Niels Stender
Biographic Data
| ID | 6852166 |
|---|---|
| NAME | Niels Stender |
| GIVEN NAMES | Niels |
| FAMILY NAME | Stender |
| SIGNATURE | STENDER N |
| AFFILIATIONS | InnospeXion (Denmark) |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 1 |
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions.…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions.…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty…
Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions.…
Computer Science (2 works) · Econometrics (2 works) · Economic growth (2 works) · Economics (2 works) · Income, Poverty, and Inequality (2 works) · Mathematics (2 works) · Poverty (2 works) · Random forest (2 works) · Statistics (2 works) · Agricultural risk and resilience (1 work)