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Ridge-based method for finding curvilinear structures from noisy data

Pulkkinen, Seppo - In: Computational Statistics & Data Analysis 82 (2015) C, pp. 89-109

as sampling noise and background clutter. It is shown that ridge curves of the marginal density induced by the model can … be used to estimate the generating functions. Given a Gaussian kernel density estimate for the marginal density, ridge …–corrector algorithm for tracing the ridge curve set of such a density estimate is developed. Efficiency and robustness of the algorithm …

Persistent link: https://www.econbiz.de/10011117702

Overlapping community detection using a generative model for networks

Wang, Zhenwen; Hu, Yanli; Xiao, Weidong; Ge, Bin - In: Physica A: Statistical Mechanics and its Applications 392 (2013) 20, pp. 5218-5230

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping...

Persistent link: https://www.econbiz.de/10010742306

Generative models for functional data using phase and amplitude separation

Tucker, J. Derek; Wu, Wei; Srivastava, Anuj - In: Computational Statistics & Data Analysis 61 (2013) C, pp. 50-66

Constructing generative models for functional observations is an important task in statistical functional analysis. In general, functional data contains both phase (or x or horizontal) and amplitude (or y or vertical) variability. Traditional methods often ignore the phase variability and focus...

Persistent link: https://www.econbiz.de/10010617232

Segmenting market structure from multi-channel clickstream data: a novel generative model

Qian, Yang; Jiang, Yuanchun; Du, Yanan; Sun, Jianshan; … - In: Electronic commerce research 20 (2020) 3, pp. 509-533

Persistent link: https://www.econbiz.de/10012287494

A general framework for portfolio construction based on generative models of asset returns

Cheng, Tuoyuan; Chen, Kan - In: The Journal of finance and data science : JFDS 9 (2023), pp. 1-21

In this paper, we present an integrated approach to portfolio construction and optimization, leveraging high-performance computing capabilities. We first explore diverse pairings of generative model forecasts and objective functions used for portfolio optimization, which are evaluated using...

Persistent link: https://www.econbiz.de/10014514017