Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …

As high-dimensional and high-frequency data are being collected on a large scale, the
development of new statistical models is being pushed forward. Functional data analysis …

Coal core samples and well log data of five exploratory wells of Korba Coalfield (CF), India
have been used for prediction of coal facies. The Indian non-coking coal lithofacies are …

Sign, Wilcoxon and Mann-Whitney tests are nonparametric methods in one or two-sample
problems. The nonparametric methods are alternatives used for testing hypothesis when the …

We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …

Regionalizing precipitation allows capturing regional-scale variability into manageable
smaller regions (Abatzoglou et al., 2009), which is useful to make information-based …

Understanding the spatio-temporal variability of the solar resource is crucial to effectively
support solar power utilization. Unfortunately, long-term and high-resolved measurements of …

Waterflooding is a significantly important process in the life of an oil field to sweep previously
unrecovered oil between injection and production wells and maintain reservoir pressure at …

Detecting change-points in multivariate settings is usually carried out by analyzing all
marginals either independently, via univariate methods, or jointly, through multivariate …

An income distribution describes how an entity's total wealth is distributed amongst its
population. A problem of interest to regional economics researchers is to understand the …