Citation:

Schliep, E., A. Gelfand, AND D. Holland. Alternating Gaussian process modulated renewal processes for modeling threshold exceedances and durations. Stochastic Environmental Research and Risk Assessment . Springer-Verlag, BERLIN-HEIDELBERG, Germany, 32(2):401-417, (2018). https://doi.org/10.1007/s00477-017-1417-9

Impact/Purpose:

Threshold exceedance events for some environmental processes can be infrequent yet have major adverse impacts on humans. For example, high levels of ozone concentration or noise pollution in an urban area may occur only a few times a year yet pose dangerous health risks for sensitive populations (Bell et al., 2004; Bell and Dominici, 2008; U.S. Environmental Protection Agency, 2011, 2013). Accurate inference regarding both the number and duration of exceedance events would provide critically important information in the evaluation of these risks across time and spatial locations.

Description:

It is often of interest to model the incidence and duration of threshold exceedance events for an environmental variable over a set of monitoring locations. Such data arrive over continuous time and can be considered as observations of a two-state process yielding, sequentially, a length of time in the below threshold state followed by a length of time in the above threshold state, then returning to the below threshold state, etc. We have a two-state continuous time Markov process, often referred to as an alternating renewal process. The process is observed over a truncated time window and, within this window, duration in each state is modeled using a distinct cumulative intensity specification. Initially, we model each intensity over the window using a parametric regression specification. We extend the regression specification adding temporal random effects to enrich the model using a realization of a log Gaussian process over time. With only one type of renewal, this specification is referred to as a Gaussian process modulated renewal process. Here, we introduce Gaussian process modulation to the intensity for each state. Model fitting is done within a Bayesian framework. We clarify that fitting with a customary log Gaussian process specification over a lengthy time window is computationally infeasible. The nearest neighbor Gaussian process, which supplies sparse covariance structure, is adopted to enable tractable computation. We propose methods for both generating data under our models and for conducting model comparison. The model is applied to hourly ozone data for four monitoring sites at different locations across the United States for the ozone season of 2014. For each site, we obtain estimated profiles of up-crossing and down-crossing intensity functions through time. In addition, we obtain inference regarding the number of exceedances, the distribution of the duration of exceedance events, and the proportion of time in the above and below threshold state for any time interval.

URLs/Downloads:

Record Details: