# Robust estimation in controlled branching processes: Bayesian estimators via disparities

Source: [Robust estimation in controlled branching processes: Bayesian estimators via disparities](<https://www.sota2.com/research/paper/68fa7d861e209131cea93781>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:23:26.944Z

Authors: M. Gonz\\'alez, C. Minuesa, I. del Puerto, A.N. Vidyashankar

Published: 2018-02-16T12:57:01.000Z

## Abstract

This paper is concerned with Bayesian inferential methods for data from controlled branching processes that account for model robustness through the use of disparities. Under regularity conditions, we establish that estimators built on disparity-based posterior, such as expectation and maximum a posteriori estimates, are consistent and efficient under the posited model. Additionally, we show that the estimates are robust to model misspecification and presence of aberrant outliers. To this end, we develop several fundamental ideas relating minimum disparity estimators to Bayesian estimators built on the disparity-based posterior, for dependent tree-structured data. We illustrate the methodology through a simulated example and apply our methods to a real data set from cell kinetics.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1802.05917>)

[PDF](<https://arxiv.org/pdf/1802.05917.pdf>)