Efficient non-parametric estimation of variable productivity Hawkes processes [0.03%]
有效估计变量生产力霍克斯过程的非参数方法
Sophie Phillips,Frederic Schoenberg
Sophie Phillips
Several approaches to estimating the productivity function for a Hawkes point process with variable productivity are discussed, improved upon, and compared in terms of their root-mean-squared error and computational efficiency for various d...
Semiparametric regression analysis of panel binary data with a dependent failure time [0.03%]
具有依赖失效时间的面板二值数据的半参数回归分析
Lei Ge,Yang Li,Jianguo Sun
Lei Ge
In health and clinical research, panel binary data from recurrent events arise when subjects are surveyed to report occurrence statuses of recurrent events over fixed observation windows. In practice, such data can be cut short by a depende...
A bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance [0.03%]
一种估计公共政策因果效应的自助法，考虑重叠和不完美遵从性
Stefano Cabras
Stefano Cabras
This paper introduces a nonparametric bootstrap method for estimating the causal effects of public policy under the circumstances of imperfect compliance and overlap. It focuses on business investment subsidies in Sardinia by comparing firm...
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...