A shortt of a one dimensional probability distribution is defined to be an interval which has at least probability t and millimallength. The length of a shortt and its obvious estimator are significant measures of scale of a distribution and the corresponding random sample, respectively. In this note a non-parametric asymptotic confidence interval for the length of the (uniqueness is assumed) shortt is established in the random censorship from the right model. The estimator of the length of the shortt is based on the product-limit (PL) estimator of the unknown distribution function. The proof of the result mainly follows from an appropriate combination of the Glivenko-Cantelli theorem and the functional central limit theorem for the PL esti...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
The paper presents the asymptotic properties of semiparametric estimates of the quantile function an...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
A shortt of a one dimensional probability distribution is defined to be an interval which has at lea...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
We derive asymptotic confidence bands for the quantile function F1 in the random censorship model. A...
The paper presents the asymptotic properties of semiparametric estimates of the quantile function an...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...
Statistical inference about parameters should depend on raw data only through sufficient statistics—...