Testing for monotonicity of a regression mean by calibrating for linear functions

By Peter Hall, Nancy E. Heckman. 2000

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function (2005, methodology): “Hall and Heckman (2000) proposed a procedure based on “running gradients” over short intervals for which the least-favorable distribution under the null, whenσ is known, corresponds to the case whereF is identically constant.”
Nonparametric state price density estimation using constrained least squares and the bootstrap (2006, unknown): “The latter are often found to be more accurate when the statistic is an asymptotic pivot (see Hall (1992) for extensive arguments in support of this proposition).”
Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints (2012, unknown): “For example, Hall and Heckman (2000) develop a bootstrap test for monotonicity whose test statistic is formulated around the slope estimates of linear regression models fitted over small intervals.”
Constrained penalized splines (2012, methodology): “Hall & Heckman (2000) compute a “running derivative” by fitting lines over portions of the data, and their test statistic is the minimum slope.”