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All code used to simulate time series data, analyse data and create figures for the paper
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创建时间：
2017-01-01
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This structure is generated by the following equations: V^{(1)}_t = 0.2 * V^{(1)}_{t - 1} + 0.1 * \eps^1_t V^{(2)}_t = 0.2 * V^{(2)}_{t - 1} + 0.7 * V^{(1)}_{t - 1} + 0.1 * \eps^2_t V^{(3)}_t = 0.2 *
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Accounting for time series errors in partially linear model with single- or multiple-run
This paper concerns statistical estimation of the partially linear model (PLM) for time course measurements, which are temporally correlated and allow multiple-run for repeated measurements to enhance
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Supplement 1. R code for simulation implementation and parameter estimation.
File List EstimationComparison.R (MD5: 5055e25ebc61fe8dbf012b48198eadda) Simulations.R (MD5: 56eef6b34cb59a85d5072b0b20e645ec) CreateFigures.R (MD5: 2c19a14d1ac5887ba544c2937d8639ca
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The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables pred
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Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing
Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests t
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