Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Computational Chemistry"
DOI: 10.1002/jcc.27006
Abstract: Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their… read more here.
Keywords: active learning; regression models; chemically accurate; gaussian process ... See more keywords
Logistic regression models of cytokines in differentiating vitreoretinal lymphoma from uveitis
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Clinical Laboratory Analysis"
DOI: 10.1002/jcla.24689
Abstract: Vitreoretinal lymphoma (VRL) can commonly masquerade as chronic idiopathic uveitis due to its nonspecific clinical presentation. Thus, its early diagnosis is difficult. In this study, new logistic regression models were used to classify VRL and… read more here.
Keywords: uveitis; logistic regression; lymphoma; vitreoretinal lymphoma ... See more keywords
Model validation and influence diagnostics for regression models with missing covariates.
Sign Up to like & getrecommendations! Published in 2018 at "Statistics in medicine"
DOI: 10.1002/sim.7584
Abstract: Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has… read more here.
Keywords: regression; validation; regression models; missing covariates ... See more keywords
Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany.
Sign Up to like & getrecommendations! Published in 2018 at "Statistics in medicine"
DOI: 10.1002/sim.7804
Abstract: In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels.… read more here.
Keywords: regression models; group; zero inflated; group regularization ... See more keywords
Variable selection in semiparametric regression models for longitudinal data with informative observation times.
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in medicine"
DOI: 10.1002/sim.9417
Abstract: A common issue in longitudinal studies is that subjects' visits are irregular and may depend on observed outcome values which is known as longitudinal data with informative observation times (follow-up). Semiparametric regression modeling for this… read more here.
Keywords: regression; informative observation; longitudinal data; semiparametric regression ... See more keywords
Fast estimation of mixed‐effects location‐scale regression models
Sign Up to like & getrecommendations! Published in 2023 at "Statistics in Medicine"
DOI: 10.1002/sim.9679
Abstract: As a result of advances in data collection technology and study design, modern longitudinal datasets can be much larger than they historically have been. Such “intensive" longitudinal datasets are rich enough to allow for detailed… read more here.
Keywords: mixed effects; fast estimation; effects location; regression models ... See more keywords
Tests for the linear hypothesis in semi-functional partial linear regression models
Sign Up to like & getrecommendations! Published in 2019 at "Metrika"
DOI: 10.1007/s00184-018-0680-1
Abstract: An empirical likelihood ratio testing method is proposed, in this paper, for semi-functional partial linear regression models. Two empirical likelihood ratio statistics are employed to test the linear hypothesis of parametric components, then we demonstrate… read more here.
Keywords: functional partial; semi functional; linear regression; regression models ... See more keywords
An empirical likelihood method for quantile regression models with censored data
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DOI: 10.1007/s00184-020-00775-1
Abstract: An estimation for censored quantile regression models, which is based on an inverse-censoring-probability weighting method, is studied in this paper, and asymptotic distribution of the parameter vector estimator is obtained. Based on the parameter estimation… read more here.
Keywords: quantile regression; regression models; likelihood; method ... See more keywords
Minimax robust designs for regression models with heteroscedastic errors
Sign Up to like & getrecommendations! Published in 2021 at "Metrika"
DOI: 10.1007/s00184-021-00827-0
Abstract: Minimax robust designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible misspecification of the error variance in the model. We propose a flexible assumption for the error… read more here.
Keywords: robust designs; minimax robust; regression models; heteroscedastic errors ... See more keywords
Does strict validation criteria for individual motor units alter population-based regression models of the motor unit pool?
Sign Up to like & getrecommendations! Published in 2020 at "Experimental Brain Research"
DOI: 10.1007/s00221-020-05906-8
Abstract: The purpose of this study was to determine if the implementation of a strict validation procedure, designed to limit the inclusion of inaccuracies from the decomposition of surface electromyographic (sEMG) signals, affects population-based motor unit… read more here.
Keywords: population based; motor unit; regression models; motor ... See more keywords
Pseudo-maximum likelihood estimators in linear regression models with fractional time series
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Papers"
DOI: 10.1007/s00362-019-01091-1
Abstract: Fractal time series and linear regression models are known to play an important role in many scientific disciplines and applied fields. Although there have been enormous development after their appearance, nobody investigates them together. The… read more here.
Keywords: regression models; linear regression; time series;