Similar Search Results

Topics in functional data analysis with biological applications

Carroll, Raymond J. (contributor); Hsing, Tailen (contributor) - 2006

Functional data analysis (FDA) is an active field of statistics, in which the primary subjectsin the study are curves. My dissertation consists of two innovative applications offunctional data analysis in biology. The data that motivated the research broadened thescope of FDA and demanded new...

Persistent link: https://www.econbiz.de/10009464813

Functional data analysis with applications in finance

Benko, Michal - 2007

An vielen verschiedenen Stellen der angewandten Statistik sind die zu untersuchenden Objekte abhängig von stetigen Parametern. Typische Beispiele in Finanzmarktapplikationen sind implizierte Volatilitäten, risikoneutrale Dichten oder Zinskurven. Aufgrund der Marktkonventionen sowie weiteren...

Persistent link: https://www.econbiz.de/10009467092

Functional Principal Components Analysis, Implementation and Applications

Benko, Michael - 2004

This master thesis discusses selected topics of Functional Data Analysis (FDA). FDA deals with the random variables (and process) with realizations in the (smooth) functional space. The first part of this thesis introduces the basic assumptions, notation and ideas of FDA, here we will mainly...

Persistent link: https://www.econbiz.de/10009467198

Estimation of Beta in a Simple Functional Capital Asset Pricing Model for High Frequency US Stock Data

Zhang, Yan - 2011

This project applies the methods of functional data analysis (FDA) to intra-daily returns of US corporations. It focuses on an extension of the Capital Asset Pricing Model (CAPM) to such returns. The CAPM is essentially a linear regression with the slope coefficient . Returns of an asset are...

Persistent link: https://www.econbiz.de/10009468694

Statistical computation and inference for functional data analysis

Jiang, Huijing - 2010

-based method for clustering random time functions which are spatially interdependent. A cluster consists of time functions which … semi-parametric model. We also assume thatthe clustering membership is a realization from a Markov random field. Under … clustering algorithm under a series of settings: small number of time points, high noise level and varying dependence structures …

Persistent link: https://www.econbiz.de/10009475878

Representing, Visualizing, and Modeling Online Auction Data

Hyde, Valerie - 2007

The wide and growing popularity of online auctions creates enormous amounts of publicly available bid data providing an important topic for research. These data pose unique statistical challenges because of their special structures. This research focuses on methods for representing, visualizing,...

Persistent link: https://www.econbiz.de/10009450765

Exploring and Modeling Online Auctions Using Functional Data Analysis

Wang, Shanshan - 2007

In recent years, the increasing popularity of eCommerce, and particularly online auctions has stirred a great amount of scholarly research, especially in information systems, economics,and marketing, but little or no attention has been received from statistics. ECommerce arrives with enormous...

Persistent link: https://www.econbiz.de/10009450766

Venture capital investments through the lens of network and functional data analysis

Esposito, Christian; Gortan, Marco; Testa, Lorenzo; … - 2022

(harmonic centrality), nor with the tightness of investors' community (clustering coefficient) and spreading ability (VoteRank). …

Persistent link: https://www.econbiz.de/10013205386

Superconsistent estimation of points of impact in non‐parametric regression with functional predictors

Poß, Dominik; Liebl, Dominik; Kneip, Alois; … - In: Journal of the Royal Statistical Society: Series B … 82 (2020) 4, pp. 1115-1140

Predicting scalar outcomes by using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time points of the functional predictors have an influence on the outcome. Such ‘points of impact’ are typically unknown and...

Persistent link: https://www.econbiz.de/10012620996

On quantitative stability in infinite-dimensional optimization under uncertainty

Hoffhues, M.; Römisch, W.; Surowiec, T. M. - In: Optimization Letters 15 (2021) 8, pp. 2733-2756

The vast majority of stochastic optimization problems require the approximation of the underlying probability measure, e.g., by sampling or using observations. It is therefore crucial to understand the dependence of the optimal value and optimal solutions on these approximations as the sample...

Persistent link: https://www.econbiz.de/10014501320