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Electronic absorption spectroscopy was used to measure the molecular association of copper phthalocyanine tetrasulfonate in micellar solutions, a microemulsion made with cationic surfactant, and homogeneous solvents. Analysis of absorbance versus concentration data using a multiple-aggregation model and non-linear regression analysis gave values of association constants, molar absorptivities and estimates of average aggregation number. Microemulsions and aqueous micellar solutions made with alkylammonium surfactants inhibited aggregation, probably because of interactions between the phthalocyanine sulfonate groups and the cationic surfactant head groups at interfacial surfaces. Similar aggregation behavior was observed previously in multiple-bilayer films of cationic surfactants. Water and aqueous solutions containing tetraethylammonium bromide or anionic SDS micelles provide environments facilitating extensive aggregation of Cu^IIPcTS⁴⁻. The major species are dimers in water and acetonitrile/water, but the formation of higher aggregates is promoted by addition of SDS or TEAB. Aprotic organic solvents provide environments intermediate between these two extremes, giving relatively large aggregation numbers (i.e. five to seven) but smaller association constants than aqueous media not containing cationic surfactants.

The main purpose of this introductory chapter is to give an overview of the following 130 papers, which discuss financial econometrics, mathematics, statistics, and machine learning. There are eight sections in this introductory chapter. Section 1 is the introduction, Section 2 discusses financial econometrics, Section 3 explores financial mathematics, and Section 4 discusses financial statistics. Section 5 of this introductory chapter discusses financial technology and machine learning, Section 6 explores applications of financial econometrics, mathematics, statistics, and machine learning, and Section 7 gives an overview in terms of chapter and keyword classification of the handbook. Finally, Section 8 is a summary and includes some remarks.

The Bayesian semi-parametric curve-fitting procedure, based on a new, flexible, fast, and efficient mixture analysis idea assuming unknown number of components is applied to analyze SDSS data to study the relationship between apparent magnitude and redshift for quasars and the possibility of clusterings. The cosmological data analysis provides strong evidence against linear relationship, and clearly indicates the possibility of clustering of quasars specially at high redshift. This sheds new light not only on the issue of evolution, existence of acceleration or deceleration and environment around quasars (say, radio loud and radio quiet) at high redshift but also help us to estimate the cosmological parameters related to acceleration or decceleration.