Search

Your search keyword '"Tzavidis N"' showing total 2 results
Start Over Author "Tzavidis N" Topic random effects model
2 results on '"Tzavidis N"'

1. Resistance to Outliers of M-Quantile and Robust Random Effects Small Area Models.

Author

Giusti, C., Tzavidis, N., Pratesi, M., and Salvati, N.

Subjects
*ROBUST control, *RANDOM effects model, *APPLICATION software, *PARAMETER estimation, *STATISTICAL models, *MAXIMUM likelihood statistics, *RESEARCH methodology
Abstract

The presence of outliers is a common feature in real data applications. It has been well established that outliers can severely affect the parameter estimates of statistical models, for example, random effects models, which can in turn affect the small area estimates produced using these models. Two outlier robust methodologies have been recently proposed in the small area literature. These are the M-quantile approach and the robust random effects approach. The M-quantile and robust random effects approaches are two distinct outlier robust small area methods and a comparison between these two methodologies is required. The present paper sets to fulfill this goal. Using model-based simulations and showing an application to real income data we examine how the alternative small area methodologies compare. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
View/download PDF

2. Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression

Author

Nicola Salvati, Nikos Tzavidis, Timo Schmid, Riccardo Borgoni, Paola Del Bianco, Borgoni, R, Del Bianco, P, Salvati, N, Schmid, T, and Tzavidis, N

Subjects
Statistics and Probability, quantile regression, Epidemiology, Computer science, Biostatistics, 01 natural sciences, Hierarchical data, quantile regression, robust estimation, Rotterdam Symptom Checklist, Phase III study, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Quality of life (healthcare), Health Information Management, Statistics, Antineoplastic Combined Chemotherapy Protocols, Econometrics, multilevel modelling, Humans, Phase III study, 030212 general & internal medicine, Longitudinal Studies, Prospective Studies, 0101 mathematics, Melanoma, Hierarchical data, Models, Statistical, Rotterdam Symptom Checklist, Regression analysis, Conditional probability distribution, robust estimation, Random effects model, Regression, Quantile regression, Quality of Life, Regression Analysis, Self Report, Regression diagnostic, 300 Sozialwissenschaften::330 Wirtschaft, Quantile
Abstract

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results