DeepAI AI Chat
Log In Sign Up

A fast Monte Carlo test for preferential sampling

by   Joe Watson, et al.
The University of British Columbia

The preferential sampling of locations chosen to observe a spatio-temporal process has been identified as a major problem across multiple fields. Predictions of the process can be severely biased when standard statistical methodologies are applied to preferentially sampled data without adjustment. Currently, methods that can adjust for preferential sampling are rarely implemented in the software packages most popular with researchers. Furthermore, they are technically demanding to design and fit. This paper presents a fast and intuitive Monte Carlo test for detecting preferential sampling. The test can be applied across a wide range of data types. Importantly, the method can also help with the discovery of a set of informative covariates that can sufficiently control for the preferential sampling. The discovery of these covariates can justify continued use of standard methodologies. A thorough simulation study is presented to demonstrate both the power and validity of the test in various data settings. The test is shown to attain high power for non-Gaussian data with sample sizes as low as 50. Finally, two previously-published case studies are revisited and new insights into the nature of the informative sampling are gained. The test can be implemented with the R package PStestR


page 1

page 2

page 3

page 4


A multivariate functional-data mixture model for spatio-temporal data: inference and cokriging

In this paper, we introduce a model for multivariate, spatio-temporal fu...

Conditional Monte Carlo revisited

Conditional Monte Carlo refers to sampling from the conditional distribu...

The Performance of Largest Caliper Matching: A Monte Carlo Simulation Approach

The paper presents an investigation of estimating treatment effect using...

Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

We propose a patch sampling strategy based on a sequential Monte-Carlo m...

Calculating the Expected Value of Sample Information in Practice: Considerations from Three Case Studies

Investing efficiently in future research to improve policy decisions is ...

Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space

We demonstrate for the first time that ill-conditioned, non-smooth, cons...

Consensus Monte Carlo for Random Subsets using Shared Anchors

We present a consensus Monte Carlo algorithm that scales existing Bayesi...