Density Estimation and Random Number Generation with Distribution Element Trees
Are All Columns in a Matrix Equal?
Pairwise Mutual Independence Test
Goodness-of-Fit Test for Linear Distributions
(Contingency) Tables for Chi-square Tests
Goodness-of-Fit Test for Uniform Distribution
Draw Contours in a Rectangle
Split a Distribution Element
Distribution Element Tree (DET) Construction
Identify Tree Leafs Intersected by Condition(s)
Extract Distribution Element Characteristics
Extract Leaf Elements from Distribution Element Tree
Density Estimation Based on Distribution Element Trees
Bootstrapping from Distribution Element Trees
Density Estimation for Univariate Data Based on Distribution Element T...
Density Estimation for Bivariate Data Based on Distribution Element Tr...
Distribution Element Trees for Density Estimation and Bootstrapping
Determine Split Dimension(s)
Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.