High-dimensional statistical inference
WebThis article develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known … WebHigh-Dimensional Statistical Inferences Ming Yuan, Columbia University Scribe: Kiran Vodrahalli 01/22/2024 1 LECTURE 1: Introduction I will focus on the methods, theory, …
High-dimensional statistical inference
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WebAbstract. High-dimensional group inference is an essential part of statistical methods for analysing complex data sets, including hierarchical testing, tests of interaction, detection of heterogeneous treatment effects and inference for local heritability. Group inference in regression models can be measured with respect to a weighted quadratic ... Webfor Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
Web29 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. WebDepartment of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, P.R. China. Correspondence to: Yu Chen, Department of …
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 … Web9 de fev. de 2015 · A new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations is proposed, which is likelihood-free and provides valid inference for a broad class of highdimensional constrained estimating equation problems, which are …
Web'This book provides an in-depth mathematical treatment and methodological intuition of high-dimensional statistics. The main technical tools from probability theory are …
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 … trump speaking tonight liveWeb13 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 … trump speaks today liveWeb16 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. trump speaking at diamonds funeralWebDownloadable (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 … trump special master appointedWebIn this article, we develop a new estimation and valid inference method for single or low-dimensional regression coefficients in high-dimensional generalized linear models. … trump speech about charlottesvilleWeb3 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 … philippines consulate in houstonWebhigh dimensional graphical models tailored to ordinal-mixed data have attracted less attention. Moreover, how to perform statistical inference on this type of model is largely unknown. In this paper we propose a uni ed framework for esti-mation and statistical inference of the graphical model named Latent Mixed Gaussian Copula Model, which philippines consulate in australia