Logspline Density Estimation for Censored Data
针对右删失、左删失或区间删失数据，提出一种全自动的对数样条密度估计方法，结合最大似然、逐步删除节点和AIC/BIC准则，并给出基于S语言的用户界面。
Abstract Logspline density estimation is developed for data that may be right censored, left censored, or interval censored. A fully automatic method, which involves the maximum likelihood method and may involve stepwise knot deletion and either the Akaike information criterion (AIC) or Bayesian information criterion (BIC), is used to determine the estimate. In solving the maximum likelihood equations, the Newton–Raphson method is augmented by occasional searches in the direction of steepest ascent. Also, a user interface based on S is described for obtaining estimates of the density function, distribution function, and quantile function and for generating a random sample from the fitted distribution.