Smooth estimators of distribution and density functions
Computational Statistics & Data Analysis - Second special issue on optimization techniques in statistics
Nonparametric econometrics
Beta kernel estimators for density functions
Computational Statistics & Data Analysis
Influence diagnostics in log-Birnbaum-Saunders regression models with censored data
Computational Statistics & Data Analysis
Lifetime analysis based on the generalized Birnbaum-Saunders distribution
Computational Statistics & Data Analysis
An adjusted boxplot for skewed distributions
Computational Statistics & Data Analysis
An R implementation for generalized Birnbaum-Saunders distributions
Computational Statistics & Data Analysis
Introduction to Nonparametric Estimation
Introduction to Nonparametric Estimation
Nonparametric density estimation for positive time series
Computational Statistics & Data Analysis
The Birnbaum-Saunders autoregressive conditional duration model
Mathematics and Computers in Simulation
Estimation of extreme percentiles in Birnbaum-Saunders distributions
Computational Statistics & Data Analysis
Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance
Applied Stochastic Models in Business and Industry
Shape and change point analyses of the Birnbaum-Saunders-t hazard rate and associated estimation
Computational Statistics & Data Analysis
Nonparametric kernel density estimation near the boundary
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The kernel method is a nonparametric procedure used to estimate densities with support in R. When nonnegative data are modeled, the classical kernel density estimator presents a bias problem in the neighborhood of zero. Several methods have been developed to reduce this bias, which include the boundary kernel, data transformation and reflection methods. An alternative proposal is to use kernel estimators based on distributions with nonnegative support, as is the case of the Birnbaum-Saunders (BS), gamma, inverse Gaussian and lognormal models. Generalized BS (GBS) distributions have received considerable attention, due to their properties and their flexibility in modeling different types of data. In this paper, we propose, characterize and implement the kernel method based on GBS distributions to estimate densities with nonnegative support. In addition, we provide a simple method to choose the corresponding bandwidth. In order to evaluate the performance of these new estimators, we conduct a Monte Carlo simulation study. The obtained results are illustrated by analyzing financial real data.