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......
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......
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.
D-optimal designs via a cocktail algorithm
Statistics and Computing - Tập 21 - Trang 475-481 - 2010
A fast new algorithm is proposed for numerical computation of (approximate) D-optimal designs. This cocktail algorithm extends the well-known vertex direction method (VDM; Fedorov in Theory of Optimal Experiments, 1972) and the multiplicative algorithm (Silvey et al. in Commun. Stat. Theory Methods 14:1379–1389, 1978), and shares their simplicity and monotonic convergence properties. Numerical exa......
Temperature schedules for simulated annealing
Statistics and Computing - Tập 4 - Trang 21-32 - 1994
It is well known that the behaviour of the simulated annealing approach to optimization is crucially dependent on the choice of temperature schedule. In this paper, a dynamic programming approach is used to find the temperature schedule which is optimal for a simple minimization problem. The optimal schedule is compared with certain standard non-optimal choices. These generally perform well provid......
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model
Statistics and Computing - Tập 29 - Trang 301-310 - 2018
Gibbs sampling is a widely used Markov chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences. Many implementations of MCMC methods do not extend easily to parallel computing environments, as their inherently sequential nature incurs a large synchronization cost. In the case study illustrated by this paper, we show......
On the design of optimal change-over experiments through multi-objective simulated annealing
Statistics and Computing - Tập 9 - Trang 37-42 - 1999
The construction of optimal designs for change-over experiments requires consideration of the two component treatment designs: one for the direct treatments and the other for the residual (carry-over) treatments. A multi-objective approach is introduced using simulated annealing, which simultaneously optimises each of the component treatment designs to produce a set of dominant designs in one run ......
Automatic polynomial wavelet regression
Statistics and Computing - Tập 14 Số 4 - Trang 337-341 - 2004
Central quantile subspace
Statistics and Computing - Tập 30 - Trang 677-695 - 2019
Quantile regression (QR) is becoming increasingly popular due to its relevance in many scientific investigations. There is a great amount of work about linear and nonlinear QR models. Specifically, nonparametric estimation of the conditional quantiles received particular attention, due to its model flexibility. However, nonparametric QR techniques are limited in the number of covariates. Dimension......
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