Results 21 to 30 of about 2,678,398 (281)
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.
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RESEARCH ON GPS HEIGHT FITTING BASED ON LINEAR REGRESSION MODEL [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 This paper mainly expounds the parameter estimation method, the outlier diagnosis and the establishment of the optimal regression equation in the linear regression model theory, the analysis of the principle of the polynomial fitting model, the ...
K. Y. Yang +7 more
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Robust Functional Linear Regression Models
The R Journal, 2023 With advancements in technology and data storage, the availability of functional data whose sample observations are recorded over a continuum, such as time, wavelength, space grids, and depth, progressively increases in almost all scientific branches. The functional linear regression models, including scalar-on-function and function-on-function, have ...
Ufuk Beyaztas, Han Lin Shang
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Kernel smoothing of Aalen's linear regression model [PDF]
, 1997 The linear regression model by Aalen for failure time analysis allows the inclusion of time-dependent covariates as well as the variation of covariate effects over time.
Aydemir, Sibel, Biller, Clemens
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Hidden Markov Linear Regression Model and its Parameter Estimation
IEEE Access, 2020 This article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and ...
Hefei Liu, Kunqjnu Wang, Yong Li
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Generating atmospheric forcing perturbations for an ocean data assimilation ensemble
Tellus: Series A, Dynamic Meteorology and Oceanography, 2019 Running ensemble of reanalyses or forecasts has proved successful at improving their performances, despite the cost. Generating ensemble simulations requires generating perturbations within the models, and for the assimilated observations and subsidiary ...
Isabelle Mirouze, Andrea Storto
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Modified One-Parameter Liu Estimator for the Linear Regression Model
Modelling and Simulation in Engineering, 2020 Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model.
Adewale F. Lukman +3 more
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Short period PM2.5 prediction based on multivariate linear regression model. [PDF]
PLoS ONE, 2018 A multivariate linear regression model was proposed to achieve short period prediction of PM2.5 (fine particles with an aerodynamic diameter of 2.5 μm or less).
Rui Zhao +4 more
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A New Regression Model: Modal Linear Regression [PDF]
Scandinavian Journal of Statistics, 2013 ABSTRACTThe mode of a distribution provides an important summary of data and is often estimated on the basis of some non‐parametric kernel density estimator. This article develops a new data analysis tool called modal linear regression in order to explore high‐dimensional data. Modal linear regression models the conditional mode of a response Y given a
Yao, Weixin, Li, Longhai
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Partially linear censored quantile regression [PDF]
, 2009 Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate.
B Honore +17 more
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