Current status linear regression

By P. Groeneboom, K. Hendrickx. Annals of Statistics, 2016

52 citations recorded by the source corpus.

We construct $\sqrt{n}$-consistent and asymptotically normal estimates for the finite dimensional regression parameter in the current status linear regression model, which do not require any smoothing device and are based on maximum likelihood estimates (MLEs) of the infinite dimensional parameter. We also construct estimates, again only based on these MLEs, which are arbitrarily close to efficient estimates, if the generalized Fisher information is finite. This type of efficiency is also derived under minimal conditions for estimates based on smooth non-monotone plug-in estimates of the distribution function. Algorithms for computing the estimates and for selecting the bandwidth of the smooth estimates with a bootstrap method are provided. The connection with results in the econometric literature is also pointed out.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0a41e81139cd43c77f9350364f85676e19ecc1fd

How researchers see this paper

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

In Statistics and Probability

Citations in the corpus

Score estimation in the monotone single‐index model (2017, methodology): “Recently, Groeneboom and Hendrickx (2018) developed several score estimators for the current status linear regression model Y = 𝜷T0 Z + 𝜀, where the distribution function F0 of 𝜀 is left unspecified.”
Nonparametric Shape-Restricted Regression (2017, background): “[9] studied the LSE in a monotone single index model (see (49)) and showed the n1/3-consistency of the LSEs of m and β∗; also see [106] and [57].”
Least squares estimation in the monotone single index model (2016, methodology): “Groeneboom and Hendrickx (2016) consider several alternative √ n-consistent estimators based on a truncated likelihood, where the truncation parameter has to be chosen by the practitioner. imsart-bj ver.”