| Title | Date | Views | Brief Description |
|---|
| Circulant matrices on global data analysis |
2017 |
1078 |
In this thesis, we investigate the unbiasedness of the commonly used covariance and variogram estimators when the underlying process is assumed to be stationary on the circle or axially symmetric on the sphere. We represent both estimators as a quadr... |
| Spectral estimation for random processes with stationary increments |
2018 |
1905 |
In studying a stationary random process on R, the covariance function is commonlyused to characterize the second-order spatial dependency. Through the inversionof Fourier transformation, its corresponding spectral density has been widely usedto descr... |
| Data generation and estimation for axially symmertic processes on the sphere |
2016 |
1223 |
Data from global networks and satellite sensors have been used to monitor a wide array of processes and variables, such as temperature, precipitation, etc. The modeling and analysis of global data has been extensively studied in the realm of spatial ... |
| Penalized weighted methods for robust offline and online learning |
2024 |
31 |
Data contamination is a prevalent issue in real-life data sets, with approximately 10% of observations being affected, as noted by Hampel et al. in 1986 [32]. The presence of data contamination undermines the assumptions underlying existing machine l... |
| The Wavelet-Galerkin method on global random processes |
2021 |
285 |
One of the main usages of covariance function or kernel is to capture the spatial or temporal dependency of a random process or random field. Covariance functions have been widely used in many areas such as environmental statistics, economics, machin... |
| Spatial prediction for axially symmetric process on spheres |
2022 |
131 |
Spatial prediction, or so-called kriging, is one of the ultimate goals in spatial data analysis. The basic idea of kriging is to use the values of a geographic variable at some locations to estimate the value(s) that are unknown at other locations. I... |