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- alr3: Data to accompany Applied Linear Regression 3rd edition , This package is a companion ... textbook S. Weisberg (2005), ”Applied Linear Regression,” 3rd edition, Wiley. It includes all the data...
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- semiparametric regression is based on penalized regression splines and mixed models. Every model in this ... book is a special case of the linear mixed model or its generalized counterpart. This ... moderate background in regression, though familiarity with matrix and linear algebra is assumed. Another audience...
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- scale linear classification. It supports logistic regression and linear support vector machines. We provide easy...
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- statistical model representations, such as linear and nonlinear regression models and generalized linear models. Many...
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- estimators for all forms of the linear regression model, and stochastic frontier, discrete choice ... wider range of single and multiple equation linear and nonlinear models. LIMDEP is a true...
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- regression models and the Buckley-James multiple regression model for right-censored responses, and implements ... logistic and ordinary linear models. rms works with almost any regression model ... ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James ... spatially correlated observations, generalized linear models, and quantile regression...
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- Algorithm 39: Clusterwise linear regression. The combinatorial problem of clusterwise discrete linear approximation is defined ... fast algorithm for clusterwise linear regression...
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- monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized...
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- readers who want to learn applied regression without loading heavy mathematical burden. For such ... exploratory tools it starts with simple linear regression, then deals with multiple regression and goes ... logistic regression and generalized linear models. It has discussions of response and predictor transformations, diagnostics...
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- statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric ... examples include mixed models, generalized linear models, correspondence analysis, and structural equations...
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- sample over the usual linear regression model and over some models like PcGets. Thus, both...
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- principle of ordinal regression, consist in solving a small linear program. The program provides piecewise ... linear functions of marginal utility that are as compatible as possible with the given preorder ... sensitivity analysis of the ordinal regression problem. There is a friendly user interface in which...
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- Imputation by matching; Multiple imputation; Multiple linear regression; Logistic regression; Univariate and multivariate censored regression...
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- bootstrap. We consider the least-square linear regression problem with regularization by the l1-norm ... Bolasso, is compared favorably to other linear regression methods on synthetic data and datasets from...
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- from Cox models (PROC PHREG) and linear regression models (PROC REG) can also be corrected...
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- clustered data using generalized linear mixed models, and regression data using generalized additive models...
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- spike-and-slab regularization paths for linear regression. Rockova and George (2014) <10.1080>
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- basic elements of Bayesian analysis for linear regression models are reminded, and Markov chain Monte ... presented. Chapter 2 is on dynamic linear models. State space models, dynamic linear models, state...