Evaluating Goodness-of-Fit Measures for Synthetic Microdata

Voas, David; Williamson, Paul

doi:10.1080/13615930120086078

Cited by 89 publications

(70 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other authors are also seeking to address this gap in the literature and there have been some useful recent papers in this regard. (Voas and Williamson 2001) provide a useful synopsis of the 'ideal' parameters that we require validation to cover and in particular an intuitive consideration of what levels of error or variance are acceptable in different circumstances. Scarborough et al (2009), Smith et al (2009, and Birkin and Clarke (2010) describe their processes for validation particular spatial microsimulation models, as does Anderson (2007;, who additionally emphasises the need for further validation work especially considering the use of building confidence intervals to assess goodness of fit.…”

Section: Discussionmentioning

confidence: 99%

Internal and External Validation of Spatial Microsimulation Models: Small Area Estimates of Adult Obesity

Edwards

Clarke

Thomas

et al. 2010

Appl. Spatial Analysis

View full text Add to dashboard Cite

Spatial microsimulation models can be used to estimate previously unknown data at the micro-level, although validation of these models can be challenging. This paper seeks to describe an approach to validation of these models. Obesity data in adults were estimated at the small area level using a static, deterministic, spatial microsimulation model called SimObesity. This model utilised both Census 2001 data and the Health Survey for England for 2004-2006. Regression analysis was used to identify the covariates that were the strongest predictors of obesity and these were used as the model input variables. The model was calibrated using regression and equal variance t-tests. Two methods of external validation were undertaken; aggregating obesity data to a coarser geographical level at which obesity data was available, and secondly using small area level cancer data for tumour sites known to be correlated to obesity. The output obesity data were mapped and statistically significant hot (cold) spots of high (low) prevalence of obesity identified. Both internal and external validation showed low errors, suggesting this was a satisfactory simulation. Statistically significant hot and cold spots of (simulated) obesity prevalence existed, even after adjusting for age. This paper emphasises three steps to validation of spatial microsimulation models: 1. Accurate simulations require strong correlations between the input and output variables; 2. It is essential to internally validate the models; 3. Use all means possible to externally validate the model.

show abstract

Section: Discussionmentioning

confidence: 99%

Internal and External Validation of Spatial Microsimulation Models: Small Area Estimates of Adult Obesity

Edwards

Clarke

Thomas

et al. 2010

Appl. Spatial Analysis

View full text Add to dashboard Cite

show abstract

“…a We also tested these variables in a non linear way, but this did not improve the model. An often used measure to evaluate the outcomes of simulation models is the standardized absolute error measure (SAE) as described by Voas and Williamson (2001). The measure sums the discrepancies (TAE ¼ total absolute error) divided by the number of expected farms:…”

Section: Simulating the Farmersmentioning

confidence: 99%

Determinants of off-farm income and its local patterns: A spatial microsimulation of Dutch farmers

Leeuwen

Dekkers

2013

Journal of Rural Studies

View full text Add to dashboard Cite

“…Table 7 in the appendix lists error measurements for testing the performance of synthetic population techniques. Voas and Williamson (2001) proposed some criteria on the decision of error measurements.…”

Section: Validation Stepmentioning

confidence: 99%