Linear regression with randomly double-truncated data

Non-parametric estimation for a linear regression model under random double-truncation is investigated, i.e. the variables are observed if and only if the dependent variable lies in a random interval. The method requires only weak distribution assumptions to ensure identifiability, but does not require any specific distribution family for any variable, neither for the truncation variables nor for the error term. By using non-parametric estimators of several distribution functions, consistent and asymptotically normal estimators are established. A simulation study shows the tendency that the lower the probability of observation, the higher the mean squared error of the estimators, even for the same number of observations. Finally, the method is applied to a doubly truncated data set of German companies, where the age-at-insolvency is of interest. Keywords: Insolvency risk, Linear regression, Non-parametric, Random double-truncation

• Publication year

  2017

• Venue

  South African Statistical Journal

• Publication date

  2017-04-18

• Fields of study

  Mathematics

• Identifiers
  DOI 10.37920/sasj.2017.51.1.1
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

• Saddlepoint p-Values for Linear Rank Tests of Left-Truncated Data with Right Censoring and Cross-Sectional Data

  2025cites this paper
• Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model

  2020cites this paper
• Weighted rank estimation for nonparametric transformation models with doubly truncated data

  2020cites this paper
• Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model

  2020influential citation