Electronic Journal of Statistics

Monte Carlo modified profile likelihood in models for clustered data

Claudia Di Caterina, Giuliana Cortese, and Nicola Sartori

Full-text: Open access

Abstract

The main focus of the analysts who deal with clustered data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed effects formulation is preferred and the total number of clusters is large relative to the single-group sizes, classical frequentist techniques relying on the profile likelihood are often misleading. The use of alternative tools, such as modifications to the profile likelihood or integrated likelihoods, for making accurate inference on a parameter of interest can be complicated by the presence of nonstandard modelling and/or sampling assumptions. We show here how to employ Monte Carlo simulation in order to approximate the modified profile likelihood in some of these unconventional frameworks. The proposed solution is widely applicable and is shown to retain the usual properties of the modified profile likelihood. The approach is examined in two instances particularly relevant in applications, i.e. missing-data models and survival models with unspecified censoring distribution. The effectiveness of the proposed solution is validated via simulation studies and two clinical trial applications.

Article information

Source
Electron. J. Statist., Volume 13, Number 1 (2019), 432-464.

Dates
Received: December 2017
First available in Project Euclid: 12 February 2019

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1549962031

Digital Object Identifier
doi:10.1214/19-EJS1532

Subjects
Primary: 62G20: Asymptotic properties asymptotic properties

Keywords
Censored data nonignorable missing data nuisance parameter profile likelihood two-index asymptotics

Rights
Creative Commons Attribution 4.0 International License.

Citation

Di Caterina, Claudia; Cortese, Giuliana; Sartori, Nicola. Monte Carlo modified profile likelihood in models for clustered data. Electron. J. Statist. 13 (2019), no. 1, 432--464. doi:10.1214/19-EJS1532. https://projecteuclid.org/euclid.ejs/1549962031


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