Pages that link to "Item:Q2283052"
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The following pages link to Improving precipitation forecasts using extreme quantile regression (Q2283052):
Displaying 17 items.
- Statistical downscaling of extreme precipitation events using extreme value theory (Q549636) (← links)
- Synoptic airflow and UK daily precipitation extremes: development and validation of a vector generalized linear model (Q549640) (← links)
- Regression model for generating time series of daily precipitation amounts for climate change impact studies (Q1360308) (← links)
- INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (Q1792632) (← links)
- Prediction of extremal precipitation by quantile regression forests: from SNU multiscale team (Q1792636) (← links)
- The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles (Q1792637) (← links)
- Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models (Q2073711) (← links)
- Scoring predictions at extreme quantiles (Q2106823) (← links)
- A Multivariate Regression Reconstruction of the Quasi-Global Annual Precipitation on 1-Deg Grid From 1900 To 2015 (Q4622899) (← links)
- A Bayesian semi-parametric mixture model for bivariate extreme value analysis with application to precipitation forecasting (Q5155203) (← links)
- Gradient boosting for extreme quantile regression (Q6144813) (← links)
- Optimal weighted pooling for inference about the tail index and extreme quantiles (Q6201851) (← links)
- Generalized Pareto regression trees for extreme event analysis (Q6601112) (← links)
- Prediction of Extremal Expectile Based on Regression Models With Heteroscedastic Extremes (Q6620881) (← links)
- Extremal Random Forests (Q6651413) (← links)
- Online prediction of extreme conditional quantiles via B-spline interpolation (Q6657809) (← links)
- Neural networks for extreme quantile regression with an application to forecasting of flood risk (Q6665468) (← links)
