Scanning-Fit Analysis of fs-Spectroscopic Data

Sophisticated algorithms of analysis of spectroscopic data

The paper presents an overview of existing and new algorithms for the processing of spectroscopic data. These include algorithms for background elimination, for separation of peak containing regions from peak-free regions, deconvolution and identification of information carrier objects. XII Advanced Computing and Analysis Techniques in Physics Research Erice, Italy 3-7 November, 2008

Multivariate Statistical Analysis of Spectroscopic Data

This paper focuses on the application and comparison of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) using two generic artificially created datasets. PCA and ICA are assessed in terms of their abilities to infer reference spectra and to estimate relative concentrations of the constituent compounds present in the analysed samples. The results show that ICA outperfo...

Assessing Approximate Fit in Categorical Data Analysis.

A family of Root Mean Square Error of Approximation (RMSEA) statistics is proposed for assessing the goodness of approximation in discrete multivariate analysis with applications to item response theory (IRT) models. The family includes RMSEAs to assess the approximation up to any level of association of the discrete variables. Two members of this family are RMSEA2, which uses up to bivariate m...

Scanning Near-field Raman Spectroscopic Microscope

Assessment of Goodness of Fit Methods in Determining the Best Regional Probability Distribution of Rainfall Data

One of the most important problems in time series analysis of stream flow and rainfall data in an area is selecting the best probability distribution. Since the rainfall stations are associated and correlated with each other, so statistical analysis of the station data seamlessly are very important. Therefore, the first step in data analysis, is selecting the prevailing probability distribution...