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  • Search: subject:"Sparse signals"
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Year of publication
Subject
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sparse signals 2 ARCH model 1 ARCH-Modell 1 Artificial intelligence 1 Börsenkurs 1 Capital income 1 Data Transformation 1 Electrical Engineering 1 Engineering 1 Financial market 1 Finanzmarkt 1 Forecasting model 1 Kapitaleinkommen 1 Künstliche Intelligenz 1 LASSO 1 Large deviation 1 Large p small n 1 Prognoseverfahren 1 Share price 1 Signalling 1 Sparse signals 1 Thresholding 1 Virtual currency 1 Virtuelle Währung 1 adaptive sensing 1 constraint resource allocation 1 cryptocurrencies 1 efficient system design 1 financial markets 1 intraday returns 1 machine learning 1 search 1
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Online availability
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Free 2 Undetermined 1
Type of publication
All
Article 1 Book / Working Paper 1 Other 1
Type of publication (narrower categories)
All
Article in journal 1 Aufsatz in Zeitschrift 1
Language
All
English 2 Undetermined 1
Author
All
Bashan, Eran 1 Chen, Song Xi 1 Lalwani, Vaibhav 1 Li, Jun 1 Meshram, Vedprakash 1 Zhong, Pingshou 1
Institution
All
Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München 1
Published in...
All
MPRA Paper 1 The journal of prediction markets 1
Source
All
BASE 1 ECONIS (ZBW) 1 RePEc 1
Showing 1 - 3 of 3
Cover Image
Predicting intraday cryptocurrency returns - A sparse signals approach
Lalwani, Vaibhav; Meshram, Vedprakash - In: The journal of prediction markets 15 (2021) 1, pp. 3-9
Persistent link: https://www.econbiz.de/10012803496
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Two-Sample Tests for High Dimensional Means with Thresholding and Data Transformation
Chen, Song Xi; Li, Jun; Zhong, Pingshou - Volkswirtschaftliche Fakultät, … - 2014
We study two tests for the equality of two population mean vectors under high dimensionality and column-wise dependence by thresholding. They are designed for better power performance when the mean vectors of two populations differ only in sparsely populated coordinates. The first test is...
Persistent link: https://www.econbiz.de/10011109223
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Efficient Resource Allocation Schemes for Search.
Bashan, Eran - 2008
This thesis concerns the problem of efficient resourceallocation under constraints. In many applications a finitebudget is used and allocating it efficiently can improveperformance. In the context of medical imaging the constraint is exposure to ionizing radiation, e.g., computed tomography...
Persistent link: https://www.econbiz.de/10009476990
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