Nonlinear mixed effects models for longitudinal DATA
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The main objectives of this master thesis are to explore the effectiveness of nonlinear mixed effects model for longitudinal data. Mixed effect models allow to investigate the nature of relationship between the time-varying covariates and the response while also capturing the variations of subjects. I investigate the robustness of the longitudinal models by building up the complexity of the models starting from multiple linear models and ending up with additive nonlinear mixed models. I use a dataset where firms’ leverage are explained by four explanatory variables in addition to a grouping factor that is the firm factor. The models are compared using comparison statistics such as AIC, BIC and by a visual inspection of residuals. Likelihood ratio test has been used in some nested models only. The models are estimated by maximum likelihood and restricted maximum likelihood estimation. The most efficient model is the nonlinear mixed effects model which has lowest AIC and BIC. The multiple linear regression model failed to explain the relation and produced unrealistic statistics
Place, publisher, year, edition, pages
2015. , 42 p.
Longitudinal data, machine learning techniques, splines, mixed effects, leverage.
Economics and Business
IdentifiersURN: urn:nbn:se:liu:diva-120579ISRN: LIU-IDA/STAT-A--15/009—SEOAI: oai:DiVA.org:liu-120579DiVA: diva2:846642
Subject / course
2015-06-03, Alan Turing, Linköpings Universitiet, Campus US, Hus E, 1 trappa (plan 3), Linköping, 16:10 (English)
Villani, Mattias, Professor of Statistics
Nordgaard, Anders, Reader and Forensic specialist in Statistics