linear regression based models
Applying Linear Regression and Neural Network Meta-Models for Evolutionary Algorithm Based Simulation Optimization
... Some of these techniques output a linear model, such as ordinary least squares regression, and some produce a non-linear model, such as neural network modeling techniques. The goal of this research ...
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A Universal Selection Method in Linear Regression Models
... Over time the problem of model selection has been studied by a large number of authors. The papers [1,2] by Akaike and Mallows inspired statisticians to think about the comparisons of fitted models to a given ...
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Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities
... ing linear regression models (Hessami et ...techniques based on deep neural networks have been applied for refining coarse-grained spatial data (Vandal et ...is based on the analogy ...
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Robust estimation in linear regression models with fixed effects
... In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regression estimates in linear models with fixed effects. We propose to calculate the principal sensitivity ...
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Testing parametric models in linear-directional regression
... test based on the local linear estimator (p = 1) provides a better power for large bandwidths in certain scenarios, the overall impression is that the test with p = 0 is hard to beat: the powers with p = 0 ...
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Textural Characteristic of Margarine Enriched with Pectin Fiber by Using Blending Method
... Based on Figure 3, it appears that the effect of the addition of pectin in each treatment is suitable for linear regression models because the R2 value obtained was 0,906 and has a value of r ...
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Shrinkage-Based Variable Selection Methods for Linear Regression and Mixed-Effects Models
... that models with random coefficients in repeated mea- sures analysis offer a more scientifically defensible framework than just using fixed effects models, as some models need to be complex to ...
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On Diagnostics in Stochastic Restricted Linear Regression Models
... However, statistical diagnostics of stochastic restricted linear regression models based on stochastic restricted ridge estimator (SRRE) are studied in this paper. The paper is organized as ...
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Ridge regression and diagnostics in generalized linear models
... generalized linear models (GLMs) introduced by Neider & Wedderburn (1972), the explanatory variables are highly correlated and so are termed collinear or ...the linear regression case is ...
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glm-ie: Generalised Linear Models Inference & Estimation Toolbox
... generalised linear mod- els over continuous-valued ...inference based on (convex) variational bounds, on expectation propagation and on factorial mean ...graphical models or Markov random fields with ...
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Research on the Effectiveness of the Confucius Institute (Classroom) Based on Linear Regression Models
... unary regression analysis or multiple regression analysis, “the Salary and Training Fee of Chinese Deans and Teachers (including Volunteers)” by China maintains a stable correlation with “Chinese test ...
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Stable Graphical Models
... (α-SG) models, a class of multivariate stable densities that can also be represented as Bayesian networks whose edges encode linear dependencies between random ...likelihood based learning ...
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5633.pdf
... multiple linear regression (MLR) models and a combination of antecedent rainfall, climate, and environmental ...enterococci models accurately predicted a high percentage (>87%) of ...
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Influence functions of trimmed likelihood estimators for lifetime experiments
... functionals based on the exponential and the (log)normal distribution in regression models with linear and nonlinear link ...asymptotically linear in the derived influence ...
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Development of Prediction Models for the Mutagenicity of Nitrated PAHs Based on Multiple Linear Regression
... QSAR models were built by multiple linear regression (MLR), which has been proved to be a multidisciplinary approach applicable for establishing linear predictive ...the models ...
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Cluster-Based Estimators For Multiple And Multivariate Linear Regression Models
... classical regression ap- proach alongside some classical outlier diagnostic in regression ...robust regression methods with emphasis on low breakdown and high break- down scenarios for multiple ...
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Double Penalized Quantile Regression in Partially Linear Models
... quantile regression estimators in partially linear ...squares based method with regard to the non-causal selection rate (NSR) and the median of model error (MME) when the error distribution is ...
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Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia’s hospital
... Roslani et al. (2012) found that blood tool is one of the main factor causes colorectal cancer in human body. The screening for colorectal neoplasias with fecal occult blood test: false-positive impact of non-dietary ...
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A study of partial F tests for multiple linear regression models
... multiple linear regression ...the linear regression model holds over the entire range of the covariates in ...band based inferential ...
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Empirical Likelihood Diagnosis of Modal Linear Regression Models
... of linear regression model has been fully developed ...modal linear regression models based on empirical likelihood method has not yet been seen in the ...
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