Evaluation of combinatorial optimisation algorithms for c-optimal experimental designs with correlated observations [0.03%]
相关观察下的实验设计的组合优化算法评估
Samuel I Watson,Yi Pan
Samuel I Watson
We show how combinatorial optimisation algorithms can be applied to the problem of identifying c-optimal experimental designs when there may be correlation between and within experimental units and evaluate the performance of relevant algor...
A fast look-up method for Bayesian mean-parameterised Conway-Maxwell-Poisson regression models [0.03%]
一种用于贝叶斯均值参数化的康威-麦克斯韦泊松回归模型的快速查找方法
Pete Philipson,Alan Huang
Pete Philipson
Count data that are subject to both under and overdispersion at some hierarchical level cannot be readily accommodated by classic models such as Poisson or negative binomial regression models. The mean-parameterised Conway-Maxwell-Poisson d...
Phylourny: efficiently calculating elimination tournament win probabilities via phylogenetic methods [0.03%]
基于系统发育方法高效计算淘汰赛获胜概率的方法（英文）
Ben Bettisworth,Alexander I Jordan,Alexandros Stamatakis
Ben Bettisworth
The prediction of knockout tournaments represents an area of large public interest and active academic as well as industrial research. Here, we show how one can leverage the computational analogies between calculating the phylogenetic likel...
Variable selection using a smooth information criterion for distributional regression models [0.03%]
基于分布回归模型的光滑信息准则变量选择方法
Meadhbh O&#x;Neill,Kevin Burke
Meadhbh O&#x;Neill
Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the least absolute shrinkage and selection operator, the use of which requires selecting the v...
On predictive inference for intractable models via approximate Bayesian computation [0.03%]
基于近似贝叶斯计算的难以处理模型的预测推断方法研究
Marko Järvenpää,Jukka Corander
Marko Järvenpää
Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the...
A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions [0.03%]
高维下的可扩展联合回归和协方差选择的广义似然基于贝叶斯方法
Srijata Samanta,Kshitij Khare,George Michailidis
Srijata Samanta
The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patter...
Daniel Ahfock,William J Astle,Sylvia Richardson
Daniel Ahfock
There is an increasing body of work exploring the integration of random projection into algorithms for numerical linear algebra. The primary motivation is to reduce the overall computational cost of processing large datasets. A suitably cho...
Automatic search intervals for the smoothing parameter in penalized splines [0.03%]
惩罚样条中平滑参数的自动搜索区间
Zheyuan Li,Jiguo Cao
Zheyuan Li
The selection of smoothing parameter is central to the estimation of penalized splines. The best value of the smoothing parameter is often the one that optimizes a smoothness selection criterion, such as generalized cross-validation error (...
Interpolating log-determinant and trace of the powers of matrix [Formula: see text] [0.03%]
矩阵对数行列式及幂的迹的插值[公式: 见文本]
Siavash Ameli,Shawn C Shadden
Siavash Ameli
We develop heuristic interpolation methods for the functions t ↦ log det A + t B and t ↦ trace ( A + t B ) p where the matrices A and B are Hermitian and positive (semi) definite and p and t are real variables. These fun...
Mark J Meyer,Elizabeth J Malloy,Brent A Coull
Mark J Meyer
Historical Functional Linear Models (HFLM) quantify associations between a functional predictor and functional outcome where the predictor is an exposure variable that occurs before, or at least concurrently with, the outcome. Prior work on...