Sökning: "random effects Model"
Visar resultat 1 - 5 av 138 uppsatser innehållade orden random effects Model.
1. Collaborative Learning in an Immersive Virtual Environment: The Effects of Context and Retrieval Practice
Master-uppsats, Lunds universitet/Institutionen för psykologiSammanfattning : The accessibility of Virtual Reality (VR) enables the investigation of desirable difficulties originating from memory research with increased ecological validity. The two desirable difficulties include contextual variation and retrieval practice. LÄS MER
2. PUBLIC HEALTH EXPENDITURE AND HEALTH OUTCOMES : AN EMPIRICAL STUDY ANALYSIS
Magister-uppsats, Umeå universitet/NationalekonomiSammanfattning : Public health expenditure plays a critical role in enhancing the health and well-being of a nation's population. Despite its significance, there is limited research exploring the link between public health expenditure and health outcomes. LÄS MER
3. Exploring the Influence of Firm Size on Leverage Evidence from OMX Stockholm Small Cap
Kandidat-uppsats,Sammanfattning : This study examines the relationship between leverage and firm size for a sample of 83 firms listed on OMX Stockholm Small Cap between 2018 and 2022. Additionally, it explores the impact of the Covid-19 crisis on this relationship through quarters experiencing negative GDP growth. LÄS MER
4. Unraveling the Complexities of Energy Poverty in Germany- A Comparative Analysis of Determinants, Dynamics, and Indicators
Master-uppsats, Göteborgs universitet/Graduate SchoolSammanfattning : This master thesis investigates the determinants and dynamics of energy poverty in Germany based on different calculation methods. Using a dynamic random-effects probit model, socioeconomic factors associated with a household’s risk of experiencing energy poverty are identified. LÄS MER
5. Data Driven Augmentation for Deep Learning Applications
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Deep learning models are achieving remarkable performance on numerous tasks across various fields and applications. However, current deep learning models often suffer from overfitting and are therefore heavily reliant on regularization techniques such as data augmentation. LÄS MER