article10.2307/2291224
Sequential Imputations and Bayesian Missing Data Problems
Augustine Kong,Jun S. Liu,Wing Hung Wong +2 more
294
Abstract: Abstract For missing data problems, Tanner and Wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors. Their method of constructing the complete data sets is closely related to the Gibbs sampler. Both required iterations, and, similar to the EM algorithm, convergence can be slow. We introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights. In many applications this new procedure works very well without the need for iterations. Sensitivity analysis, influence analysis, and updating with new data can be performed cheaply. Bayesian prediction and model selection can also be incorporated. Examples taken from a wide range of applications are used for illustration.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Filtering via Simulation: Auxiliary Particle Filters
Michael K. Pitt,Neil Shephard +1 more
TL;DR: This article analyses the recently suggested particle approach to filtering time series and suggests that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution.
Particle filters for positioning, navigation, and tracking
Fredrik Gustafsson,Fredrik Gunnarsson,Niclas Bergman,Urban Forssell,Jonas Jansson,Rickard Karlsson,Per-Johan Nordlund +6 more
TL;DR: The technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map and it is shown that the accuracy is comparable with satellite navigation but with higher integrity.
Propensity score estimation with boosted regression for evaluating causal effects in observational studies.
TL;DR: Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
Olivier Cappé,Simon J. Godsill,Eric Moulines +2 more
- 02 Jul 2007
TL;DR: This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
Particle filters for state estimation of jump Markov linear systems
TL;DR: This paper presents efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem forJump Markov linear systems.