Journal article
Selection-bias-adjusted inference for the bivariate normal distribution under soft-threshold sampling
Annals of the Institute of Statistical Mathematics, Vol.77(4), pp.597-625
08/2025
DOI: 10.1007/s10463-025-00925-0
Abstract
The problem of estimating parameters and predicting outcomes of a bivariate Normal distribution is more challenging when, owing to data-dependent selection (or missingness or dropout), the available data are not a representative sample of bivariate realizations. This problem is addressed using an observation model that is induced by a combination of a multivariate Normal “science” model and a realistic “soft-threshold selection” model with unknown truncation point. This observation model, which is expressed using an intuitive selection subset notation, is a generalization of existing “hard-threshold” models. It affords simple-to-compute selection-bias-adjusted estimates of both the regression (conditional mean) parameters and the bivariate correlation. In addition, a simple bootstrap approach for computing both confidence and prediction intervals in the soft-threshold selection setting is described. Simulation results are promising. To motivate this research, two illustrative examples describe a setting where selection bias is an issue of concern.
Details
- Title: Subtitle
- Selection-bias-adjusted inference for the bivariate normal distribution under soft-threshold sampling
- Creators
- Joseph B. Lang
- Resource Type
- Journal article
- Publication Details
- Annals of the Institute of Statistical Mathematics, Vol.77(4), pp.597-625
- DOI
- 10.1007/s10463-025-00925-0
- ISSN
- 0020-3157
- eISSN
- 1572-9052
- Publisher
- SPRINGER HEIDELBERG
- Language
- English
- Electronic publication date
- 05/02/2025
- Date published
- 08/2025
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984820565202771
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