High dimensional inference
Web21 de dez. de 2024 · We develop theory of high-dimensional U-statistic, circumvent challenges stemming from the non-smoothness of loss function, and establish … Web14 de abr. de 2024 · Traditional Food Knowledge (TFK) is needed to define the acculturation of culture, society, and health in the context of food. TFK is essential for a …
High dimensional inference
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WebAccess to Project Euclid content from this IP address has been suspended. If your organization is a subscriber, please contact your librarian/institutional administrator. If you are a non-subscriber, please contact the Help Desk. Business Office. 905 W. Main Street. Suite 18B. Durham, NC 27701 USA. WebIn the field of high-dimensional statistical inference more generally, uncertainty quantification has become a major theme over the last decade, originating with influential work on the debiased Lasso in (generalized) linear models (Javanmard and Montanari 2014; van de Geer et al. 2014; Zhang and Zhang 2014), and subsequently developed in other ...
WebDownloadable (with restrictions)! Confidence sets are of key importance in high-dimensional statistical inference. Under case–control study, a popular response-selective sampling design in medical study or econometrics, we consider the confidence intervals and statistical tests for single or low-dimensional parameters in high-dimensional logistic … WebIn this work, we study high-dimensional varying-coefficient quantile regression models and develop new tools for statistical inference. We focus on development of valid confidence intervals and honest tests for nonparametric coefficients at a fixed time point and quantile, while allowing for a high-dimensional setting where the number of input ...
Web28 de set. de 2024 · A common complication that can arise with analyses of high-dimensional data is the repeated use of hypothesis tests. A second complication, … Web9 de out. de 2024 · In this work we will argue that the bootstrap is very useful for individual and especially for simultaneous inference in high-dimensional linear models, that is for testing individual or group hypotheses H_ {0,j} or H_ {0,G}, and for corresponding individual or simultaneous confidence regions. We thereby also demonstrate its usefulness to deal ...
WebHigh-dimensional empirical likelihood inference 3 high-dimensional over-identification test by assessing the maximum of the marginal empirical likelihood ratios. Our …
Web7 de out. de 2024 · ABSTRACT. This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p × q) is comparable to or greater than the number of observations (T).We propose an estimation method called α-PCA that preserves the … parker health and rehabWeb19 de nov. de 2006 · High Dimensional Statistical Inference and Random Matrices. Iain M. Johnstone. Multivariate statistical analysis is concerned with observations on several variables which are thought to possess some degree of inter-dependence. Driven by problems in genetics and the social sciences, it first flowered in the earlier half of the last … time warner eastgateWebAbstract Linear regression models with stationary errors are well studied but the non-stationary assumption is more realistic in practice. An estimation and inference procedure for high-dimensional... parker healthcare australiaWebCommunication-efficient estimation and inference for high-dimensional quantile regression based on smoothed decorrelated score. Fengrui Di, Fengrui Di. School of Statistics ... we … parker healthcareWeb21 de dez. de 2024 · We develop theory of high-dimensional U-statistic, circumvent challenges stemming from the non-smoothness of loss function, and establish convergence rate of regularized estimator and asymptotic normality of the resulting de-biased estimator as well as consistency of the asymptotic variance estimation. parker healthcare indianaWebTo the best of our knowledge, no structural inference methods exist for sparse high-dimensional systems. Our paper attempts to fill this gap. By now, a quite large literature has emerged that deals with the problem of fitting sparse high-dimensional VAR models using ℓ 1 -penalized estimators; see among others Song and Bickel (2011), Han et al. … parker healthcare sdn bhdWeb20 de ago. de 2024 · With the availability of high-dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients’ survival, along … time warner elyria