Estimators and confidence intervals for plant area density at voxel scale with T-LiDAR

Consistent Confidence Intervals for Maximum Pseudolikelihood Estimators

Maximum pseudolikelihood estimation (MPLE) constitutes a computationally efficient and easily implemented alternative to maximum likelihood and simulationbased methods. The MPLE has been shown to be consistent and asymptotically normally distributed in a number of interesting cases. However, the coverage probability of the conventional confidence interval for the MPLE is biased downward. We pro...

Confidence Intervals Based on Robust Estimators

Area specific confidence intervals for a small area mean under the Fay-Herriot model

‎Small area estimates have received much attention from both private and public sectors due to the growing demand for effective planning of health services‎, ‎apportioning of government funds and policy and decision making‎. ‎Surveys are generally designed to give representative estimates at national or district level‎, ‎but estimates of variables of interest are oft...

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 ...

A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR

Forest canopy structure has long been known to be a major driver of the processes regulating the exchange of CO2 and water vapour between terrestrial ecosystems and the atmosphere. It is also an important driver of terrestrial vegetation dynamics. Information about fine-scale ecosystem structure is needed to better understand and predict how terrestrial ecosystems respond to and affect environm...