High-dimensional statistical inference

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August undefined, 2024

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 ... WebEstimation 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 high dimension. Annals of Statistics 40, 436-465. [5] Bai, J., Li, K., 2016. Maximum likelihood estimation and inference for approximate factor models of high ...

High-Dimensional Statistics - Cambridge Core

WebIn the field of high-dimensional statistical inference more generally, uncertainty quantification has become a major theme over the last decade, originating with influential … Web'This book provides an in-depth mathematical treatment and methodological intuition of high-dimensional statistics. The main technical tools from probability theory are … gpw 1000fc 1a9

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WebEstimation 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 … 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 … Web13 de abr. de 2024 · 2.1 Stochastic models. The inference methods compared in this paper apply to dynamic, stochastic process models that: (i) have one or multiple unobserved … gpv weather model

[2003.05968] Statistical Inference for High Dimensional Panel ...

Statistical inference for high-dimensional pathway analysis with ...

Web2.2.1 Optimality of statistical inference In high-dimensional linear model, the paper [31] established the minimax expected length of the con dence interval over the parameter … Web16 de set. de 2024 · This motivates us to propose statistical inference approaches for LCSTE(X) with high-dimensional covariates. As an extension of the proposed approach, doubly robust estimation for high-dimensional data is discussed in Section 8. 3 Estimation Method. We begin with notation and definitions. gpw13 outcomesWeb19 de nov. de 2006 · Download a PDF of the paper titled High Dimensional Statistical Inference and Random Matrices, by Iain M. Johnstone Download PDF Abstract: … gpvt-50 water heater

"Web1 de jan. de 2024 · In modern-day analytics, there is ever-growing need to develop statistical models to study large data sets, i.e., high-dimensional data. Between … " - High-dimensional statistical inference

High-dimensional statistical inference

Statistical Inference for High-Dimensional Matrix-Variate Factor …

Web1 de jan. de 2024 · In high-dimensional analysis, as seen in Section 2, p is assumed to be increasing with n. How can we construct consistent estimators for Σ and test statistics to … WebEstimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation (with discussion). Electronic Journal of Statistics 10, 1-59. Cai, T. T. & Zhang, A. (2016). Inference for high-dimensional differential correlation matrices. Journal of Multivariate Analysis 143, 107–126.

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WebOn asymptotically optimal confidence regions and tests for high-dimensional models. Ann. Statist., 42(3): 1166-1202, 06 2014. Google Scholar; Sara A. van de Geer. High-dimensional generalized linear models and the lasso. Ann. Statist., 36(2):614-645, 04 2008. Google Scholar; Aad W van der Vaart. Asymptotic statistics, volume 3. Web3 de out. de 2024 · Inference on High-dimensional Single-index Models with Streaming Data. Traditional statistical methods are faced with new challenges due to streaming …

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 … Web13 de abr. de 2024 · 2.1 Stochastic models. The inference methods compared in this paper apply to dynamic, stochastic process models that: (i) have one or multiple unobserved internal states \varvec {\xi } (t) that are modelled as a (potentially multi-dimensional) random process; (ii) present a set of observable variables {\textbf {y}}.

Web12 de mar. de 2024 · Statistical Inference for High Dimensional Panel Functional Time Series. Zhou Zhou, Holger Dette. In this paper we develop statistical inference tools for … WebDescribing many concepts arising in high-dimensional statistical inference for linear models is instructive, as the concepts are simple yet tremendously useful in many applications. Extensions to other regression-type models are discussed in Section 4. Remarks on the radically different marginal approach are given in Section 5. Estimation …

Web1 de jun. de 2024 · Abstract. In this paper, we discuss the estimation of a nonparametric component f1 f 1 of a nonparametric additive model Y = f1(X1)+⋯+fq(Xq)+ϵ Y = f 1 ( X 1) + ⋯ + f q ( X q) + ϵ. We allow the number q of additive components to grow to infinity and we make sparsity assumptions about the number of nonzero additive components.

Web1 HighDimensionalStatisticalInferenceAndRand omMatricesPdf Pdf Recognizing the way ways to get this books HighDimensionalStatisticalInferenceAndRandomMatricesPdf … gpw 13 result frameworkWeb29 de ago. de 2016 · Here, we reformulate high-dimensional statistical inference in the framework of the statistical physics of quenched disorder to address these fundamental issues for big data. We are accordingly able to obtain powerful generalizations of time-honored classical statistical theorems dating back to the 1940s. gpvt water heaterWeb5 de abr. de 2024 · For high-dimensional statistical inference, de-sparsifying methods have received popularity thanks to their appealing asymptotic properties. Existing results … gpw 1000 release dateWeb1 de mai. de 2024 · In this article, we propose a pathway analysis approach for jointly analyzing multiple responses with high-dimensional features. Our approach accounts … gpw 13 whoWeb22 de fev. de 2024 · We propose a new method under the Bayesian framework to perform valid inference for low dimensional parameters in high dimensional linear models under sparsity constraints. Our approach is to use surrogate Bayesian posteriors based on partial regression models to remove the effect of high dimensional nuisance variables. We … gpw1501 pressure washer gpw13 outputsWebIn this paper we develop an online statistical inference approach for high-dimensional generalized linear models with streaming data for real-time estimation and inference. We propose an online debiased lasso (ODL) method to accommodate the special structure of streaming data. ODL differs from offline debiased lasso in two important aspects. First, in … gpw1803 pressure washer