Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models

By John Thomas Ormerod. Statistics in Medicine, 2007

902 citations recorded by the source corpus.

cover point processes in the modern sense of random measures. This chapter also is more technical than the subsequent two on Markov processes. Chapter 4 (on discrete-time countable state Markov processes) is extremely short. At only 32 pages, it is shorter than all those covering a specific type of process, except for Chapter 6 (martingales). I wonder how much the typical student will benefit from the minimal, and delayed, presentation of this topic. In my experience, most students need this material early on, to fully appreciate just what stochastic processes are and what the relevant issues are, before moving on to the more technical processes. The fifth chapter (on continuous-time countable state Markov processes) is the best-developed chapter and contains many more examples than do the other chapters. Birth–death processes and queuing models get substantial coverage, and there is plenty of material here that an instructor may choose to incorporate into a course. I personally believe that an undergraduate text also should devote more space to discussion of covariance structure, especially in the context of Gaussian processes, to reinforce the notions of dependence. Examples with Kalman filters, time series, and control theory are certainly within the range of applications appropriate for a text such as this one. The opening chapter is quite long, covering a wide range of basic probability concepts, such as classification of distributions according to hazard rate behavior (e.g., IFR), partial ordering of distributions, Wald’s identity, general limit theorems, and even advanced inequalities, such as Hölder’s and Minkowski’s. The concise treatment makes these results easy to refer to, but again few if any examples are given for each topic and no context in which the student might appreciate their value is provided. On the other hand, these presentations can be skipped and then returned to later as needed. Although the title claims finance as an applied area, I could find only one financial application (options pricing in the chapter on Brownian motion), unless a few applications to actuarial science are included. The bottom line is that I would not use this book as a text for either an undergraduate or a graduate course. However, I will likely put it on the reference list for my course.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6c12d2a3fb2c69556f1da9270a1312b3469e16e5

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Citations in the corpus

A linear mixed model analysis of masked repetition priming (2010, background): “…and priming effects and correlations than is accomplished with a withinsubject analysis because they take into account between-subject differences in reliability of mean RTs as well as of frequency and priming effects (i.e., the predictions are a type of shrinkage estimate; Faraway, 2006).”
Herbarium specimens reveal increasing herbivory over the past century (2018, methodology): “We contrasted the residual deviances across models (see Faraway, 2016) with and without key predictors to quantify how much of the variation in herbivory was uniquely explained by time, space, and temperature.”
A quantitative review of relationships between ecosystem services (2014, methodology): “We tested for the significance of the differences of deviances by comparing the saturated model which contained the interaction between both factors to the model with just the main effects (Faraway, 2005).”
LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “…this problem, researchers also paid much attention to non-parametric approaches in the traffic flow forecasting field, such as non-parametric regression [17], neural network prediction [18], support vector machine (SVM) [19], Kaman filtering [20, 21] and the combination of these algorithms [22–27].”
Green corridors in urban landscapes affect the arthropod communities of domestic gardens (2012, methodology): “We used a hypothesis approach for each step of analyses (Faraway, 2006).”