Kernel estimation of the conditional density under a censorship model
Kernel estimation of the conditional density under a censorship model is a scholarly work, published in 2019 in ''Statistics and Probability Letters''. The main subjects of the publication include regularization, skew normal distribution, kernel, statistics, conditional expectation, econometrics, kernel regression, kernel method, mean squared error, rate of convergence, kernel density estimation, Variable kernel density estimation, conditional variance, estimator, conditional probability distribution, density estimation, causal inference, applied mathematics, and mathematics. The authors establish the mean square convergence, with rate, for an introduced kernel estimator of the conditional density function when the response variable is twice censored.