Robust estimation in controlled branching processes: Bayesian estimators via disparities

Autor:	González, M., Minuesa, C., del Puerto, I., Vidyashankar, A. N.
Rok vydání:	2018
Předmět:	Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Paper and suplementary material
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1802.05917 Zobrazit plný text záznamu View this record from Arxiv