Florian Gerber
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View article: Large-Scale Spatial Prediction by Scalable Geographically Weighted Regression: Comparative Study (Short Paper)
Large-Scale Spatial Prediction by Scalable Geographically Weighted Regression: Comparative Study (Short Paper) Open
Although the scalable geographically weighted regression (GWR) has been developed as a fast regression approach modeling non-stationarity, its potential on spatial prediction is largely unexplored. Given that, this study applies the scalab…
View article: Fast covariance parameter estimation of spatial Gaussian process models using neural networks
Fast covariance parameter estimation of spatial Gaussian process models using neural networks Open
Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often, a few parameters are sufficient to parameterize the covariance …
View article: florafauna/optimParallel-python: test Zenodo
florafauna/optimParallel-python: test Zenodo Open
A parallel computing interface to the L-BFGS-B optimizer
View article: Parallel cross-validation: a scalable fitting method for Gaussian\n process models
Parallel cross-validation: a scalable fitting method for Gaussian\n process models Open
Gaussian process (GP) models are widely used to analyze spatially referenced\ndata and to predict values at locations without observations. In contrast to\nmany algorithmic procedures, GP models are based on a statistical framework,\nwhich…
View article: optimParallel: An R Package Providing a Parallel Version of the L-BFGS-B Optimization Method
optimParallel: An R Package Providing a Parallel Version of the L-BFGS-B Optimization Method Open
The R package optimParallel provides a parallel version of the L-BFGS-B optimization method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can signific…
View article: optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim()
optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim() Open
The R package optimParallel provides a parallel version of the gradient-based optimization methods of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can s…
View article: optimParallel: an R Package Providing Parallel Versions of the\n Gradient-Based Optimization Methods of optim()
optimParallel: an R Package Providing Parallel Versions of the\n Gradient-Based Optimization Methods of optim() Open
The R package optimParallel provides a parallel version of the gradient-based\noptimization methods of optim(). The main function of the package is\noptimParallel(), which has the same usage and output as optim(). Using\noptimParallel() ca…
View article: Predicting missing values in spatio-temporal remote sensing data
Predicting missing values in spatio-temporal remote sensing data Open
Continuous, consistent, and long time-series from remote sensing are essential to monitoring changes on Earth's surface. However, analyzing such data sets is often challenging due to missing values introduced by cloud cover, missing orbits…
View article: Methods for Analyzing Large Spatial Data: A Review and Comparison
Methods for Analyzing Large Spatial Data: A Review and Comparison Open
The Gaussian process is an indispensable tool for spatial analysts. The onset of the data era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives …
View article: A Case Study Competition Among Methods for Analyzing Large Spatial Data
A Case Study Competition Among Methods for Analyzing Large Spatial Data Open
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alte…
View article: Statistical tools to model space-time data with a focus on biodiversity applications
Statistical tools to model space-time data with a focus on biodiversity applications Open
Statistische Modelle sind wichtige Hilfsmittel um Raum-Zeit-Daten wie Satellitenbilder und ökologische Feldmessungen zu analysieren und interpretieren. Dabei verunmöglichen komplexe Datenstrukturen und immer grössere Datenmengen den Gebrau…
View article: Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI 3g data
Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI 3g data Open
Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and mem…
View article: <b>gsbDesign</b>: An<i>R</i>Package for Evaluating the Operating Characteristics of a Group Sequential Bayesian Design
<b>gsbDesign</b>: An<i>R</i>Package for Evaluating the Operating Characteristics of a Group Sequential Bayesian Design Open
The R package gsbDesign provides functions to evaluate the operating characteristics of Bayesian group sequential clinical trial designs. More specifically, we consider clinical trials with interim analyses, which compare a treatment with …