Recent Advances In Theory And Methods For The Analysis Of High Dimensional And High Frequency Financial Data
Download Recent Advances In Theory And Methods For The Analysis Of High Dimensional And High Frequency Financial Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Recent Advances In Theory And Methods For The Analysis Of High Dimensional And High Frequency Financial Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data
Recently, considerable attention has been placed on the development and application of tools useful for the analysis of the high-dimensional and/or high-frequency datasets that now dominate the landscape. The purpose of this Special Issue is to collect both methodological and empirical papers that develop and utilize state-of-the-art econometric techniques for the analysis of such data.
Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data
Recently, considerable attention has been placed on the development and application of tools useful for the analysis of the high-dimensional and/or high-frequency datasets that now dominate the landscape. The purpose of this Special Issue is to collect both methodological and empirical papers that develop and utilize state-of-the-art econometric techniques for the analysis of such data.
Clustering High--Dimensional Data
This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.