Flexible regression models over river networks

Flexible regression models over river networks

Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of co...

Flexible Regression Models and Relative Forecast Performance ∗

F: Regression Models over Factorized Views

We demonstrate F, a system for building regression models over database views. At its core lies the observation that the computation and representation of materialized views, and in particular of joins, entail non-trivial redundancy that is not necessary for the efficient computation of aggregates used for building regression models. F avoids this redundancy by factorizing data and computation ...

Incremental Maintenance of Regression Models over Joins

This paper introduces a principled incremental view maintenance (IVM) mechanism for in-database computation described by rings. We exemplify our approach by introducing the covariance matrix ring that we use for learning linear regression models over arbitrary equi-join queries. Our approach is a higher-order IVM algorithm that exploits the factorized structure of joins and aggregates to avoid ...

Comparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...