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High dimensional inference

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 … 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 ...

High-dimensional robust inference for censored linear models

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 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 … dr bradley borsari fullerton ca https://joolesptyltd.net

Structural inference in sparse high-dimensional vector …

WebMulti-armed bandits in high-dimension More noise sensitivity to the choice of tuning parameter Linear UCB with variable selection attains oracle properties Issues of dynamic variable selection in high-dimension Kosuke Imai (Princeton) High-Dimensional Causal Inference Harvard/MIT (Feb., 2016) 11 / 11 WebHigh-Dimensional Methods and Inference on Structural and Treatment Effects† Alexandre Belloni is Associate Professor of Decision Sciences, Fuqua School of Business, Duke … 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 … dr bradley boop little rock

Statistical Inference for High-Dimensional Matrix-Variate Factor Models ...

Category:Post-selection Inference of High-dimensional Logistic Regres

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High dimensional inference

High-dimensional robust inference for censored linear models

Web12 de abr. de 2024 · A novel algorithm, TransHDGLM, that integrates data from the target study and the source studies is proposed. Minimax rate of convergence for estimation is established and the proposed estimator is shown to be rate-optimal. Statistical inference for the target regression coefficients is also studied. WebMoreover, the manifold hypothesis is widely applied in machine learning to approximate high-dimensional data using a small number of parameters . Experimental studies …

High dimensional inference

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WebVarying-coefficient models are frequently used to capture changes in the effect of input variables on the response as a function of an index or time. In this work, we study high … WebBy 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.

Web1 de jul. de 2024 · High-dimensional inference, on the other hand, is much less developed. In particular, although considerable progress has been made for inference in standard high-dimensional regression (Javanmard and Montanari, 2014, van de Geer et al., 2014, Zhang and Zhang, 2014, Ning and Liu, 2024), much less is known for more … WebSpringer Nature 2024 LATEX template Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models T. Tony Cai1, Zijian Guo2 and Yin Xia3 1Department of Statistics ...

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 … WebIn the field of high-dimensional statistical inference more generally, uncertainty quantification has become a major theme over the last decade, originating with influential …

WebWe consider high-dimensional inference when the assumed linear model is misspecified. We describe some correct interpretations and corresponding sufficient assumptions for …

WebMoreover, the manifold hypothesis is widely applied in machine learning to approximate high-dimensional data using a small number of parameters . Experimental studies showed that a dynamical collapse occurs in the brain from incoherent baseline activity to low-dimensional coherent activity across neural nodes [ 66 – 68 ]. dr bradley boyd orthoWebCommunication-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 ... dr bradley bourcyWeb1 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 … dr bradley beer cedar rapidsWebHowever, 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. enbridge air conditioningWebAbstract 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... enbridge and alectraWebHigh-Dimensional Methods and Inference on Structural and Treatment Effects by Alexandre Belloni, Victor Chernozhukov and Christian Hansen. Published in volume 28, … dr bradley bowman ophthalmologistWebHowever, 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 … dr bradley bruner wichita ks