Regression Models
REGRESSION MODELS
... egression models are used to predict one variable from one or more other ...variables. Regression models provide the scientist with a powerful tool, allowing predictions about past, present, or ...
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Robust fitting of mixture regression models
... linear regression, the normality based MLE is sensitive to outliers or heavy-tailed error ...linear regression, the M estimate, which replaces the least squares criterion by a robust criterion, is one of ...
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Generalized Inference in Linear Regression Models
... linear regression under both ho- moscedasticity and heteroscedasticity of the error ...for regression coeffi- cients of linear regression ...The regression data from two different regimes are ...
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Flexible Regression Models for Functional Responses.
... in regression models with functional outcomes; see for example Guo (2002) and Morris and Carroll (2006) who considered inference in functional mixed model ...function-on-function regression ...
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On concurvity in nonlinear and nonparametric regression models
... case of symmetric smoothers with eigenvalues [0, 1], approximate concurvity (i.e. the existence of an approximate minimizer of the penalized least square criterion that leads to approximate nonlinear additive ...
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Regression Models for Count Data in R
... binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical ...zero-inflated ...
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Validating Geospatial Regression Models With Bootstrapping
... of regression parameters ...in regression modeling have not been realized and/or applied widely in spatial regression ...error) regression models is only one example to illustrate the ...
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Diagnostic in Poisson Regression Models
... Poisson regression models have received much attention in econometrics and medicine literature as model for describing count data that assume integer values corresponding to the number of events occurring ...
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Derivation of regression models for pan evaporation estimation
... linear regression (MLR) in conjunction with Principal Component Analysis (PCA) was used for modeling of pan ...Mathematical models of pan evaporation were derived for each ...for models in each ...
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Subsampling Method for Robust Estimation of Regression Models
... classical regression methods for both identifying the outlier free subsamples and then estimating the regres- sion model with the outlier free ...ideal regression model and some outliers which are not ...
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Some Regression Models and Algorithms for Functional Data.
... linear regression models with scalar response and functional covariates have received a significant amount of attention in literature since its introduction by Ramsay and Dalzell ...linear models, ...
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A note on specification testing in some regression models
... equations models with endogenous regressors is comprehensively surveyed in Hausman ...linear regression models, under the maintained assumption of the exogeneity of a set of instruments, is due to ...
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Log Link Regression Models for Ordinal Responses
... logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response ...these models with a logarithmic link to allow estimation of different forms of the risk ...
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Building Regression Models with the Forward Search
... of regression variables with a forward search for each model consid- ...of models found by dropping each variable in ...of models often uses a model selection criterion such as AIC [ 1 ] in which the ...
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Flexible Regression Models for Survival Data
... Similar to in Chapter 2, inspired by the relative survival that observed survival probability is actually the product of net survival probability and expected survival probability, we also incorporate the background ...
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VARIABLE SELECTION IN REGRESSION MODELS
... Regression models are used to predict one variable from one or more other ...variables. Regression models provide the scientist with a powerful tool, allowing predictions about past, present, ...
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Addressing multicollinearity in regression models: a ridge regression application
... of regression models ...Multiple regression models are used when the response variable Y depends on a set of explanatory variables (X 1 , X 2 , … , X m ) ...multilinear regression ...
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Inference for Approximating Regression Models
... squares regression at the population ...that regression defines, rather than the slopes of an assumed linear model, become the population quantities of interest, and they can be estimated ...population ...
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On Some Ridge Regression Estimators for Logistic Regression Models
... Logistic regression is a widely used method for categorical ...of regression model where the response variable is a dichotomous variable and the independent variables are continuous or ...logistic ...
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A Universal Selection Method in Linear Regression Models
... linear regression model with fixed design and deal with the problem of how to select a model from a family of models which fits the data ...linear models is done for the sake of ...simple ...
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