Flexible parametric estimation of duration and competing risk models
持续期与竞争风险模型的灵活参数估计AI 解读 ↓
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持续期与竞争风险模型的灵活参数估计AI 解读 ↓
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Abstract In this paper we specify and estimate a flexible parametric proportional hazards model. The model specification is flexibly parametric in the sense that the baseline hazard is non parametric while the effect of the covariates takes a particular functional form. We also add parametric heterogeneity to the underlying hazard model specification. We specify a flexible parametric proportional competing risks model which permits unrestricted correlation among the risks. Unemployment duration data are then analysed using the flexible parametric duration and competing risks specifications. We find an important effect arising from the exhaustion of unemployment insurance and significantly different hazards for the two types of risks, new jobs and recalls.
PICPIs:预测区间条件预测区间
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估计纵向因果剂量反应关系的贝叶斯框架
Existing causal methods for time-varying exposure and time-varying confounding focus on estimating the average causal effect of a time-varying binary treatment on an end-of-study outcome, offering limited tools for characterizing marginal causal dose-response relationships under continuous exposures. We propose a scalable, nonparametric Bayesian framework for estimating marginal longitudinal causal dose-response functions with repeated outcome measurements. Our approach…
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当处理发生溢出:时空因果效应估计的神经网络框架
Real-world public health policies are often enacted by local jurisdictions, but their effects can ripple through connected populations. For causal evaluation, this creates a difficult spatiotemporal problem: intermittent policy updates create path-level treatments, and because spillover depends on neighboring treatment paths along with the focal-unit's own history, controlling time-varying confounding requires high-dimensional, multimodal information from both sources. To…
ARMA 过程谱密度函数的分解
Quasi-periodic oscillations are characterized by fitting Lorentzian functions to a nonparametric estimate of the power spectral density, a two-step procedure that separates estimation from modeling and does not accommodate measurement error. We develop a parametric alternative based on autoregressive moving-average (ARMA) processes observed with measurement error. We derive an exact decomposition of the ARMA spectral density into real-valued components, one for each class of…