The Expected Parameter Change (EPC) for Local Dependence Assessment in Binary Data Latent Class Models

01/08/2018
by   Daniel L. Oberski, et al.
0

Binary data latent class models crucially assume local independence, violations of which can seriously bias the results. We present two tools for monitoring local dependence in binary data latent class models: the "Expected Parameter Change" (EPC) and a generalized EPC, estimating the substantive size and direction of possible local dependencies. The asymptotic and finite sample behavior of the measures is studied, and two applications to the U.S. Census estimation of Hispanic ethnicity and medical experts' ratings of x-rays demonstrate its value in arriving at a model that balances realism and parsimony.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/10/2022

Rearranged dependence measures

Most of the popular dependence measures for two random variables X and Y...
research
05/18/2022

Dependent Latent Class Models

Latent Class Models (LCMs) are used to cluster multivariate categorical ...
research
07/20/2022

Bias-correction and Test for Mark-point Dependence with Replicated Marked Point Processes

Mark-point dependence plays a critical role in research problems that ca...
research
12/13/2018

Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model

In Bayesian classification, it is important to establish a probabilistic...
research
11/09/2021

Changepoint detection in non-exchangeable data

Changepoint models typically assume the data within each segment are ind...
research
10/17/2016

BET on Independence

We study the problem of nonparametric dependence detection. Many existin...

Please sign up or login with your details

Forgot password? Click here to reset