Posterior contraction and testing for multivariate isotonic regression
نویسندگان
چکیده
We consider the nonparametric regression problem with multiple predictors and an additive error, where function is assumed to be coordinatewise nondecreasing. propose a Bayesian approach make inferences on multivariate monotone function, obtain posterior contraction rate, construct universally consistent testing procedure for monotonicity. To facilitate analysis, we temporarily set aside shape restrictions, endow prior blockwise constant functions independently normally distributed heights. The unknown variance of error term either estimated by marginal maximum likelihood estimate, or equipped inverse-gamma prior. Then unrestricted block-heights are posteriori also given variance, conjugacy. comply project samples from onto class functions, inducing “projection-posterior distribution”, used making inference. Under L1-metric, show that projection-posterior based n independent contracts around true at optimal rate n−1∕(2+d). test monotonicity probability shrinking neighborhood functions. consistent, is, level goes zero, power any fixed alternative one. Moreover, smooth one as long its distance least order estimation function. best our knowledge, no other available in frequentist literature.
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ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2023
ISSN: ['1935-7524']
DOI: https://doi.org/10.1214/23-ejs2115