High dimensional inference

Author: crbg

August undefined, 2024

WebAbstract 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... Web12 de abr. de 2024 · Asymptotic normality for a debiased estimator is established, which can be used for constructing coordinate-wise confidence intervals of the regression …

High-dimensional robust inference for censored linear models

Web19 de nov. de 2006 · High Dimensional Statistical Inference and Random Matrices. Iain M. Johnstone. Multivariate statistical analysis is concerned with observations on several … WebHigh Dimensional Change Point Inference: Recent Developments and Extensions J Multivar Anal. 2024 Mar;188:104833. doi: 10.1016/j ... Based on that, we provide a survey of some extensions to general high dimensional parameters beyond mean vectors as well as strategies for testing multiple change points in high dimensions. chinayarn.com

High Dimensional Inference With Random Maximum A-Posteriori ...

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. WebSpringer Nature 2024 LATEX template Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models T. Tony Cai1, Zijian Guo2 and Yin … WebTo 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. … grand banker nova scotia

FACT: High-Dimensional Random Forests Inference DeepAI

High-dimensional inference for linear model with correlated errors

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, … 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 … grand bank pharmacyWebBy dealing with strong and weak signals separately, our work combines sparse regression techniques with Stein estimation to build an honest and adaptive confidence set in high-dimensional regression. Corollaries 3 and 4 provide theoretical guarantees for the use of popular sparse regression methods, lasso and MCP, in our two-step method. grand bank of texas

"Web1 de jan. de 2024 · High-dimensional linear models with independent errors have been well-studied. However, statistical inference on a high-dimensional linear model with heteroskedastic, dependent (and possibly ... " - High dimensional inference

High dimensional inference

High Dimensional Statistical Inference and Random Matrices

Web19 de ago. de 2024 · In this chapter, a comprehensive overview of high dimensional inference and its applications in data analytics is provided. Key theoretical … Web1 de jan. de 2024 · For high-dimensional parametric models, estimation and hypothesis testing for mean and covariance matrices have been extensively studied. However, the practical implementation of these methods is fairly limited and is primarily restricted to …

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Web22 de out. de 2024 · First, we propose to construct a new set of estimating equations such that the impact from estimating the high-dimensional nuisance parameters becomes … WebCommunication-efficient estimation and inference for high-dimensional quantile regression based on smoothed decorrelated score. Fengrui Di, Fengrui Di. School of Statistics ... we …

WebIn this work, we propose a Bayesian methodology to make inferences for the memory parameter and other characteristics under non-standard assumptions for a class of stochastic processes. This class generalizes the Gamma-modulated process, with trajectories that exhibit long memory behavior, as well as decreasing variability as time … Web28 de out. de 2024 · This "high-dimensional regime" is reminiscent of statistical mechanics, which aims at describing the macroscopic behavior of a complex …

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 … Web20 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 with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes.

WebCommunication-efficient estimation and inference for high-dimensional quantile regression based on smoothed decorrelated score. Fengrui Di, Fengrui Di. School of Statistics ... we focus on the distributed estimation and inference for a preconceived low-dimensional parameter vector in the high-dimensional quantile regression model with small ...

WebHowever, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. grand bank of texas in grand prairieWebHigh-dimensional empirical likelihood inference 3 high-dimensional over-identiﬁcation test by assessing the maximum of the marginal empirical likelihood ratios. Our … china yan restaurant homesteadWeb21 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. china yard sales office2011WebHigh-Dimensional Methods and Inference on Structural and Treatment Effects by Alexandre Belloni, Victor Chernozhukov and Christian Hansen. Published in volume 28, issue 2, pages 29-50 of Journal of Economic Perspectives, Spring 2014, Abstract: Data with a large number of variables relative to the sa... china yarn dyed cushionWeb15 de mai. de 2024 · Abstract: This paper presents a new approach, called perturb-max, for high-dimensional statistical inference in graphical models that is based on applying random perturbations followed by optimization. This framework injects randomness into maximum a-posteriori (MAP) predictors by randomly perturbing the potential function for … china yantai shipping company limitedWebDownloadable (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 … grand bank provincial courtWebEstimation and inference of change points in high-dimensional factor models. Journal of Econometrics 219, 66-100. [4] Bai, J., Li, K., 2012. Statistical analysis of factor models of … china yarn-dyed weaving association