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Full-Text Articles in Physical Sciences and Mathematics
Asymptotically Unbiased Estimation Of A Nonsymmetric Dependence Measure Applied To Sensor Data Analytics And Financial Time Series, Angel Caƫaron, Razvan Andonie, Yvonne Chueh
Asymptotically Unbiased Estimation Of A Nonsymmetric Dependence Measure Applied To Sensor Data Analytics And Financial Time Series, Angel Caƫaron, Razvan Andonie, Yvonne Chueh
All Faculty Scholarship for the College of the Sciences
A fundamental concept frequently applied to statistical machine learning is the detection of dependencies between unknown random variables found from data samples. In previous work, we have introduced a nonparametric unilateral dependence measure based on Onicescu’s information energy and a kNN method for estimating this measure from an available sample set of discrete or continuous variables. This paper provides the formal proofs which show that the estimator is asymptotically unbiased and has asymptotic zero variance when the sample size increases. It implies that the estimator has good statistical qualities. We investigate the performance of the estimator for data analysis applications …
Visual Knowledge Discovery And Machine Learning For Investment Strategy, Antoni Wilinski, Boris Kovalerchuk
Visual Knowledge Discovery And Machine Learning For Investment Strategy, Antoni Wilinski, Boris Kovalerchuk
All Faculty Scholarship for the College of the Sciences
Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in opportunity to substitute some complex cognitive tasks by easier perceptual tasks. However for cognitive tasks such as financial investment decision making this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2D or 3D world. This paper presents an approach to find an investment strategy based on pattern discovery in multidimensional space of specifically prepared time series. Visualization based on the lossless Collocated Paired Coordinates (CPC) plays an important role in this approach …
Asymptotically Unbiased Estimator Of The Informational Energy With Knn, Angel Caţaron, Răzvan Andonie, Chinmei Y. Chueh
Asymptotically Unbiased Estimator Of The Informational Energy With Knn, Angel Caţaron, Răzvan Andonie, Chinmei Y. Chueh
All Faculty Scholarship for the College of the Sciences
Motivated by machine learning applications (e.g., classification, function approximation, feature extraction), in previous work, we have introduced a non- parametric estimator of Onicescu’s informational energy. Our method was based on the k-th nearest neighbor distances between the n sample points, where k is a fixed positive integer. In the present contribution, we discuss mathematical properties of this estimator. We show that our estimator is asymptotically unbiased and consistent. We provide further experimental results which illustrate the convergence of the estimator for standard distributions.