Results 51 to 60 of about 1,128,323 (301)
BMC Medical Research Methodology, 2022 Background Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model ...
Michael Kammer +3 more
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Bayesian variants of some classical semiparametric regression techniques [PDF]
, 2004 This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=zβ+f(x)+var epsilon where f(.) is an unknown function.
Koop, Gary, Poirier, Dale J.
core +1 more source
Integrative Factor Regression and Its Inference for Multimodal Data Analysis [PDF]
Journal of the American Statistical Association, 2019 Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful ...
Quefeng Li, Lexin Li
semanticscholar +1 more source
Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs [PDF]
Econometrica, 2019 We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias‐aware, in the sense that they take possible bias explicitly into account.
C. Noack, Christoph Rothe
semanticscholar +1 more source
Self‐normalization inference for linear trends in cointegrating regressions
Journal of Time Series Analysis, 2022 In this article, statistical tests concerning the trend coefficient in cointegrating regressions are addressed for the case when the stochastic regressors have deterministic linear trends. The self‐normalization (SN) approach is adopted for developing inferential methods in the integrated and modified ordinary least squares (IMOLS) estimation framework.
openaire +1 more source
Statistical Inference for Functional Linear Quantile Regression
, 2022 We propose inferential tools for functional linear quantile regression where the conditional quantile of a scalar response is assumed to be a linear functional of a functional covariate. In contrast to conventional approaches, we employ kernel convolution to smooth the original loss function.
Sang, Peijun, Shang, Zuofeng, Du, Pang
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Calibrated Percentile Double Bootstrap For Robust Linear Regression Inference [PDF]
Statistica Sinica, 2018 We consider inference for the parameters of a linear model when the covariates are random and the relationship between response and covariates is possibly non-linear. Conventional inference methods such as z-intervals perform poorly in these cases.
McCarthy, Daniel +6 more
openaire +2 more sources
Distributed inference for quantile regression processes [PDF]
Annals of Statistics, 2017 The increased availability of massive data sets provides a unique opportunity to discover subtle patterns in their distributions, but also imposes overwhelming computational challenges. To fully utilize the information contained in big data, we propose a
S. Volgushev +2 more
semanticscholar +1 more source
Revstat Statistical Journal, 2015 This paper develops statistical inference in linear models, dealing with the theory of maximum likelihood estimates and likelihood ratio tests under some linear inequality restrictions on the regression coefficients. The results are widely applicable to
Miguel Fonseca +3 more
doaj +1 more source
Physics Letters B, 2022 We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers.
Asad Khan, E.A. Huerta, Prayush Kumar
doaj +1 more source