Text

LowCon : A Design-based Subsampling Approach in a Misspecified Linear Model

We consider a measurement constrained supervised learning problem, that is, (i) full sample of the predictors are given; (ii) the response observations are unavailable and expensive to measure. Thus, it is ideal to select a subsample of predictor observations, measure the corresponding responses, and then fit the supervised learning model on the subsample of the predictors and responses. However, model fitting is a trial and error process, and a postulated model for the data could be misspecified. Our empirical studies demonstrate that most of the existing subsampling methods have unsatisfactory performances when the models are misspecified. In this paper, we develop a novel subsampling method, called “LowCon,” which outperforms the competing methods when the working linear model is misspecified. Our method uses orthogonal Latin hypercube designs to achieve a robust estimation. We show that the proposed design-based estimator approximately minimizes the so-called worst-case bias with respect to many possible misspecification terms. Both the simulated and real-data analyses demonstrate the proposed estimator is more robust than several subsample least-squares estimators obtained by state-of-the-art subsampling methods. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.30 No.3 September 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p, 694 - 708

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EXPERIMENTAL DESIGN
CONDITION NUMBER
LEAST-SQUARES ESTIMATION
WORST-CASE MSE

Info Detail Spesifik

https://doi.org/10.1080/10618600.2020.1844215

Pernyataan Tanggungjawab

Cheng Meng, Rui Xie, Abhyuday Mandal, Xinlian Zhang, Wenxuan Zhong, Ping Ma

Versi lain/terkait

Lampiran Berkas

Komentar