Results 21 to 30 of about 1,095,657 (311)

Local model uncertainty and incomplete-data bias (with discussion) [PDF]

, 2005
Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model.
Barry, Sarah, Recchia, Angela, Sharif, Maarya,, Copas, John,, Conte, Rossella Lo, Skinner, Chris J., Eguchi, Shinto, Parker, Helen,, Onabid, Mathias, Purutçuoğlu, Vilda, Wit, Ernst,, Weir, James, McGrory, Clare, Nguyen, The Mahn,, Wit, Ernst, Pritchard, Gareth, Miller, James, Zhu, Ximin, Conte, Rossella Lo,, Henderson, Neil, Ferguson, Claire, Pritchard, Gareth,, Zhu, Ximin,, Copas, John, Parker, Helen, Barry, Sarah,, John Copas, Weir, James,, Eguchi, Shinto,, Purutçuoğlu, Vilda,, Shinto Eguchi, Fearnside, Alastair, Fearnside, Alastair,, McGrory, Clare,, Ferguson, Claire,, Sharif, Maarya, Onabid, Mathias,, Miller, James,, Nguyen, The Mahn, Recchia, Angela,, Henderson, Neil,   +40 more
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R code associated with Methods for Implementing Integrated Step-Selection Functions with Incomplete Data

, 2023
See Readme file uploaded with the repositoryThis repository contains R code (along with associated output from running the code) presented in: Hofmann, D. D., G. Cozzi, and J. Fieberg.
Hofmann, David D, Fieberg, John R, Cozzi, Gabriele   +2 more
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Learning from incomplete data in Bayesian networks with qualitative influences [PDF]

, 2016
Domain experts can often quite reliably specify the sign of influences between variables in a Bayesian network. If we exploit this prior knowledge in estimating the probabilities of the network, it is more likely to be accepted by its users and may in ...
Sub Algorithmic Data Analysis, Algorithmic Data Analysis, Decision Support Systems, Sub Decision Support Systems, van der Gaag, L.C., Feelders, A.J., Masegosa, Andrés   +6 more
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Profile Likelihood and Incomplete Data [PDF]

International Statistical Review, 2010
Summary According to the law of likelihood, statistical evidence is represented by likelihood functions and its strength measured by likelihood ratios. This point of view has led to a likelihood paradigm for interpreting statistical evidence, which carefully distinguishes evidence about a parameter from error probabilities and personal belief.
openaire   +3 more sources

Textually Summarising Incomplete Data [PDF]

Proceedings of the 10th International Conference on Natural Language Generation, 2017
Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.
Stephanie Inglis, Ehud Reiter, Somayajulu Sripada   +2 more
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Uncovering Suspicious Activity From Partially Paired and Incomplete Multimodal Data

IEEE Access, 2017
Multimodal data can be used to gain additional perspective on a phenomenon. For applications, such as security and the detection of suspicious activity, the need to aggregate and analyze data from multiple modes is vital.
Carter Chiu, Justin Zhan, Felix Zhan
doaj   +1 more source

Phase Identification With Incomplete Data

IEEE Transactions on Smart Grid, 2018
Phase identification is a process to determine which of the three phases a particular house is connected to. The state-of-the-art identification methods usually exploit smart metering data. However, the data sets are not always available and the major challenge is hence to identify phases with incomplete data set.
Minghao Xu, Ran Li 0004, Furong Li 0004
openaire   +2 more sources

Unsupervised and Supervised Feature Selection for Incomplete Data via L_2,1-Norm and Reconstruction Error Minimization

Applied Sciences, 2022
Feature selection has been widely used in machine learning and data mining since it can alleviate the burden of the so-called curse of dimensionality of high-dimensional data.
Jun Cai, Linge Fan, Xin Xu, Xinrong Wu
doaj   +1 more source

Determining the Macroscopic Fundamental Diagram from Mixed and Partial Traffic Data

Promet (Zagreb), 2018
The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management.
Yangbeibei Ji, Mingwei Xu, Jie Li, Henk J van Zuylen   +3 more
doaj   +1 more source

Correlating variables with different scale types: A new framework based on matrix comparisons

Methods in Ecology and Evolution, 2023
Ecological variables may be expressed on four basic measurement scales (nominal, ordinal, interval or ratio), whereas circular variables and those combining a nominal state with other scale types are also common.
János Podani, Dénes Schmera, Simonetta Bagella   +2 more
doaj   +1 more source