Unbiased Recursive Partitioning: A Conditional Inference Framework
نویسندگان
چکیده
منابع مشابه
Unbiased Recursive Partitioning: A Conditional Inference Framework
Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of exhaustive search procedures usually applied to fit such models have been known for a long time: Overfitting and a selection bias towards covariates with many possible splits or missing values. While pruning procedures are able to solve the overfitting problem, the variable selection bias still ...
متن کاملRecursive partitioning and Bayesian inference on conditional distributions
In this work we introduce a Bayesian framework for nonparametric inference on conditional distributions in the form of a prior called the conditional optional Pólya tree. The prior is constructed based on a two-stage nested procedure, which in the first stage recursively partitions the predictor space, and then in the second generates the conditional distribution on those predictor blocks using...
متن کاملcoin: A Computational Framework for Conditional Inference
The coin package implements a unified approach for conditional inference procedures commonly known as permutation tests. The theoretical basis of design and implementation is the unified framework for permutation tests given by Strasser and Weber (1999). For a very flexible formulation of multivariate linear statistics, Strasser and Weber (1999) derived the conditional expectation and covarianc...
متن کاملRecursive partitioning and multi-scale modeling on conditional densities
Abstract: We introduce a nonparametric prior on the conditional distribution of a (univariate or multivariate) response given a set of predictors. The prior is constructed in the form of a two-stage generative procedure, which in the first stage recursively partitions the predictor space, and then in the second stage generates the conditional distribution by a multi-scale nonparametric density ...
متن کاملSorting by Recursive Partitioning
We present a new O(nlglgn) time sort algorithm that is more robust than O(n) distribution sorting algorithms. The algorithm uses a recursive partition-concatenate approach, partitioning each set into a variable number of subsets using information gathered dynamically during execution. Sequences are partitioned using statistical information computed during the sort for each sequence. _ Space com...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2006
ISSN: 1061-8600,1537-2715
DOI: 10.1198/106186006x133933