Related Experiment Video
Updated: Sep 8, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
Two new nonparametric kernel distribution estimators based on a transformation of the data
1Laboratoire de Mathématiques et Application, Université de Poitiers, Futuroscope Chasseneuil, France.
Abstract:
In this paper, we propose two kernel distribution estimators based on a data transformation. We study the properties of these estimators and we compare them with two conventional estimators. It appears that with an appropriate choice of the parameters of the two proposed estimators, the convergence rate of two estimators will be faster than that of the two conventional estimators and the Mean Integrated Square Error will be smaller than the two conventional estimators. We corroborate these theoretical results through simulations as well as a real data set.
Related Concept Videos
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Distributions to Estimate Population Parameter
Introduction to Nonparametric Statistics
One of...
Data: Types and Distribution
Distributions in...
Skewness
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
Choosing Between z and t Distribution

