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用于贝叶斯优化的层级期望改进方法AI 解读

The Expected Improvement (EI) method, proposed by Jones, Schonlau, andWelch, is a widely used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI is that it is overly greedy in exploiting the fitted Gaussian process model for optimization, which results in suboptimal solutions even with large sample sizes. To address this, we propose a new hierarchical EI (HEI) framework, which…

最优模拟器选择AI 解读

Computer simulators are widely used for the study of complex systems. In many applications, there are multiple simulators available with different scientific interpretations of the underlying mechanism, and the goal is to identify an optimal simulator based on the observed physical experiments. To achieve the goal, we propose a selection criterion based on leave-one-out cross-validation. This criterion consists of a goodness-of-fit measure and a generalized degrees of…

用于二元时间序列计算机实验的广义高斯过程模型AI 解读

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an…

cmenet：条件主效应的双层变量选择方法AI 解读

Simon Maka & C. F. Jeff Wua*a School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GACONTACT C. F. Jeff Wu jeffwu@isye.gatech.edu School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to…

大涡模拟的高效代理模型与物理机制提取AI 解读

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations, and statistical modeling. In this article, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices…

不完美计算机模型的有效校准AI 解读

Many computer models contain unknown parameters which need to be estimated using physical observations. Tuo and Wu (2014) show that the calibration method based on Gaussian process models proposed by Kennedy and O’Hagan [J. R. Stat. Soc. Ser. B. Stat. Methodol. 63 (2001) 425–464] may lead to an unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the $L_{2}$…

后费舍尔实验设计：从实体实验到虚拟实验AI 解读

Fisher’s pioneering work in design of experiments has inspired further work with broader applications, especially in industrial experimentation. This article discusses three topics in physical experiments: principles of effect hierarchy, sparsity, and heredity for factorial designs, a new method called conditional main effect (CME) for de-aliasing aliased effects, and robust parameter design. I also review the recent emergence of virtual experiments on a computer. Some major…

二元时间序列建模及其在黏附频率实验中的应用AI 解读

Repeated adhesion frequency assay is the only published method for measuring the kinetic rates of cell adhesion. Cell adhesion plays an important role in many physiological and pathological processes. Traditional analysis of adhesion frequency experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption can often be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating…

估计权重对加权最小二乘的影响AI 解读

In weighted least squares, it is typical that the weights are unknown and must be estimated. Most packages provide standard errors assuming that the weights are known. This is fine for sufficiently large sample sizes, but what about for small-to-moderate sample sizes? The investigation of this article into the effect of estimating weights proceeds under the assumption typical in practice—that one has a parametric model for the variance function. In this context, generalized…

二元响应数据的高效序贯设计AI 解读

A class of sequential designs for estimating the percentiles of a quantal response curve is proposed. Its updating rule is based on an efficient summary of all of the data available via a parametric model. The logit-MLE version of the proposed designs can be viewed as a natural analog of the Robbins—Monro procedure in the case of binary data. It is shown to be asymptotically consistent, optimal, and nonparametric via its connection with the latter procedure. For certain…

合并比率估计量与合并回归估计量的方差估计AI 解读

SUMMARY A class of variance estimators including the standard ones, for the combined ratio estimator and for the combined regression estimator, is proposed. Optimal variance estimators in terms of minimizing the mean square error are obtained. Both model-free and model-dependent comparison of some standard variance estimators is made. For the combined ratio estimator, a new formula for the second order expansion of its mean square error is derived; the jackknife variance…

已知协方差下线性试验的比较AI 解读

For two linear experiments $d_1 = L(X_1\beta, V_1)$ and $d_2 = L(X_2\beta, V_2)$ where the covariances $V_1$ and $V_2$ are known and can be singular or nonsingular, we characterize the following relations: $d_1$ at least as good as $d_2, d_1$ better than $d_2$, and $d_1$ equivalent to $d_2$. Sometimes only a subset of parameters is of interest to the experimenter. We extend the above relations between $d_1$ and $d_2$ to estimation of a common subset of parameters and give…