Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions [0.03%]
基于重尾分布的稳健泊松几何过程模型的贝叶斯分析
Wai-Yin Wan,Jennifer So-Kuen Chan
Wai-Yin Wan
We propose a robust Poisson geometric process model with heavy-tailed distributions to cope with the problem of outliers as it may lead to an overestimation of mean and variance resulting in inaccurate interpretations of the situations. Two...
Michael Höhle,Ulrike Feldmann
Michael Höhle
RLadyBug is an S4 package for the simulation, visualization and estimation of stochastic epidemic models in R. Maximum likelihood and Bayesian inference can be performed to estimate the parameters in a susceptible-exposed-infectious-recover...
A Skripnikov,G Michailidis
A Skripnikov
In a number of applications, one has access to high-dimensional time series data on several related subjects. A motivating application area comes from the neuroimaging field, such as brain fMRI time series data, obtained from various groups...
A Goodness-of-fit Test for Zero-Inflated Poisson Mixed Effects Models in Tree Abundance Studies [0.03%]
树丰度研究中零膨胀泊松混合效应模型的拟合优度检验
Juxin Liu,Yanyuan Ma,Jill Johnstone
Juxin Liu
Field studies in ecology often make use of data collected in a hierarchical fashion, and may combine studies that vary in sampling design. For example, studies of tree recruitment after disturbance may use counts of individual seedlings fro...
Non-inferiority Testing for Risk Ratio, Odds Ratio and Number Needed to Treat in Three-arm Trial [0.03%]
三臂试验中的风险比、比数比及治疗人次数量的非劣效性检验
Shrabanti Chowdhury,Ram C Tiwari,Samiran Ghosh
Shrabanti Chowdhury
Three-arm non-inferiority (NI) trial including the experimental treatment, an active reference treatment, and a placebo where the outcome of interest is binary are considered. While the risk difference (RD) is the most common and well explo...
Bayesian Hidden Markov Models for Dependent Large-Scale Multiple Testing [0.03%]
依赖的大规模多重比较的贝叶斯隐马尔科夫模型
Xia Wang,Ali Shojaie,Jian Zou
Xia Wang
An optimal and flexible multiple hypotheses testing procedure is constructed for dependent data based on Bayesian techniques, aiming at handling two challenges, namely dependence structure and non-null distribution specification. Ignoring d...
Philip S Boonstra,Ryan P Barbaro,Ananda Sen
Philip S Boonstra
In logistic regression, separation occurs when a linear combination of predictors perfectly discriminates the binary outcome. Because finite-valued maximum likelihood parameter estimates do not exist under separation, Bayesian regressions w...
A Smooth Nonparametric Approach to Determining Cut-Points of A Continuous Scale [0.03%]
一种确定连续尺度分类切割点的平滑非参数方法
Zhiping Qiu,Limin Peng,Amita Manatunga et al.
Zhiping Qiu et al.
The problem of determining cut-points of a continuous scale according to an establish categorical scale is often encountered in practice for the purposes such as making diagnosis or treatment recommendation, determining study eligibility, o...
Modelling and estimation of nonlinear quantile regression with clustered data [0.03%]
带有群集数据的非线性分位数回归的建模和估计
Marco Geraci
Marco Geraci
In regression applications, the presence of nonlinearity and correlation among observations offer computational challenges not only in traditional settings such as least squares regression, but also (and especially) when the objective funct...
Robust probabilistic classification applicable to irregularly sampled functional data [0.03%]
适用于不规则采样功能性数据的稳健概率分类方法
Yeonjoo Park,Douglas G Simpson
Yeonjoo Park
A robust probabilistic classifier for functional data is developed to predict class membership based on functional input measurements and to provide a reliable probability estimates for class membership. The method combines a Bayes classifi...