Implementing SAE Techniques to Predict Global Spectacles Needs
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE credits
Student thesis
Abstract [en]
This study delves into the application of Small Area Estimation (SAE) techniques to enhance the accuracy of predicting global needs for assistive spectacles. By leveraging the power of SAE, the research undertakes a comprehensive exploration, employing arange of predictive models including Linear Regression (LR), Empirical Best Linear Unbiased Prediction (EBLUP), hglm (from R package) with Conditional Autoregressive (CAR), and Generalized Linear Mixed Models (GLMM). At last phase,the global spectacle needs’ prediction includes various essential steps such as random effects simulation, coefficient extraction from GLMM estimates, and log-linear modeling. The investigation develops a multi-faceted approach, incorporating area-level modeling, spatial correlation analysis, and relative standard error, to assess their impact on predictive accuracy. The GLMM consistently displays the lowest Relative Standard Error (RSE) values, almost close to zero, indicating precise but potentially overfit results. Conversely, the hglm with CAR model presents a narrower RSE range, typically below 25%, reflecting greater accuracy; however, it is worth noting that it contains a higher number of outliers. LR illustrates a performance similar to EBLUP, with RSE values reaching around 50% in certain scenarios and displaying slight variations across different contexts. These findings underscore the trade-offs between precision and robustness across these models, especially for finer geographical levels and countries not included in the initial sample.
Place, publisher, year, edition, pages
2023.
Keywords [en]
small area estimation, area-level model, empirical best linear unbiased prediction (EBLUP), generalized linear mixed models, Conditional Autoregressive, spatial correlation, spectacle needs, assistive products, auxiliary data, hglm, relative standard error, simulation
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:du-47047OAI: oai:DiVA.org:du-47047DiVA, id: diva2:1800374
Subject / course
Microdata Analysis
2023-09-262023-09-26Bibliographically approved