Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
Jie Peng,Hans-Georg Müller +1 more
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This paper proposes a distance between two realizations of a random process where for each realization only sparse and irregularly spaced measurements with additional measurement errors are available, and applies distance-based clustering methods to eBay online auction data.Abstract:
We propose a distance between two realizations of a random process where for each realization only sparse and irregularly spaced measurements with additional measurement errors are available. Such data occur commonly in longitudinal studies and online trading data. A distance measure then makes it possible to apply distance-based analysis such as classification, clustering and multidimensional scaling for irregularly sampled longitudinal data. Once a suitable distance measure for sparsely sampled longitudinal trajectories has been found, we apply distance-based clustering methods to eBay online auction data. We identify six distinct clusters of bidding patterns. Each of these bidding patterns is found to be associated with a specific chance to obtain the auctioned item at a reasonable price.read more
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Functional Data Analysis
TL;DR: In this article, the authors provide an overview of FDA, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is functional principal component analysis (FPCA).
Journal ArticleDOI
Functional data clustering: a survey
Julien Jacques,Cristian Preda +1 more
TL;DR: Four groups of clustering algorithms for functional data are proposed, composed of methods which perform simultaneously dimensionality reduction of the curves and clustering, leading to functional representation of data depending on clusters.
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Review of Functional Data Analysis
TL;DR: An overview of FDA is provided, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is Functional Principal Component Analysis (FPCA), an important dimension reduction tool and in sparse data situations can be used to impute functional data that are sparsely observed.
Journal ArticleDOI
Funclust: A curves clustering method using functional random variables density approximation
Julien Jacques,Cristian Preda +1 more
TL;DR: A new method for clustering functional data is proposed under the name Funclust, which relies on the approximation of the notion of probability density for functional random variables, which generally does not exist and a parametric mixture model is proposed.
References
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Journal ArticleDOI
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
TL;DR: The fundamental hypothesis is that dissimilarities and distances are monotonically related, and a quantitative, intuitively satisfying measure of goodness of fit is defined to this hypothesis.
Book
Local polynomial modelling and its applications
Jianqing Fan,Irène Gijbels +1 more
TL;DR: Applications of Local Polynomial Modeling in Nonlinear Time Series and Automatic Determination of Model Complexity and Framework for Local polynomial regression.
Journal ArticleDOI
A Nonlinear Mapping for Data Structure Analysis
TL;DR: An algorithm for the analysis of multivariate data is presented along with some experimental results that is based upon a point mapping of N L-dimensional vectors from the L-space to a lower-dimensional space such that the inherent data "structure" is approximately preserved.
Reference EntryDOI
Functional Data Analysis
TL;DR: The article considers general issues such as characteristics of functional data, uses of derivatives in functional modelling, estimation of phase variation by the alignment or registration of curve features, the nature of error, and so forth.
Book ChapterDOI
Functional Data Analysis
TL;DR: In this article, the authors introduce the concept of functional data analysis (FDA) to describe the smoothness of the process of generating functional data from a set of observed curves and images.