Density estimation with quadratic loss: a confidence intervals method

Density estimation with quadratic loss: a confidence intervals method

Confidence intervals for kernel density estimation

This article describes asciker and bsciker, two programs that enrich the possibility for density analysis using Stata. asciker and bsciker compute asymptotic and bootstrap confidence intervals for kernel density estimation, respectively, based on the theory of kernel density confidence intervals estimation developed in Hall (1992b) and Horowitz (2001). asciker and bsciker allow several options ...

Estimation with Quadratic Loss

It has long been customary to measure the adequacy of an estimator by the smallness of its mean squared error. The least squares estimators were studied by Gauss and by other authors later in the nineteenth century. A proof that the best unbiased estimator of a linear function of the means of a set of observed random variables is the least squares estimator was given by Markov [12], a modified ...

Parameter estimation without confidence intervals?

We greatly appreciate the comments contributed by one of the peer reviewers. Apparently, his comments have clearly sorted out the limitations our article involves. We recognize all the limitations and still, we believe the Diagnosis Procedure Combination (DPC) system in Japan is worth being aware of as a novel medical database in Asia. As for the first point, the DPC database only involves 65.1...

Parameter estimation without confidence intervals?

Fukuhara and Hori’s article [1] reported the possibility of calculating the incidence of spontaneous intracerebral hemorrhage (sICH) using publicly accessible data in Japan. If the method were valid and satisfied statistical assumptions, overcoming the limitation of the source of the data, known as Diagnosis Procedure Combination (DPC) data, this suggestion could be an innovative method. Becaus...