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Modeling Maternal Mortality with Outlier Detection and Space-Time Regression

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Institute for Health Metrics and Evaluation - University of Washington
Institute for Health Metrics and Evaluation - University of Washington
ViajesTecnología
1 de 53
Estimating maternal mortality II November 2, 2010 Christopher J.L. Murray Institute Director
Outline ,[object Object],[object Object]
Outliers: a reality in this dataset ,[object Object],[object Object],[object Object],[object Object]
What’s the problem with outliers? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What is an outlier in this dataset? ,[object Object],[object Object],[object Object]
Outlier detection ,[object Object],[object Object],[object Object]
Approach to outlier detection ,[object Object],[object Object],[object Object],[object Object]
Approach to outlier detection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Approach to outlier detection ,[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object]
Recall the steps in the first stage:
First stage linear regression model   Robust Regression   Coefficient Std. Error Intercept 4.715 0.100 ln(TFR) 1.903 0.022 ln(GDP per capita) -0.511 0.010 Neonatal mortality 13.662 0.721 Education -0.086 0.003 HIV 0.108 0.005 HIV² -0.001 0.000 Age 15-19 -1.176 0.021 Age 20-24 -0.374 0.020 Age 25-29 -0.077 0.020 Age 35-39 -0.165 0.020 Age 40-44 -0.633 0.021 Age 45-49 -1.390 0.025
But, the linear predictions don’t track the data very well
The linear regression isn’t enough ,[object Object],[object Object],[object Object],[object Object]
General Modeling Strategy (Two stages) Linear model estimation Spatial-temporal local regression
Spatial-temporal regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Weights for spatial-temporal regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
HIV counterfactual estimates ,[object Object],[object Object]
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression
Modeling Maternal Mortality with Outlier Detection and Space-Time Regression

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Modeling Maternal Mortality with Outlier Detection and Space-Time Regression

  • 1. Estimating maternal mortality II November 2, 2010 Christopher J.L. Murray Institute Director
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  • 24. Recall the steps in the first stage:
  • 25. First stage linear regression model   Robust Regression   Coefficient Std. Error Intercept 4.715 0.100 ln(TFR) 1.903 0.022 ln(GDP per capita) -0.511 0.010 Neonatal mortality 13.662 0.721 Education -0.086 0.003 HIV 0.108 0.005 HIV² -0.001 0.000 Age 15-19 -1.176 0.021 Age 20-24 -0.374 0.020 Age 25-29 -0.077 0.020 Age 35-39 -0.165 0.020 Age 40-44 -0.633 0.021 Age 45-49 -1.390 0.025
  • 26. But, the linear predictions don’t track the data very well
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  • 28. General Modeling Strategy (Two stages) Linear model estimation Spatial-temporal local regression
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Notas del editor

  1. Each of these weights have parameters that allow us to narrow or widen the windows, as well as alter the decay functions.