Nonparametric Bayesian testing for monotonicity

作者：Scott J G^*; Shively T S; Walker S G

来源：Biometrika, 2015, 102(3): 617-630.

DOI：10.1093/biomet/asv023

• Bayesian asymptotics
• Model selection
• Monotonicity
• Regression spline
• Smoothing spline

This paper adopts a nonparametric Bayesian approach to testing whether a function is monotone. Two new families of tests are constructed. The first uses constrained smoothing splines with a hierarchical stochastic-process prior that explicitly controls the prior probability of monotonicity. The second uses regression splines together with two proposals for the prior over the regression coefficients. Via simulation, the finite-sample performance of the tests is shown to improve upon existing frequentist and Bayesian methods. The asymptotic properties of the Bayes factor for comparing monotone versus nonmonotone regression functions in a Gaussian model are also studied. Our results significantly extend those currently available, which chiefly focus on determining the dimension of a parametric linear model.

• 出版日期2015-9