linear regression models
Fast FSR Methods for Second-Order Linear Regression Models
... This research has focused on studies where fitting second-order linear regression models is appropriate. When the number of predictors or factors is large, the full second-order model is often ...
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Robust Linear Regression Models: Use of a Stable Distribution for the Response Data
... of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality ...standard ...
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Some New Methods for the Comparison of Two Linear Regression Models
... two linear regression models is to use the partial F ...As regression models are true or of interest often only over a restricted region of the covariates, the part of this confidence ...
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A Bayesian analysis of linear regression models with highly collinear regressors
... stable linear regression models with highly collinear ...several linear relationships hold between variables and the meaning subsequently changed to linear dependence between ...
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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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A study of partial F tests for multiple linear regression models
... Partial F tests play a central role in model selections in multiple linear regression models. This paper studies the partial F tests from the view point of simultaneous confidence bands. It first ...
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Maximum likelihood estimators in linear regression models with Ornstein Uhlenbeck process
... in linear regression models with non-Gaussian long memory moving average ...the regression parameters in a linear re- gression model with autoregressive ...in linear ...
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U-statistic Type Tests for Structural Breaks in Linear Regression Models
... consider linear regression models (hereafter LRMs) with coefficients that are constant for all time ...of linear regression models to ...in regression parameters at a ...
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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 follows. ...
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Generalized Inference in Linear Regression Models
... of linear regression coefficients and dispersion parameters and generalized tests (GTs) for comparing regression coefficients for small and moderate sample sizes 3, 5, 10, 14, 15, 20, 30 and ...
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Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions
... (penalized) linear regression models for ...ridge regression (RR) [1], ridge regression best linear unbiased prediction (RR-BLUP) [2], the least absolute shrinkage and selection ...
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Adaptive Partially Linear Regression Models by Mixing Different Estimates
... Abstract: This paper proposes adapting the semiparametric partial model (PLM) by mixing different estimation procedures defined under different conditions. Choosing an estimation method of PLM, from several estimation ...
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New Criteria of Model Selection and Model Averaging in Linear Regression Models
... various models differ according to sample size, for example, in Figure (1-6) when n=500, the first best model has the intercept, x3, and x7 with lowest value of ...various models differ according to sample ...
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Test of hypotheses for linear regression models with non-sample prior information
... simple regression, multiple regression and parallel regression models, the sampling distribution of the UT and RT follows a univariate noncentral F distribution under the alternative ...four ...
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Test of hypotheses for linear regression models with non-sample prior information
... simple regression model follow Student’s t distribu- tion but that of the PTT follows a correlated bivariate Student’s t ...four regression models, there is a correlation between the UT and PT but ...
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A Universal Selection Method in Linear Regression Models
... For models with a large number of regressors, the problem of establishing an effective selection algorithm is not dis- cussed in this paper; we refer to the paper ...
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Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia’s hospital
... Phillips et al. (2013) studied on initiating the colorectal cancer screening at the age of 50. Their aim was to get the factors associated with colorectal cancer screening among persons age 50-75 by using descriptive and ...
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Inferential Models for Linear Regression
... In this paper we have elaborated on the recently proposed inferential model framework, which produces prior-free probabilistic summaries of evidence for/against assertions of interest and, moreover, these summaries have ...
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Modelling the mass of kiwi fruit by geometrical attributes
... consider models for the total of observations (variety is ignored), similar models were obtained, that are shown in Table ...Nonlinear regression models (polynomial and power) are also shown ...
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Mapping snow depth return levels: smooth spatial modeling versus station interpolation
... Table 3 (lines a to d) compared to Table 2 confirms that using the mean snow depth as covariate for the location and scale parameters is helpful. There is a clear improvement in the spline and linear ...
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