Modern Applied Statistics with S-Plus.

By William N. Venables, B. D. Ripley. 1996

Subfields: Statistics and Probability, Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

In Optimization & Training

Citations in the corpus

Deep Residual Learning for Image Recognition (2015, background): “An early practice of training multi-layer perceptrons (MLPs) is to add a linear layer connected from the network input to the output [34, 49].”
Resting structures and resting habitat of fishers in the southern Sierra Nevada, California (2009, background): “…Development Core Team, 2004.) to obtain a logit response regressed on a linear combination of smoothing functions of the explanatory variables and graphs of their partial residuals to explore their significance and functional form (linear, polynomial, logarithmic, etc.) (Venables and Ripley, 1997).”
Arctic lemmings, Lemmus spp. and Dicrostonyx spp.: integrating ecological and evolutionary perspectives (2008, methodology): “We tested the statistical significance of spectral density peaks against the null hypothesis of white noise using the S-plus test (Venables and Ripley, 1994).”
Accounting for responsive movement in line transect estimates of abundance (2001, methodology): “The 95% CI was estimated using the bias-corrected accelerated percentile interval method, where the acceleration vector was estimated using jackknifing (default option in S-PLUS).”
Adaptive Regression by Mixing (2001, methodology): “The simulation is conducted using the ppreg function in Splus and the Splus code nnet of Venable and Ripley (1997). The input random variables X1, .”
Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation (2004, methodology): “S-PLUS software [15] was used to carry out the simulations.”
Bayesian inference in ecology (2004, methodology): “Automated tools for model selection such as the stepAIC function in S (Venables & Ripley 2002), the MARK software (White & Burnham 1999), or the DIC function in WinBUGS (Spiegelhalter et al. 2002) may have the unintended consequence of discouraging scientists from thinking about uncertainty in…”
Spatial viability analysis of Amur tiger Panthera tigris altaica in the Russian Far East: the role of protected areas and landscape matrix in population persistence (2006, methodology): “We identified an optimal RSF model based on information criteria (AIC; Akaike 1973) and model interpretability and generality (Taper 2004).”
Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data (2000, methodology): “In addition to the tree method and linear-regression model, neural networks [feed-forward neural networks as in the book by Venables and Ripley ( 13 )] with various numbers of hidden layers were tested.”
BAYESIAN ESTIMATION IN SINGLE-INDEX MODELS (2004, methodology): “This popular conjugate prior is known as Zellner’s (1986) g-prior.”
Homophony and morphology: The acoustics of word-final S in English 1 (2015, methodology): “The Box–Cox transformation (Box & Cox 1964, Venables & Ripley 2002) can be employed to identify a suitable transformation parameter λ for a power transformation.”