论文检索

二元工具变量与删失数据下的因果分位数效应估计AI 解读

Abstract The causal effect of a treatment is of fundamental interest in the social, biological and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under…

纵向数据回归模型的累积回归函数检验AI 解读

The longitudinal regression model $Y_{i,j} = m(V_\tau_{i,j}^i) + \varepsilon_{i,j}$ where $Y_{i,j}$, is the $j$th measurement of the $i$th subject at random time $\tau_{i,j}$, $m$ is the regression function, $V_\tau_{i, j}$ is a predictable covariate process observed at time $\tau_{i,j}$ and $\varepsilon_{i,j}$ is noise, often provides an adequate framework for modeling and comparing groups of data. The proposed longitudinal regression model is based on marked point process…

利用一阶累积特征识别非线性时间序列AI 解读

A new approach to the problem of identifying a nonlinear time series model is considered. We argue that cumulative lagged conditional mean and variance functions are the appropriate "signatures" of a nonlinear time series for the purpose of model identification, being analogous to cumulative distribution functions or cumulative hazard functions in iid models. We introduce estimators of the cumulative lagged conditional mean and variance functions and study their asymptotic…