A unit-level one-inflated beta model for small area prediction of seat-belt use rates [0.03%]
一种用于估计安全带使用率的小区域单位水平一次膨胀贝塔模型预测方法研究
Zirou Zhou,Emily Berg
Zirou Zhou
We develop a unit-level one-inflated beta model for the purpose of small area estimation. Our specific interest is in estimation of seat-belt use rates for Iowa counties using data from the Iowa Seat-Belt Use Survey. As a result of small co...
Mixture mean residual life model for competing risks data with mismeasured covariates [0.03%]
具有测量误差的竞险数据的混合平均剩余寿命模型
Chyong-Mei Chen,Chih-Ching Lin,Chih-Cheng Wu et al.
Chyong-Mei Chen et al.
This paper proposes a mixture regression model for competing risks data, where the logistic regression model is specified for the marginal probabilities of the failure types and the mean residual lifetime (MRL) model is assumed for the fail...
A non-linear integer-valued autoregressive model with zero-inflated data series [0.03%]
具有零膨胀数据序列的非线性整值自回归模型
Predrag M Popović,Hassan S Bakouch,Miroslav M Ristić
Predrag M Popović
A new non-linear stationary process for time series of counts is introduced. The process is composed of the survival and innovation component. The survival component is based on the generalized zero-modified geometric thinning operator, whe...
Robust estimation of the incubation period and the time of exposure using γ-divergence [0.03%]
利用γ散度 robust 估计暴露时间及潜伏期
Daisuke Yoneoka,Takayuki Kawashima,Yuta Tanoue et al.
Daisuke Yoneoka et al.
Estimating the exposure time to single infectious pathogens and the associated incubation period, based on symptom onset data, is crucial for identifying infection sources and implementing public health interventions. However, data from rap...
Mitigating the choice of the duration in DDMS models through a parametric link [0.03%]
通过参数链接缓解DDMS模型中持续时间的选择问题
Fernando Henrique de Paula E Silva Mendes,Douglas Eduardo Turatti,Guilherme Pumi
Fernando Henrique de Paula E Silva Mendes
One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration i...
Evaluating the median p-value method for assessing the statistical significance of tests when using multiple imputation [0.03%]
评估多重插补法使用多个假设检验时评估中位p值方法的统计显著性
Peter C Austin,Iris Eekhout,Stef van Buuren
Peter C Austin
Rubin's Rules are commonly used to pool the results of statistical analyses across imputed samples when using multiple imputation. Rubin's Rules cannot be used when the result of an analysis in an imputed dataset is not a statistic and its ...
Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data [0.03%]
一种基于复值fMRI数据的高效全贝叶斯脑活动图谱构建方法
Zhengxin Wang,Daniel B Rowe,Xinyi Li et al.
Zhengxin Wang et al.
Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. I...
Prediction intervals and bands with improved coverage for functional data under noisy discrete observation [0.03%]
在噪声离散观测下的函数数据的改进覆盖预测区间和带状图
David Kraus
David Kraus
We revisit the classic situation in functional data analysis in which curves are observed at discrete, possibly sparse and irregular, arguments with observation noise. We focus on the reconstruction of individual curves by prediction interv...
Wisdom Aselisewine,Suvra Pal,Helton Saulo
Wisdom Aselisewine
The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a ...
R Lakshmi,T A Sajesh
R Lakshmi
Identifying outliers in data analysis is a critical task, as outliers can significantly influence the results and conclusions drawn from a dataset. This study explores the use of the Mahalanobis distance metric for detecting outliers in mul...