Identifying influential observations in a Bayesian multi-level mediation model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10434857" target="_blank" >RIV/00216208:11320/21:10434857 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ewxXcoy4Ec" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ewxXcoy4Ec</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/02664763.2020.1748179" target="_blank" >10.1080/02664763.2020.1748179</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identifying influential observations in a Bayesian multi-level mediation model

Popis výsledku v původním jazyce

Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we eveloped a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated that this approach is useful when interest lies in the impact of individual observations in a subset of model parameters. The method is illustrated on a three-level data set from the field of nursing research, which was previously used to fit a mediation model of patient satisfaction with care. We focused on influential cases on both the second and the third level of the data.

Název v anglickém jazyce

Identifying influential observations in a Bayesian multi-level mediation model

Popis výsledku anglicky

Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we eveloped a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated that this approach is useful when interest lies in the impact of individual observations in a subset of model parameters. The method is illustrated on a three-level data set from the field of nursing research, which was previously used to fit a mediation model of patient satisfaction with care. We focused on influential cases on both the second and the third level of the data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-00015S" target="_blank" >GA19-00015S: Identifikace schémat časového vývoje indikátorů chudoby a sociálního vyčlenění domácností založená na vícerozměrných panelových datech smíšeného typu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Applied Statistics

ISSN

0266-4763

e-ISSN

—

Svazek periodika

48

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

943-960

Kód UT WoS článku

000560591400001

EID výsledku v databázi Scopus

2-s2.0-85083519508