Introduction to Linear Regression Analysis (3rd ed.)

By Jammalamadaka. 2003

1,309 citations recorded by the source corpus.

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Citations in the corpus

Trend analysis of climate time series: A review of methods (2019, background): “Generalized least-squares (GLS) employs another sum of squares, which includes also the autocorrelation structure (Montgomery and Peck, 1992).”
Factors determining online sustainability reporting by local governments (2015, methodology): “Moreover, the apparent problem is alleviated by the step-by-step regression methods by backward elimination applied in this study (Mederos et al., 2000; Montgomery et al., 2001).”
Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification (2005, methodology): “Residual analysis for the linear fit was performed by constructing normal probability plots of the residuals for each subject [21].”
Support vector machines in remote sensing: A review (2011, unknown): “…is also another interesting concept that serves as a key attraction to SVMs. Commonly described by many authors under the notion of overfitting (Montgomery and Peck, 1992), yet variously referred to by others as bias-variance tradeoff (Geman et al., 1992) or capacity control (Guyon et al.,…”
Break function regression (2009, methodology): “Besides quantitative measures of the goodness of fit it is important to evaluate fit quality also using graphical tools of residual analysis [29].”
A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment (2006, background): “Usually, VIF values larger than 10 suggest that multicollinearity may be causing estimation problems (Montgomery and Peck, 1992).”
Linear vs. quadratic discriminant analysis classifier: a tutorial (2016, background): “Many classification and regression models have been proposed in the literature such as neural networks (NN) (Specht, 1990), linear regression (Montgomery et al., 2012; Seber and Lee, 2012), nonlinear regression (Motulsky and Christopoulos, 2004; Hahne et al.”
Assumptions of multiple regression: Correcting two misconceptions (2013, unknown): “, Huber-White standard errors; White, 1980), bootstrap methods (Montgomery et al., 2001), estimation via Weighted Least Squares (Chatterjee & Hadi, 2012), or the specification of a Generalized Linear Model (Cohen et al.”
Inference in Hidden Markov Models (2006, background): “The regression texts of Montgomery, Peck, and Vining (2001) and Mendenhall and Sincich (2003) are more comprehensive in the regression treatment than the reviewed text.”
An approach to estimate nonparametric flow duration curves in ungauged basins (2009, methodology): “Furthermore, a test against multicollinearity has been performed in order to exclude variables with redundant information [Montgomery et al., 2001].”
Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, methodology): “The main steps of stepwise method are listed below (Montgomery et al. 2001): (1) Calculating simple correlation coefficients (ryx) with the following equation, and then selecting the first variable having largest ryx to enter into the model (X1); ryx ¼ X Y−Y…”
Distribution of non-native invasive species and soil properties in proximity to paved roads and unpaved roads in a quartzitic mountainous grassland of southeastern Brazil (rupestrian fields) (2010, methodology): “To determine possible differences in richness and abundance of native and non-native invasive species along the gradient away from the road, Spearman correlation tests were carried out, as the residuals did not fit the assumptions of homoscedasticity after attempts to change (Montgomery et al. 2006)”
Generalized Linear Models (2002, unknown): “The last two authors collaborated on the most recent edition of a book on regression analysis (Montgomery, Peck, and Vining (2001), reported by Gray (2002), and the rst author has had multiple editions of his own regression analysis book (Myers 1990), the latest of which was reported by Ziegel…”
Exploration from Demonstration for Interactive Reinforcement Learning (2016, methodology): “Discrepancy is computed using the externally studentized residual [27].”