A Note on Nonparametric Estimation of the Distribution Function from Interval-Censored and Truncated Observations
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SUMMARY This paper is concerned with the nonparametric estimation of the distribution function F from interval-censored and truncated data as discussed by Turnbull. It is observed that Turnbull's characterization of F̂, the maximum likelihood estimator of F, is not applicable in general when the data are interval censored and truncated. It is shown, however, that Turnbull's characterization can be easily modified to obtain the appropriate characterization of F̂.
弱依赖非线性时间序列模型准似然估计量的一致高斯近似
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事件研究中的时变异质性处理效应
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使用非线性筛方法推断时间序列非参数条件矩限制
This paper studies estimation of and inference on dynamic nonparametric conditional moment restrictions of high dimensional variables for weakly dependent data, where the unknown functions of endogenous variables can be approximated via nonlinear sieves such as neural networks and Gaussian radial bases. The true unknown functions and their sieve approximations are allowed to be in general weighted function spaces with unbounded supports, which is important for time series…
Summary This paper develops a closed-form non-parametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: (i) it has a closed-form expression for all the sieve coefficients,…