A general approach to categorizing a continuous scale according to an ordinal outcome

DOI10.1016/j.jspi.2015.12.006zbMath1334.62197OpenAlexW2219533367WikidataQ36630132 ScholiaQ36630132MaRDI QIDQ256464

Limin Peng, Amita K. Manatunga, Ming Wang, AKM Fazlur Rahman, Ying Guo

Publication date: 9 March 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4770918

zbMATH Keywords

empirical processassociationsubsamplingagreementM-estimationnon-smooth objective function

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)

Related Items (1)

A smooth nonparametric approach to determining cut-points of a continuous scale

Cites Work

• Unnamed Item
• Unnamed Item
• Cube root asymptotics
• Inconsistency of bootstrap: the Grenander estimator
• Subsampling
• Model based bootstrap methods for interval censored data
• Large sample confidence regions based on subsamples under minimal assumptions
• Weak convergence and empirical processes. With applications to statistics
• Introduction to empirical processes and semiparametric inference
• Density estimation
• Estimation of the mode
• Cutpoint Selection for Categorizing a Continuous Predictor
• Estimation of the Mixing Proportion in a Mixture of Two Normal Distributions from Simple, Rapid Measurements
• On the Bootstrap of the Maximum Score Estimator
• On the bootstrap in cube root asymptotics
• Onmout ofnBootstrapping for Nonstandard M-Estimation With Nuisance Parameters
• A NEW MEASURE OF RANK CORRELATION
• Subsampling inference in cube root asymptotics with an application to Manski's maximum score estimator.

This page was built for publication: A general approach to categorizing a continuous scale according to an ordinal outcome