8: Non-parametric methods for continuous or ordered data

Regression Modeling for Spherical Data via Non-parametric and Least Square Methods

Introduction Statistical analysis of the data on the Earth's surface was a favorite subject among many researchers. Such data can be related to animal's migration from a region to another position. Then, statistical modeling of their paths helps biological researchers to predict their movements and estimate the areas that are most likely to constitute the presence of the animals. From a geome...

Parametric v non-parametric methods for data analysis.

Continuous data arise in most areas of medicine. Familiar clinical examples include blood pressure, ejection fraction, forced expiratory volume in 1 second (FEV1), serum cholesterol, and anthropometric measurements. Methods for analysing continuous data fall into two classes, distinguished by whether or not they make assumptions about the distribution of the data. Theoretical distributions are ...

Very High Resolution Parametric and Non- Parametric Sartomography Methods for Monitoring Urban Areas Structures

Synthetic Aperture Radar (SAR) is the only way to evaluate deformation of the Earth’s surface from space on the order of centimeters and millimeters due to its coherent nature and short wavelengths. Hence, by this means the long term risk monitoring and security are performed as precisely as possible. Traditional SAR imaging delivers a projection of the 3-D object to the two dimensional (2-D) a...

Scalable Non-Parametric Methods for Large Data Sets

Mining and visualising ordinal data with non-parametric continuous BBNs

Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net(BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of ...