Concentration Based Inference in High Dimensional Generalized Regression Models (I: Statistical Guarantees)
نویسندگان
چکیده
منابع مشابه
Generalized orthogonal components regression for high dimensional generalized linear models
Here we propose an algorithm, named generalized orthogonal components regression (GOCRE), to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an exten...
متن کاملHigh-Dimensional Gaussian Copula Regression: Adaptive Estimation and Statistical Inference
We develop adaptive estimation and inference methods for high-dimensional Gaussian copula regression that achieve the same optimal performance without the knowledge of the marginal transformations as that for high-dimensional linear regression. Using a Kendall’s tau based covariance matrix estimator, an `1 regularized estimator is proposed and a corresponding de-biased estimator is developed fo...
متن کاملRobust inference in high- dimensional approximately sparse quantile regression models
This work proposes new inference methods for the estimation of a regression coefficientof interest in quantile regression models. We consider high-dimensional models where the number ofregressors potentially exceeds the sample size but a subset of them suffice to construct a reasonableapproximation of the unknown quantile regression function in the model. The proposed methods are<lb...
متن کاملStatistical inference in high dimensional linear and AFT models
A large amount of previous literature proposed and studied variable selection procedures for high dimensional data, and most of the researchers focused on the selection properties as well as the point estimation properties. However, there have been limited studies considering the construction of confidence intervals for the highdimensional variable selection problems. In this thesis, we propose...
متن کاملBayesian Inference for Generalized Additive Regression based on Dynamic Models
We present a general approach for Bayesian inference via Markov chain Monte Carlo MCMC simulation in generalized additive semiparametric and mixed models It is particularly appropriate for discrete and other fundamentally non Gaussian responses where Gibbs sampling techniques developed for Gaussian models cannot be applied We use the close relation between nonparametric regression and dynamic o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2018
ISSN: 1556-5068
DOI: 10.2139/ssrn.3234409