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Data driven modeling in the presence of time series structure: Improved bounds and effective algorithms

• Authors : Mazhar, Othmane, Ph.D. student, 1990;

Subjects: Time series; Non-asymptotic estimation; Minimax

• Source: TRITA-SCI-FOU.

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Figure 7. Robustness of optimal receptor distribution to subsampling of odorants and receptor types. ; Robustness in the prediction is measured as the Pearson correlation between the predicted OSN numbers with complete information, and after subsampling. (a) Robustness of OSN abundances as a function of the fraction of receptors removed from the sensing matrix. Given a full sensing matrix (in this case a 24 × 110 matrix based on Drosophila data (Hallem and Carlson, 2006)), the abundances of a subset of OSN types were calculated in two ways. First, the optimization problem from Equation (7) was solved including all the OSN types and an environment with a random covariance matrix (see Figure 5). Then a second optimization problem was run in which a fraction of the OSN types were removed. The optimal neuron counts Ki′ obtained using the second method were then compared (using the Pearson correlation coefficient) against the corresponding numbers Ki from the full optimization. The shaded area in the plot shows the range between the 20th and 80th percentiles for the correlation values obtained in 10 trials, while the red curve is the mean. A new subset of receptors to be removed and a new environment covariance matrix were generated for each sample. (b) Robustness of OSN abundances as a function of the fraction of odorants removed from the environment, calculated similarly to panel a except now a certain fraction of odorants was removed from the environment covariance matrix, and from the corresponding columns of the sensing matrix.

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Data driven modeling in the presence of time series structure: : Improved bounds and effective algorithms

• Authors : Mazhar, Othmane

Subjects: Time series; Non-asymptotic estimation; Minimax

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The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization

• Authors : Li, Mufan Bill; Nica, Mihai; Roy, Daniel M.

Subjects: Statistics - Machine Learning; Computer Science - Machine Learning

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Wasserstein F-tests for Fr\'echet regression on Bures-Wasserstein manifolds

• Authors : Xu, Haoshu; Li, Hongzhe

Subjects: Statistics - Methodology; Statistics - Machine Learning

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A joint latent class model of longitudinal and survival data with a time-varying membership probability

• Authors : Miao, Ruoyu; Charalambous, Christiana

Subjects: Statistics - Methodology; Mathematics - Statistics Theory; Statistics - Applications

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Testing Late Time Cosmic Acceleration with uncorrelated Baryon Acoustic Oscillations dataset

• Authors : Benisty, David; Staicova, Denitsa

Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics; Astrophysics - Astrophysics of Galaxies; General Relativity and Quantum Cosmology

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Adaptive Bayesian Detection for MIMO Radar in Gaussian Clutter

• Authors : HAN Jinwang; ZHANG Zijing; LIU Jun

Subjects: Multiple-Input Multiple-Output (MIMO) radar; Adaptive detection; Bayesian

• Source: Leida xuebao, Vol 8, Iss 4, Pp 501-509 (2019)

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THE RATE OF CONVERGENCE OF SPECTRA OF SAMPLE COVARIANCE MATRICES.

• Authors : Götze, F.¹ ; Tikhomirov, A. N.²

• Source: Theory of Probability & Its Applications. 2010, Vol. 54 Issue 1, p129-140. 12p.

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Radical Complexity

• Authors : Bouchaud, Jean-Philippe

Subjects: Quantitative Finance - General Finance; Condensed Matter - Statistical Mechanics

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