Statistical Models and Methods for Lifetime Data

By Gordon Johnston. 2003

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Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “In practice, we can use a very small m (e.g., 5 or 10) and still achieve good results, which is also confirmed by our later experiments.”
A two‐stage procedure for comparing hazard rate functions (2007, background): “functions of the treatment and control groups (cf., e.g., Lawless 1982, Bain and Engelhardt 1991,”
Testing equality of stationary autocovariances (2009, background): “These conditions hold for any causal and invertible autoregressive moving-average (ARMA) time series.”
Maximum likelihood estimation in Dagum distribution with censored samples (2011, methodology): “Figure 2 compares the survival functions of the above-mentioned parametric distributions with the empirical Kaplan–Meier curves [18].”
On the one parameter unit-Lindley distribution and its associated regression model for proportion data (2018, background): “According to Lawless [16] if themodel is appropriate, then the êi should behave approximately like a sample from the standard exponential distribution.”
Maximum likelihood estimation under a finite mixture of generalized exponential distributions based on censored data (2012, neutral): “For more details about the analysis of censored failure times and type-I and type-II censoring, see Kalbfleisch and Prentice (1980) and Lawless (1982).”