance reduction.
|Title:||The time machine: a simulation approach for stochastic trees|
|Keywords:||stochastic trees, sequential Monte Carlo, coalescent, MONTE-CARLO METHODS, STATE-SPACE MODELS, MAXIMUM-LIKELIHOOD, ASYMPTOTIC PROPERTIES, COALESCENT HISTORIES, POPULATION-GENETICS, NEUTRAL ALLELES, SAMPLING THEORY, SEQUENCE DATA, ESTIMATORS|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
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