High-dimensional data bootstrap
WebIn addition, we also show that the Gaussian-like convergence rates can be achieved for heavy-tailed data, which are less conservative than those obtained by the Bonferroni technique that ignores the dependency in the underlying data distribution. KW - Bootstrap. KW - Gaussian approximation. KW - High-dimensional inference. KW - U-statistics 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 …
High-dimensional data bootstrap
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Web19 de mai. de 2024 · Abstract. This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of … Web19 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. The method produces a ℓ_∞-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method …
Web1 de set. de 2024 · This has led to high-dimensional data becoming a common characteristic of early-stage biological research, particularly in genomics , proteomics, and imaging. High-dimensional data are data that are generated when p features are measured on each of n samples, so they can be organized into a p × n matrix X, with n … 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 …
WebTeams. Q&A for work. Connect and share knowledge within a single location that is structured and easy to search. Learn more about Teams 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 ...
WebST10CH18_Kato ARjats.cls February 14,2024 12:48 Annual Review of Statistics and its Application High-Dimensional Data Bootstrap Victor Chernozhukov,1 Denis …
Webdimensionality adaptive and robust bootstrap methods. Keywords: Bootstrap, high-dimensional inference, random matrices, resampling 1. Introduction The bootstrap … ct birthday partyWeb9 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. ears and teasers magazineWebHigh-dimensional Factor Pricing Models Qiang Xia College of Mathematics and Informatics, South China Agricultural University and ... H0 Bootstrap data Size (%) 8.6 4.5 7.8 5.8 5.9 5.7 2.6 Ha Bootstrap data Power (%) 18.2 69.6 65.0 74.4 74.5 74.4 9.2 Window 2 … ears and stressWeb21 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 … ears and sinus infectionWeb18 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 … ears and sinusesWebhigh dimensional systems. By data based or "parametric bootstrap" Monte Carlo simulations, we mean simulations where the Data Generating Process (DGP) uses the parame-ter values obtained from an estimation using actual data. We base our simulations on estimated parameter values in order to ascertain that our results are empirically … ears and hearing txWebhelps 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 ... ears and sinuses clogged