A reduced basis decomposition approach to efficient data collection in pairwise comparison studies [0.03%]
成对比较研究中有效数据采集的降阶基础分解方法
Jiahua Jiang,Joseph Marsh,Rowland Seymour
Jiahua Jiang
Comparative judgement studies elicit quality assessments of objects through pairwise comparisons, typically analysed using the Bradley-Terry model. A challenge in these studies is experimental design, specifically, determining the optimal p...
A latent class pattern mixture model for nonignorable nonresponses in multivariate categorical data [0.03%]
多元分类数据非忽略缺失下的潜在类别模式混合模型
Jungwun Lee,Margaret Lloyd Sieger,Jon D Phillips
Jungwun Lee
Survey data using categorical item variables are widely used in applied research such as psychology, education, and behavioral studies. Unfortunately, survey data are highly susceptible to nonignorable missing values that may threaten the v...
A stochastic approach to k-nearest neighbors search using a fixed radius method [0.03%]
基于固定半径的k近邻算法的随机方法研究
Brahian Cano Urrego,Alexander Alsup,Jeffrey A Thompson et al.
Brahian Cano Urrego et al.
This study aims to optimize the [Formula: see text]-nearest neighbors search (kNN search) by reducing the computational burden of the well-known Brute-force method while providing the same solution. While there exist rule-based approaches f...
Ami Sheth,Aaron Smith,Andrew J Holbrook
Ami Sheth
Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that allows one to model and visualize data consisting of dissimilarities between pairs of objects. Although BMDS has proven useful within, e.g., Bayesian ...
Owen Thomas,Raquel Sá-Leão,Hermínia de Lencastre et al.
Owen Thomas et al.
Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a challenge for the Ap...
Cornelia Fuetterer,Malte Nalenz,Thomas Augustin et al.
Cornelia Fuetterer et al.
Penalized regression methods that shrink model coefficients are popular approaches to improve prediction and for variable selection in high-dimensional settings. We present a penalized (or regularized) regression approach for multinomial lo...
A class of transformed joint quantile time series models with applications to health studies [0.03%]
一类变换联合分位数时间序列模型及其在健康研究中的应用
Fahimeh Tourani-Farani,Zeynab Aghabazaz,Iraj Kazemi
Fahimeh Tourani-Farani
Extensions of quantile regression modeling for time series analysis are extensively employed in medical and health studies. This study introduces a specific class of transformed quantile-dispersion regression models for non-stationary time ...
Approximate Bayesian inference in a model for self-generated gradient collective cell movement [0.03%]
自生梯度集体细胞运动模型中的近似贝叶斯推理
Jon Devlin,Agnieszka Borowska,Dirk Husmeier et al.
Jon Devlin et al.
In this article we explore parameter inference in a novel hybrid discrete-continuum model describing the movement of a population of cells in response to a self-generated chemotactic gradient. The model employs a drift-diffusion stochastic ...
Likelihood Inference for Unified Transformation Cure Model with Interval Censored Data [0.03%]
区间删失数据下统一变换模型的似然推断方法
Jodi Treszoks,Suvra Pal
Jodi Treszoks
In this paper, we extend the unified class of Box-Cox transformation (BCT) cure rate models to accommodate interval-censored data. The probability of cure is modeled using a general covariate structure, whereas the survival distribution of ...
Peter C Austin
Peter C Austin
In time-to-event analyses, a competing risk is an event whose occurrence precludes the occurrence of the event of interest. Settings with competing risks occur frequently in clinical research. Missing data, which is a common problem in rese...