arXiv subjects

Sally Cripps

Publications and source records attributed to Sally Cripps.

21 records · Page 2Linked to original sources

arXiv2018

A comparison of methods for modeling marginal non-zero daily rainfall across the Australian continent

Naveau et al. (2016) have recently developed a class of methods, based on extreme-value theory (EVT), for capturing low, moderate, and heavy rainfall simultaneously, without the need to choose a threshold typical to EVT methods. We analyse the performance of Naveau et al.'s methods, along with mixtures of gamma distributions, by fitting them to marginal non-zero rainfall from 16,968 sites spanning the Australian continent and which represent a wide variety of rainfall patterns. Performance is assessed by the distribution across sites of the log ratios of each method's estimated quantiles and the empirical quantiles. We do so for quantiles corresponding to low, moderate, and heavy rainfall. Under this metric, mixtures of three and four gamma distributions outperform Naveau et al's methods for small and moderate rainfall, and provide equivalent fits for heavy rainfall.

stat.AP↗

arXiv2018

Irreproducibility; Nothing is More Predictable

The increasing ease of data capture and storage has led to a corresponding increase in the choice of data, the type of analysis performed on that data, and the complexity of the analysis performed. The main contribution of this paper is to show that the subjective choice of data and analysis methodology substantially impacts the identification of factors and outcomes of observational studies. This subjective variability of inference is at the heart of recent discussions around irreproducibility in scientific research. To demonstrate this subjective variability, data is taken from an existing study, where interest centres on understanding the factors associated with a young adult's propensity to fall into the category of `not in employment, education or training' (NEET). A fully probabilistic analysis is performed, set in a Bayesian framework and implemented using Reversible Jump Markov chain Monte Carlo (RJMCMC). The results show that different techniques lead to different inference but that models consisting of different factors often have the same predictive performance, whether the analysis is frequentist or Bayesian, making inference problematic. We demonstrate how the use of prior distributions in Bayesian techniques can be used to as a tool for assessing a factor's importance.

stat.AP↗

arXiv2016

A Bayesian Approach to Predicting Disengaged Youth

This article presents a Bayesian approach for predicting and identifying the factors which most influence an individual's propensity to fall into the category of Not in Employment Education or Training (NEET). The approach partitions the covariates into two groups: those which have the potential to be changed as a result of an intervention strategy and those which must be controlled for. This partition allows us to develop models and identify important factors conditional on the control covariates, which is useful for clinicians and policy makers who wish to identify potential intervention strategies. Using the data obtained by O'Dea (2014) we compare the results from this approach with the results from O'Dea (2014) and with the results obtained using the Bayesian variable selection procedure of Lamnisos (2009) when the covariates are not partitioned. We find that the relative importance of predictive factors varies greatly depending upon the control covariates. This has enormous implications when deciding on what interventions are most useful to prevent young people from being NEET.

stat.AP↗