Statistics and Computing
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On conditional variance estimation in nonparametric regression
Statistics and Computing - Tập 23 - Trang 261-270 - 2012
In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib (in Rev. Econ. Stud. 65:361–393, 1998) f......
Optimal designs for dose-escalation trials and individual allocations in cohorts
Statistics and Computing - Tập 32 - Trang 1-16 - 2022
Dose escalation trials are crucial in the development of new pharmaceutical products to optimize the amount of drug administered while avoiding undesirable side effects. We adopt the framework established by Bailey (Stat Med 28(30):3721–3738, 2009.
https://doi.org/10.1002/sim.3646......
Embedded topics in the stochastic block model
Statistics and Computing - Tập 33 - Trang 1-20 - 2023
Communication networks such as emails or social networks are now ubiquitous and their analysis has become a strategic field. In many applications, the goal is to automatically extract relevant information by looking at the nodes and their connections. Unfortunately, most of the existing methods focus on analysing the presence or absence of edges and textual data is often discarded. However, all co......
Sequential Monte Carlo with transformations
Statistics and Computing - Tập 30 - Trang 663-676 - 2019
This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an......
Efficient Monte Carlo simulation via the generalized splitting method
Statistics and Computing - Tập 22 - Trang 1-16 - 2010
We describe a new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multidimensional densities. The algorithm is inspired by the classical splitting method and can be applied to general static simulation models. We provide examples from rare-event probability estimation, counting, and sampling, demonstrating that the prop......
Semi-automated simultaneous predictor selection for regression-SARIMA modelsAbstract Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges of predicting events across a telecommunications network, we propose a semi-automated, joint model-fitting and predictor selection procedure for linear regression models. Our approach can model and account for s... ...
Statistics and Computing - - 2020
Weighted likelihood mixture modeling and model-based clustering
Statistics and Computing - Tập 30 - Trang 255-277 - 2019
A weighted likelihood approach for robust fitting of a mixture of multivariate Gaussian components is developed in this work. Two approaches have been proposed that are driven by a suitable modification of the standard EM and CEM algorithms, respectively. In both techniques, the M-step is enhanced by the computation of weights aimed at downweighting outliers. The weights are based on Pearson resid......
Marginal information for structure learning
Statistics and Computing - Tập 30 - Trang 331-349 - 2019
Structure learning for Bayesian networks has been made in a heuristic mode in search of an optimal model to avoid an explosive computational burden. In the learning process, a structural error which occurred at a point of learning may deteriorate its subsequent learning. We proposed a remedial approach to this error-for-error process by using marginal model structures. The remedy is made by fixing......
A stopping rule for structure-preserving variable selection
Statistics and Computing - Tập 6 - Trang 51-56 - 1996
A stopping rule is provided for the backward elimination process suggested by Krzanowski (1987a) for selecting variables to preserve data structure. The stopping rule is based on perturbation theory for Procrustes statistics, and a small simulation study verifies its suitability. Some illustrative examples are also provided and discussed.
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