Multiple linear regression models to simulate spore yields of Bacillus amyloliquefaciens BS13 through optimization of medium composition
Sign Up to like & getrecommendations! Published in 2022 at "Biotechnology and Applied Biochemistry"
DOI: 10.1002/bab.2315
Abstract: Bacillus amyloliquefaciens is a food spoilage spore‐forming bacterium. Its spores are useful for multiple biotechnological applications. Nevertheless, few reports are available regarding the achievement of a high cell density and good sporulation effectiveness under fermentation… read more here.
Keywords: linear regression; strain bs13; composition; bacillus amyloliquefaciens ... See more keywords
Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value
Sign Up to like & getrecommendations! Published in 2020 at "Biofuels"
DOI: 10.1002/bbb.2140
Abstract: The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In… read more here.
Keywords: stochastic gradient; heating value; regression algorithm; linear regression ... See more keywords
Development of Multiple Linear Regression Models for Predicting Chronic Iron Toxicity to Aquatic Organisms
Sign Up to like & getrecommendations! Published in 2023 at "Environmental Toxicology and Chemistry"
DOI: 10.1002/etc.5623
Abstract: We developed multiple linear regression (MLR) models for predicting iron (Fe) toxicity to aquatic organisms for use in deriving site‐specific water quality guidelines (WQGs). The effects of dissolved organic carbon (DOC), hardness, and pH on… read more here.
Keywords: linear regression; toxicology; insects; chemistry ... See more keywords
A comparison of three model output statistics approaches for the bias correction of simulated soil moisture
Sign Up to like & getrecommendations! Published in 2020 at "Meteorological Applications"
DOI: 10.1002/met.1970
Abstract: Errors in the physics schemes and parameters of a land surface model can lead to large errors/bias in simulated soil moisture. In addition, large bias in simulated soil moisture may be caused by soil lateral… read more here.
Keywords: soil moisture; simulated soil; moisture; linear regression ... See more keywords
Understanding MCP-MOD dose finding as a method based on linear regression.
Sign Up to like & getrecommendations! Published in 2017 at "Statistics in medicine"
DOI: 10.1002/sim.7424
Abstract: MCP-MOD is a testing and model selection approach for clinical dose finding studies. During testing, contrasts of dose group means are derived from candidate dose response models. A multiple-comparison procedure is applied that controls the… read more here.
Keywords: dose finding; mcp mod; model; linear regression ... See more keywords
Improving estimation and prediction in linear regression incorporating external information from an established reduced model.
Sign Up to like & getrecommendations! Published in 2018 at "Statistics in medicine"
DOI: 10.1002/sim.7600
Abstract: We consider a situation where there is rich historical data available for the coefficients and their standard errors in a linear regression model describing the association between a continuous outcome variable Y and a set… read more here.
Keywords: estimation; information; linear regression; model ... See more keywords
Kernel density-based likelihood ratio tests for linear regression models.
Sign Up to like & getrecommendations! Published in 2020 at "Statistics in medicine"
DOI: 10.1002/sim.8765
Abstract: In this article, we develop a so-called profile likelihood ratio test (PLRT) based on the estimated error density for the multiple linear regression model. Unlike the existing likelihood ratio test (LRT), our proposed PLRT does… read more here.
Keywords: ratio test; likelihood; likelihood ratio; linear regression ... 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 iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Geodesy"
DOI: 10.1007/s00190-017-1062-6
Abstract: In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error… read more here.
Keywords: reweighted least; regression; linear regression; iteratively reweighted ... 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;
Bootstrapping multiple linear regression after variable selection
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Papers"
DOI: 10.1007/s00362-019-01108-9
Abstract: This paper suggests a method for bootstrapping the multiple linear regression model $$Y = \beta _1 + \beta _2 x_2 + \cdots + \beta _p x_p + e$$Y=β1+β2x2+⋯+βpxp+e after variable selection. We develop asymptotic theory… read more here.
Keywords: variable selection; multiple linear; linear regression; selection ... See more keywords