High-dimensional data bootstrap
WebThe bootstrap is a tool that allows for efficient evaluation of prediction performance of statistical techniques without having to set aside data for validation. This is especially important for high-dimensional data, e.g., arising from microarrays, because there the number of observations is often … Web30 de set. de 2016 · Download a PDF of the paper titled Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications, by Xiaohui Chen. ... A two-step Gaussian approximation procedure that does not impose structural assumptions on the data distribution is proposed.
High-dimensional data bootstrap
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Web4 de jun. de 2014 · Abstract. We focus on the problem of conducting inference for high dimensional weakly dependent time series. Our results are motivated by the applications in modern high dimensional inference ... Web14 de mai. de 2024 · Variable selection in inferential modelling is problematic when the number of variables is large relative to the number of data points, especially when multicollinearity is present. A variety of ...
Web9 de out. de 2024 · This supports their use for practical analysis of high-dimensional data. 1.1 Related work and our contribution. Besides the growing literature in assessing uncertainty in high-dimensional statistical inference mentioned at the beginning of the introductory section, the use of the bootstrap has been advocated in other works. Webbootstrap on high-dimensional stationary time series. Factor modelling or low-rank representation can project high-dimensional data into low-dimensional subspace. …
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Web18 de mar. de 2024 · High-dimensional covariance matrix estimation plays a central role in multivariate statistical analysis. It is well-known that the sample covariance matrix is singular when the sample size is smaller than the dimension of the variable, but the covariance estimate must be positive-definite. This motivates some modifications of the sample …
Web21 de ago. de 2024 · The parameter \(\gamma \) controls the concavity in both SCAD and MCP penalties: small values of \(\gamma \) indicate that the penalty tends to be concave. It is interesting to note also that when \(\gamma \rightarrow \infty \) both SCAD and MCP reduce to the LASSO penalty.. 2.2 Group Variable Selection. In high dimensional … hill \u0026 griffith companyWebThe bootstrap is a tool that allows for efficient evaluation of prediction performance of statistical techniques without having to set aside data for validation. This is especially … hill \u0026 fischer syracuse nyWebdimensionality adaptive and robust bootstrap methods. Keywords: Bootstrap, high-dimensional inference, random matrices, resampling 1. Introduction The bootstrap … hill \u0026 gully ridersWeb14 de abr. de 2024 · A high-dimensional mediation analysis of MS on birth weight was performed using placental DNAm data from the EDEN mother–child cohort. At an FDR … smart aggregation in tableauWebhelps the Gaussian and bootstrap approximations. In Section 4, we apply the proposed bootstrap method to a number of important high-dimensional problems, including the data-dependent tuning parameter selec-tion in the thresholded covariance matrix estimator and the simultaneous inference of the covariance and Kendall’s tau rank correlation ... smart agent companyWeb19 de fev. de 2024 · We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. … smart aggiornamento softwareWeb29 de nov. de 2024 · In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. We propose an easy-to-implement bootstrap method for high-dimensional WN test and prove its consistency for a variety of test statistics. smart aging clinic opinie