A look at some data on the old faithful geyser

By Adelchi Azzalini, Adrian W. Bowman. 1990

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Citations in the corpus

Telling cause from effect in deterministic linear dynamical systems (2015, neutral): “2 Old Faithful Geyser [2]”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “It is originally described in (Azzalini and Bowman, 1990) and contains data about the duration of an eruption and the time interval between subsequent eruptions of the Old Faithful geyser in Yellowstone National Park, USA.”
On Conditional Density Estimation (2003, methodology): “A motivating example: Consider the bivariate data analysed by AZZALINI and BOWMAN (1990) on the waiting time between the starts of successive eruptions and the duration of the subsequent eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming.”
Most Likely Transformations (2015, methodology): “interval-censored (“medium” eruption) or right-censored (“long” eruption) as explained by Azzalini and Bowman (1990). This censoring was widely ignored in analyses of the Old Faithful data because most non-parametric kernel techniques cannot deal with censoring (see for example Hyndman and Yao 2002)”
Causal Inference on Time Series using Structural Equation Models (2012, background): “[Azzalini and Bowman, 1990, length 194] X t contains the duration of an eruption and Y t the time interval to the next eruption of the Old Faithful geyser.”
Model‐based geostatistics (2007, background): “Azzalini and Bowman (1990) demonstrate a similar phenomenon in connection with the sample autocorrelation function of time series data.”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “It is originally described in (Azzalini and Bowman, 1990) and contains data about the duration of an eruption and the time interval between subsequent eruptions of the Old Faithful geyser in Yellowstone National Park, USA.”
Causal Inference on Time Series using Restricted Structural Equation Models (2013, background): “[Azzalini and Bowman, 1990, length 194] X t contains the duration of an eruption and Y t the time interval to the next eruption of the Old Faithful geyser.”
The Topp–Leone odd log-logistic family of distributions (2017, methodology): “Application 2: Plasma ferritin data We now consider the data discussed by Azzalini and Bowman [29] consisting of 299 interval times for the starts of successive eruptions of the Old Faithful Geyser in Yellowstone National Park, Wyoming, USA.”
SEMIPARAMETRIC ESTIMATION OF A TWO-COMPONENT MIXTURE MODEL (2006, background): “The Annals of Statistics 2006, Vol. 34, No. 3, 1204–1232 DOI: 10.1214/009053606000000353 © Institute of Mathematical Statistics, 2006 SEMIPARAMETRIC ESTIMATION OF A TWO-COMPONENT MIXTURE MODEL”
Bandwidth selection for kernel conditional density estimation (2001, methodology): “1 Introduction To motivate the problem, consider the data given in Azzalini and Bowman (1990) on the waiting time between the starts of successive eruptions and the duration of the subsequent eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming.”
A note on composite likelihood inference and model selection (2005, methodology): “Here we present an application to the Old Faithful geyser dataset discussed in Azzalini & Bowman (1990), with the aim of comparing binary Markov and hidden Markov models.”
Principles of Data Mining (2001, methodology): “The data are extracted from a larger data set given in Azzalini and Bowman (1990). Figure 9.”
Nonlinear causal discovery with additive noise models (2008, background): “The first dataset, the “Old Faithful” dataset [17] contains data about the duration of an eruption and the time interval between subsequent eruptions of the Old Faithful geyser in Yellowstone National Park, USA.”