Methods and Applications of Linear Models: Regression and the Analysis of Variance

By R. R. Hocking. 2003

408 citations recorded by the source corpus.

Subfields: Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/205181f62fe2e516b4206e1d8e73e49d8bf18040

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Citations in the corpus

Analyzing Reaction Times (2010, background): “Thus, influential values are those outliers or extreme values which essentially alter the estimates, the residuals and/or the fitted values (more about these issues can be found in Hocking, 1996).”
Linear Models in Statistics (2001, neutral): “Among the books that were meant to be undergraduate or graduate texts, Bapat (1993) and Jørgensen (1993) keep the coverage at a basic level, and Stapleton (1995) and Hocking (1996) provide somewhat broader coverage.”
Using mixed models to combine smooth size-selection and catch-comparison curves over hauls (2003, methodology): “The model is an example of a mixed model (e.g., Hocking 1996) in which hauls are treated as random effects.”
Predictability Effects on Durations of Content and Function Words in Conversational English (2009, background): “Since regression coefficients are notoriously unstable in models with highly correlated factors (Hocking, 2003, chap.”
Applied Linear Regression (2004, methodology): “…with the Anova function in the car package in R. 6.2 the analysis of variance 141 this testing paradigm, called the marginal means method by Hocking (1985, 2003), but some of these tests depend on the parameterization used for the regressors and so they are not recommended for general use…”
Corporate Governance and Performance in the Wake of the Financial Crisis: Evidence from US Commercial Banks (2011, background): “In interpreting the results, we conclude that a non-linear association is present, given a significant quadratic term (Hocking, 2003).”
Simultaneous Variable Selection (2005, methodology): “…Mathematical and Information Sciences Cleveland, Qld 4163, Australia Stephen J. WRIGHT Computer Sciences Department University of Wisconsin Madison, WI 53706 We propose a new method for selecting a common subset of explanatory variables where the aim is to model several response variables.”
A Comparative Study On Some Methods For Handling Multicollinearity Problems (2006, background): “Assuming the predictors are in a standard form, V denotes the orthogonal matrix of eigenvectors of the correlation matrix, and Z = XV , where the variables are defined by the columns of Z, that are the principal components (Hocking [9]).”