Detecting influential observations in nonlinear regression modeling of groundwater flow

Detecting influential observations in Kernel PCA

Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity of Kernel PCA is characterized by calculating the influence function. A robust Kernel PCA method is pro...

Detecting influential observations in principal components and common principal components

Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis distance based on several robust estimates (l...

Modeling of monthly flow duration curve using nonlinear regression method for un-gauged watersheds of Ardabil Province

The flow duration curve (FDC) represents the frequency distribution of water flow over a period of time, which is widely used in hydrology to evaluate different ranges of river water flow applications. Therefore, it is necessary to develop a suitable estimation model and method in un-gauged watersheds. To this end, in the present study, a modeling method based on nonlinear regression, for the p...

Detecting Nonlinear Causality via Nonlinear Modeling

We analyze a set of complex time series from the view point of nonlinear causality. The mathematical background for analyzing time series is an extension of embedding theories of autonomous systems to an input{output system. We consider that the existence of nonlinear causality can be detected by nonlinear predictability of input and output sequences. Several numerical examples are given for co...

Integration of Groundwater Flow Modeling and GIS